0.1 foss4g-2026 2026-09-01 2026-09-03 3 00:05 https://talks.osgeo.org https://talks.osgeo.org/media/foss4g-2026/img/logo-04_f0U7YtL.svg Japan Phoenix Hall Keynote 2026-09-01T11:30:00+09:00 11:30 00:30 OSGeo AGM details are currently TBD and will be announced later. foss4g-2026-5899-osgeo-agm en The details for the OSGeo AGM session are currently being finalized by the organizing committee. Information regarding the meeting agenda, speakers, schedule, and session format will be announced once preparations are complete. We appreciate your patience and understanding, and we look forward to sharing additional details in the near future. Please stay tuned for future updates and announcements. false https://talks.osgeo.org/foss4g-2026/talk/HHNHXD/ Himawari General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 This presentation introduces a spatial system integrating an Environmental Impact Assessment (EIA) scoping tool with online authoring and review functions. Built with OpenLayers, the system connects spatial data, experts, and stakeholders to support collaborative environmental decision-making. foss4g-2026-5287-eia-scoping-tool-and-collaborative-authoring-and-review-system Choi Yeon Ho en Environmental Impact Assessment (EIA) plays an essential role in balancing development with environmental protection. However, many EIA processes still rely on fragmented workflows and document-centered communication, which can limit participation, and efficient collaboration among stakeholders. The EIA scoping tool supports the early stage of the assessment process by allowing experts and planners to define the spatial scope of analysis by drawing the project site on the map, uploading spatial data such as shapefiles, or specifying locations using addresses. Using web-based geospatial technologies such as OpenLayers, the tool allows users to explore spatial datasets, identify key environmental factors, and determine assessment boundaries. In addition, The EIA preparation document authoring and review system enables multiple stakeholders to collaboratively prepare, review, and discuss assessment documents within a unified platform, improving the efficiency and consistency of the evaluation workflow. Through these integrated tools, spatial technologies support more connected and collaborative environmental decision-making. By enabling experts, agencies, and stakeholders to interact around shared geographic information, the system allows them to collaboratively define assessment areas and prepare and review EIA documents. false https://talks.osgeo.org/foss4g-2026/talk/7PFDBP/ Himawari General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 Africa’s freshwater ecosystems are highly threatened yet poorly documented. This talk introduces FBIS Africa, an open-access platform designed to mobilise, harmonise, and analyse freshwater biodiversity data to support conservation, management, and policy decisions across the continent. foss4g-2026-4812-fbis-africa-freshwater-biodiversity-data-from-africa-for-africa /media/foss4g-2026/submissions/7RGJVD/Screenshot_2026-02-02_at_10.34.57_Q4y9hmt.png Dimas Tri Ciputra en Africa’s freshwater ecosystems support exceptional biodiversity and provide essential services to millions of people, yet they face increasing pressure from habitat degradation, overexploitation, invasive species, and climate change. Effective conservation and management are often constrained by fragmented, inaccessible, or incomplete biodiversity data. This session presents FBIS Africa, a continental Freshwater Biodiversity Information System, building on more than seven years of experience and proven impact of the national FBIS platform in South Africa. FBIS Africa is designed as an open-access, user-friendly platform that enables the mobilisation, harmonisation, analysis, and visualisation of freshwater biodiversity data at an unprecedented scale across Africa. false https://talks.osgeo.org/foss4g-2026/talk/7RGJVD/ Himawari General session talk 2026-09-01T16:00:00+09:00 16:00 00:30 The GDAL Sponsorship Program changed how the project operates, has resulted in project-wide functionality and improvements, and put the project on a previously unachievable sustainability path. foss4g-2026-5031-building-the-gdal-sponsorship-program Howard Butler en GDAL is a lynchpin of capability in the geospatial community, but its business model historically looked like the venerable XKCD cartoon. Its development, enhancement, and maintenance was entirely driven by consulting, with most of it coming in the form of a single individual making a business of managing the project. The GDAL Sponsorship Program changed GDAL's business model by relieving pressure on the keystone individuals who hold up our community's software ecosystem by resourcing "maintenance" activities independently from consulting. The sponsorship program resources help grow capable maintainers, pay down decades-old technical debt, and change the economics of project maintenance activities that everyone needs but for which no individual organization can pay. We will describe how it was formed, fundraising lessons learned, how it works, and why it was needed for the project that fills such a critical role in our community's software ecosystem. false https://talks.osgeo.org/foss4g-2026/talk/8YFSA8/ Himawari General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 First ever FOSS4G talk on QuickMapServices, the #1 QGIS plugin. Seriously. foss4g-2026-4896-the-1-plugin-for-qgis Maxim Dubinin en Story of QuickMapServices: its origins, evolution, lessons learned, and the newest powerful features shaping its future. We will explore community growth, technical decisions, unexpected challenges, and key turning points. And finally — how did this QGIS plugin surpass the incredible milestone of 10,000,000 downloads worldwide? The inside story finally revealed. 😱 false https://talks.osgeo.org/foss4g-2026/talk/37T8AK/ Himawari General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 Update on the FOSS libCartoSym, libCSCQL2 and libDE9IM implementing the candidate OGC Cartographic Symbology 2.0 Standard, Common Query Language (CQL2) and Simple Features. http://cartosym.org/ https://github.com/ecere/libCartoSym foss4g-2026-5659-state-of-libcartosym-libcscql2-libde9im /media/foss4g-2026/submissions/3GKMF8/CartoSym1_kWMMD5w.png Jerome St-Louis en This session will provide an update on the latest developments related to libCartoSym, focusing on: - The `cs-canif` tool allowing to transcode styles, expressions and geometries, evaluate styles and expressions, and evaluate spatial relations between geometries, - Completed support for lossless conversion between the CartoSym-CSS and CartoSym-JSON encodings, - Improved support for transcoding styles from Mapbox GL/MapLibre Styles to the CartoSym conceptual model, - Improved support for transcoding styles to OGC Styled Layer Descriptor / Symbology Encoding, - Progress on providing bindings to libCartoSym, libCSCQL2 and libDE9IM to different programming languages (C, C++, Python...), - A progress update on the candidate OGC Cartographic Symbology 2.0 standard. libCartoSym is a Free and Open-Source Software library implementing the OGC Cartographic Symbology 2.0 (https://docs.ogc.org/DRAFTS/18-067r4.html), Common Query Language (https://www.opengis.net/doc/IS/cql2/1.0) and Simple Features (https://portal.ogc.org/files/?artifact_id=25355) standards. libCartoSym implements both the CartoSym-CSS (https://docs.ogc.org/DRAFTS/18-067r4.html#rc-cscss) and CartoSym-JSON (https://docs.ogc.org/DRAFTS/18-067r4.html#rc-json) encodings defined in the candidate standard. libCartoSym and the `cs-canif` tool can be installed with `pip install cartosym` (https://pypi.org/project/cartosym/), while its dependency libCSCQL2 implementing CQL2 support can be installed with `pip install cscql2` (https://pypi.org/project/cscql2/). The library allows to read and write these CartoSym encodings, as well as import from and export to additional encodings of portrayal rules such as OGC SLD/SE (https://portal.ogc.org/files/?artifact_id=22364 / https://portal.ogc.org/files/?artifact_id=16700) and Mapbox GL Styles (https://docs.mapbox.com/mapbox-gl-js/guides/styles/). Functionality to evaluate the symbolizer specified by a style for specific conditions (e.g., feature properties and scale denominator), is also provided for integration within rendering engines. The associated libCSCQL2 implements CQL2, as the CartoSym encodings extend the CQL2 language to define expressions used within rule selectors and symbolizer parameter values. Support for performing spatial relation queries based on the Dimensionally Extended-9 Intersection Model (https://en.wikipedia.org/wiki/DE-9IM) is also integrated within a jointly developed libDE9IM open-source library, and support for OGC Simple Features as well as parsing and writing geometries defined in Well-Known Text (WKT) and GeoJSON is also provided by related open-source libraries. These libraries are written in the eC programming language for native performance, with object-oriented bindings for libCartoSym planned for multiple programming languages including C, C++, Python, Java, Rust and JavaScript. Acknowledgement Financial support provided by GeoConnections, a national collaborative initiative led by Natural Resources Canada. GeoConnections supports the modernization of the Canadian Geospatial Data Infrastructure (CGDI). The CGDI is the collection of geospatial data, standards, policies, applications, and governance that facilitate its access, use, integration, and preservation. false https://talks.osgeo.org/foss4g-2026/talk/3GKMF8/ Himawari General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 Digital Twins are becoming a standard requirement for urban planning and infrastructure, but the software landscape is still dominated by expensive, proprietary systems. foss4g-2026-5063-qgis-as-a-digital-twins-platform Saber Razmjooei en At Lutra Consulting, we wanted to change that. Over the last few years, with support from a community crowdfunding campaign, we have been working to make QGIS a viable, high-performance alternative for building Open Source Digital Twins. This talk will walk through the specific engineering challenges we faced and the solutions we implemented to get QGIS 3D ready for production work. We will move beyond simple visualisation to show how QGIS now handles the heavy lifting required for real-world projects. We will cover: Handling Massive Data: We rewrote parts of the rendering engine to support instanced rendering and dynamic chunking. This means you can now load millions of 3D objects, like city-wide tree datasets or street furniture, and navigate through massive point clouds without the software lagging or crashing. Making 3D Useful, Not Just Pretty: A Digital Twin is useless if you can't query it. We bridged the gap between the 2D and 3D views, adding tools to identify, select, and inspect feature attributes directly in the 3D window. Interoperability: We added native support for 3D Tiles and ESRI Scene Layers (I3S), so users can stream in heavy 3D mesh data from existing sources without needing complex conversion workflows. We will also demonstrate significant enhancements to the cross-section tool, improving its usability for analysing complex 3D datasets. Join us to see how these updates have turned QGIS into a serious tool for 3D data management. false https://talks.osgeo.org/foss4g-2026/talk/3UP9NC/ Dahlia1 General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 Nikkei Visual Data produces digital news content. This presentation introduces how FOSS4G technologies contribute to article production and related developments. It demonstrates the application of geospatial information in journalism through visualization using MapLibre and vector tiles, as well as data analysis and infrastructure development with tools such as QGIS. foss4g-2026-5125-leveraging-open-geospatial-data-in-journalism-visualization-and-analysis-workflows-at-nikkei-visual-data Ryohei SenzakiRyo NamikiSotaro Sakai en Nikkei Visual Data contributes to journalism by producing digital content that uses visual storytelling techniques and technologies to communicate news more clearly and in greater depth. This presentation explains the role that FOSS4G technologies play in the production of public-interest digital articles and introduces selected development cases involving the use of geospatial information at Nikkei Visual Data. In newsroom practice, FOSS4G technologies play an important role in connecting map-based data visualization and spatial analysis to reporting. For example, in coverage of the ongoing Russian invasion of Ukraine since 2022, journalists have collected and analyzed diverse datasets with location information and visualized them on maps, enabling the production of high-value reporting even from remote locations. Geospatial data are also used in reconstruction projects that present past disasters and events in digital space. In 2023, marking the 100th anniversary of the Great Kanto Earthquake, Nikkei Visual Data vividly recreated on maps the spread of earthquake-triggered fires that ultimately burned down approximately 40 percent of Tokyo City. To deliver such geospatial visualizations effectively to readers of news applications, it is essential to load and render data and maps at high speed. It is equally important to provide a consistent user experience across diverse device environments, including laptops and smartphones. Combining rendering libraries such as MapLibre and [deck.gl](http://deck.gl/) with vector and raster tiles, including formats such as PMTiles, represents one effective practice. At the same time, user experience is further enhanced through responsive design based on core web technologies such as HTML and CSS. Spatial data analysis also plays a key role in reporting. In Japan, public institutions publish a wide range of geospatial datasets related to disaster risks, infrastructure, and municipal statistics. By using disaster hazard data as a starting point and conducting temporal analysis or cross-analysis with railway networks and population grid datasets, journalists have been able to uncover new story angles and generate original news insights. In such data journalism practices, FOSS4G technologies form an indispensable foundation. Furthermore, establishing data lakes for large-scale geospatial datasets contributes to the democratization of map data use within the newsroom. Such infrastructure provides a foundation for dashboards and tools that enable journalists and designers—who may not have extensive engineering expertise—to conduct spatial analysis and visualization. At Nikkei Visual Data, a cloud-based data lake has been developed to manage the large volumes of geospatial data required for reporting. This foundation also supports the integration of geospatial information with machine learning techniques and large language models (LLMs). Through the use of FOSS4G technologies for map visualization and spatial data analysis, Nikkei Visual Data advances the creation of distinctive digital content and supports the discovery of new stories. The team is also working to create environments in which professionals across a wide range of roles—not only engineers—can access and utilize geospatial information. These efforts illustrate how FOSS4G serves as an essential enabler of high-public-value journalism. false https://talks.osgeo.org/foss4g-2026/talk/DN8NTM/ Dahlia1 General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 pgRouting project status presentation. Come and find out the latest news on the project as well as future plans, and how to get involved! foss4g-2026-5242-pgrouting-project-status /media/foss4g-2026/submissions/TGQR7D/pgrouting_gjvnMDG.png Vicky Vergara en pgRouting, the PostgreSQL/PostGIS driven extension for graphs and routing. At the end of 2025, we moved to pgRouting 4.0 with new additional functions and a standardized structure on the function signatures. Additionally there are more sub-products from pgRouting that are available for public use. This presentation will provide an update on: - The current status - pgRouting - osm2pgrouting - pgroutingLayers - vrpRouting - Our classification: official, proposed, experimental - Changes and migration from 3.8 to 4.0 versions - GSoC-OSGeo program: for students to get involved false https://talks.osgeo.org/foss4g-2026/talk/TGQR7D/ Dahlia1 General session talk 2026-09-01T16:00:00+09:00 16:00 00:30 MapboxVectorTiles have changed the geospatial ecosystem. Vector data distribution has become more efficient, map rendering has moved from server-side to client-side, map styling has become more common. But does that mean we no longer need raster tiles? No. Raster tiles still matter. foss4g-2026-5065-we-still-need-raster-tiles-then-chiitiler Kanahiro Iguchi en ## Abstract MapboxVectorTiles have changed the geospatial ecosystem. Vector data distribution has become more efficient, map rendering has moved from server-side to client-side, map styling has become more common. But does that mean we no longer need raster tiles? No. Raster tiles still matter. ## Outline 1. Introduction 2. What vector-tiles changed 3. Why raster-tiles still matter 4. Raster Tile server 5. chiitiler - A serverless oriented raster tile server ## References - https://github.com/Kanahiro/chiitiler - https://github.com/maptiler/tileserver-gl - https://github.com/developmentseed/titiler false https://talks.osgeo.org/foss4g-2026/talk/897ACP/ Dahlia1 General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 Update on the FOSS Discrete Global Grid Abstraction Library (DGGAL), focusing on support for new Discrete Global Grid Reference Systems (DGGRSs), use of the library in the OGC AI-DGGS Pilot for Disaster Management, and new high-level DGGAL "High Vibes" tools https://dggal.org https://github.com/ecere/dggal foss4g-2026-5652-state-of-dggal-discrete-global-grid-abstraction-library /media/foss4g-2026/submissions/8JTAXF/L0d3_B9Qk0gN.png Jerome St-Louis en This session will provide an update on latest developments related to the Discrete Global Grid Abstraction Library (DGGAL), focusing on: - Support for new Discrete Global Grid Reference Systems (DGGRSs), - Use of DGGAL as a foundational library for the OGC AI-DGGS Pilot for Disaster Management (https://aidggs-pilot.hartis.org/) by organizations participating in that project, - DGGAL "High Vibes", a set of high-level vibe-coded Python tools to import/export data into DGGS-quantized data stores and serve it through OGC API - DGGS DGGAL presents a common interface to perform operations on DGGRSs, facilitating the implementation of Discrete Global Grid Systems (DGGS), including implementing Web APIs based on the OGC API - DGGS Standard (https://docs.ogc.org/is/21-038r1/21-038r1.html). DGGAL for Python can be installed with `pip install dggal` (https://pypi.org/project/dggal/), and High Vibes tools with `pip install dgg-vibes`. DGGAL supports all nine DGGRS from OGC API - DGGS Annex B (https://docs.ogc.org/is/21-038r1/21-038r1.html#annex-dggrs-def), plus additional DGGRSs. The library is written in the eC programming language (https://ec-lang.org) for optimal native performance, with object-oriented bindings for C, C++ Python, Rust, Java and JavaScript available (example bindings usage: https://github.com/ecere/dggal/tree/main/bindings_examples). The `dgg` command-line tool allows performing various operations including generating grids at different refinement levels, querying a particular zone identifier, identifying the zone at geospatial coordinates, listing zones within a bounding box, resolving sub-zone indices and converting DGGS-JSON to GeoJSON. The DGGAL "High Vibes" tools for working with UBJSON DGGS Data Stores include: - `dgg-import`: Import from a raster (e.g., GeoTIFF) or vector (GeoJSON), quantizing to a specific DGGRS (e.g., `dgg-import gebco.tiff --dggrs IVEA4R --fields Elevation`) - `dgg-fetch`: Fetch (acting as a client) from an OGC API - DGGS deployment (e.g., `dgg-fetch https://example.com/collections/gebco/dggs/IVEA4R`) - `dgg-export`: Export a GeoTIFF or GeoJSON (e.g., `dgg-export data out.tif --collection gebco --level 10`) - `dgg-serve`: Deploy a server implementing OGC API - DGGS to provide access to DGGS-quantized collections (e.g., `python dgg-serve.py --data-root data --port 8080`) - `dgg-fg`: Utility implementing support for DGGS-JSON-FG (an extension of OGC Features and Geometry JSON, itself an extension of GeoJSON, quantizing points of vector geometry to a DGGRS without rasterizing) including tiling to DGGS-JSON-FG and converting one file or a set of tiles to GeoJSON (generalization of vector tiles to arbitrary shapes, such as "HexVecTiles") The implementation of a Vision Transformer (ViT) / Masked Auto Encoder (MAE) model trained on a DGGAL High Vibes DGGS-UBJSON Data Store will also be presented. Acknowledgement Financial support provided by GeoConnections, a national collaborative initiative led by Natural Resources Canada. GeoConnections supports the modernization of the Canadian Geospatial Data Infrastructure (CGDI). The CGDI is the collection of geospatial data, standards, policies, applications, and governance that facilitate its access, use, integration, and preservation. false https://talks.osgeo.org/foss4g-2026/talk/8JTAXF/ Dahlia1 General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 GRASS, Geographic Resources Analysis Support System, is a powerful engine for geospatial processing and analysis. This talk delivers the latest GRASS update, covering technical progress, new integration pathways, community developments, and key outcomes from the 2026 community meeting. foss4g-2026-5197-state-of-grass /media/foss4g-2026/submissions/KUY9QP/grass-logo-green-white-bg3x_xdwAuVb.png Alen Mangafić en What is new in GRASS, and where is the project heading? This talk gives a current overview of GRASS as both a powerful geospatial processing engine and an active open-source project, highlighting recent progress in development, integration, and community. On the technical side, the talk will cover major recent advances such as the new Python API, improved NumPy integration, broader JSON outputs for smoother data science workflows, modernized documentation, parallel raster algebra, and a growing ecosystem of addons. It will also look at packaging and interoperability, including conda-forge and the broader push to make GRASS easier to integrate into modern scientific and geospatial workflows. On the project side, the talk will report on the GRASS Community Meeting 2026, held from 11 to 19 July 2026 in San Michele, Italy. These meetings bring contributors together to advance ongoing work, coordinate priorities, and help shape the next phase of GRASS development. Overall, this session offers a compact and accessible update for both longtime users and newcomers. It will show where GRASS is improving, what is becoming easier or more powerful, and how the project continues to grow as a platform for serious open-source geospatial work. false https://talks.osgeo.org/foss4g-2026/talk/KUY9QP/ Dahlia1 General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 This presentation will introduce the attendees to GeoNode's new capabilities. We will provide a summary of the new features added to GeoNode in the last release together with a glimpse of what we have planned for next year and beyond, straight from the core developers. foss4g-2026-5465-state-of-geonode Stefano BovioMattia Giupponi en GeoNode is a Web Spatial Content Management System based entirely on Open Source tools whose purpose is to promote the sharing of data and their management in a simple environment where even non-expert users of GIS technologies can view, edit, manage, and share spatial data, maps, prints and documents attached. This presentation provides a summary of new features added to GeoNode in the year up to the latest releases of GeoNode together with a glimpse of what we have planned for next year and beyond, straight from the core developers. The purpose of this presentation is to introduce the attendees to those which are the GeoNode current capabilities and to some practical use cases of particular interest in order to also highlight the possibility of customization and integration. Finally, we will provide a summary of new features added to GeoNode in the last release up to the latest releases of GeoNode together with a glimpse of what we have planned for next year and beyond, straight from the core developers. false https://talks.osgeo.org/foss4g-2026/talk/XF9KXZ/ Dahlia2 General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 Will plugins really break when migrating them to QGIS 4? foss4g-2026-5645-qgis-4-is-coming-and-my-plugins-will-break Numa Gremling en The title of this talk may sound intimidating, but it reflects a very common concern. Will the plugins I’ve previously developed break in QGIS 4? Do I need to rewrite every line of code, or will plugins continue to work with only minor adjustments? This talk is aimed at those familiar with plugin development in QGIS 3 who want to get started with QGIS 4. You’ll learn what you really need to know when migrating. And then you will decide for yourself: is the process really as scary as the title of this talk? ;-) false https://talks.osgeo.org/foss4g-2026/talk/FXACB8/ Dahlia2 General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 QGIS Web Client (QWC) is a modular next generation responsive web client for QGIS Server, built with ReactJS and OpenLayers. This presentation gives a short introduction to the QWC project and presents new developments. foss4g-2026-5167-qgis-web-client-qwc-project-status-and-new-developments Marco Hugentobler en The QGIS Web Client (QWC) provides publication of QGIS projects on the web with the same look and feel as QGIS Desktop, thanks to QGIS Server. The environment consists of a modern web application written in JavaScript using ReactJS and OpenLayers, and the qwc-services, an ecosystem of server-side Python/Flask microservices that can be used, for example, to manage user rights and edit geospatial data within the web application. This presentation gives a short introduction to the QWC project and presents new developments, e.g. the 3D view, which was developed using THREE.JS and Giro3D. false https://talks.osgeo.org/foss4g-2026/talk/VGU3PW/ Dahlia2 General session talk 2026-09-01T16:00:00+09:00 16:00 00:30 DigiAgriApp is a free open-source suite to monitor agricultural fields. It is a comprehensive client-server platform designed to manage agricultural data with high granularity, from fields down to individual plants. It is built on standards to ensure flexibility and integration. Let’s see what’s new in 2026. foss4g-2026-5131-digiagriapp-2026-updates Luca Delucchi en DigiAgriApp is a free and open-source suite to monitor agricultural fields. The development was started and carried out by the Digital Agriculture Unit of Fondazione Edmund Mach. The suite is based on an Open Source architecture, built on a Django (Python) backend with a PostgreSQL/PostGIS database and a Flutter-based multi-platform client (mobile/desktop). The suite is completed by a plugin for QGIS for easier management of geographical data. This year, development has slowed down somewhat. Few major changes have been made to the code, especially on the server side, although bug fixes have continued and some new features have been added to the cross-platform client. 2026 is the year of confirmation and stabilisation, with more energy devoted to dissemination. The first course dedicated to DigiAgriApp was held internally at the Mach Foundation, which made it possible to improve documentation and expand the user base. In addition, several videos were created to showcase the app's features. false https://talks.osgeo.org/foss4g-2026/talk/XU9UKV/ Dahlia2 General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 QGIS is translated into many languages, but structured tutorials remain scarce. In 2024, we launched a Japanese QGIS platform now reaching 20,000 monthly users. Our data shows onboarding content — installation guides, first steps — draws the widest audience. We share our approach and how to replicate it in your language. foss4g-2026-5610-qgis-in-your-language /media/foss4g-2026/submissions/K33LPE/qgis-lab-ogp_WbkouBk.png Keita UEMORI en QGIS's user interface is translated into dozens of languages by community volunteers — a remarkable achievement that means anyone can open QGIS and see menus in their own language. Yet having a translated UI is not the same as having a step-by-step tutorial in your language, written with screenshots of that translated interface and built around the datasets and coordinate systems you actually use. In Japan, a wealth of QGIS-related information exists online, but it is scattered across personal blogs, forums, and social media. We felt that a dedicated, organized platform was needed. In October 2024, we launched "QGIS LAB," a Japanese-language media platform focused entirely on QGIS. It provides how-to tutorials, real-world use cases, blog posts covering new features and FOSS4G event reports, and curated "learning packages" — structured sets of articles that guide users through topics such as installation, vector analysis, and QField. All tutorials use Japanese coordinate reference systems and data from major Japanese open data portals, so readers can follow along immediately. The platform is ad-free — we want users to focus on QGIS, not navigate around promotions. Since launch, we have published over 100 articles and the platform reaches approximately 20,000 users per month. So what is our most-viewed article? Not an advanced analysis technique — it is how to download and install QGIS. Articles on adding basemaps and importing CSV data also rank consistently high. This tells us something worth sharing: there is significant demand for onboarding content — guides that help beginners through their very first steps, with screenshots of the same interface they see on their own screen. We also put emphasis on local context. Tutorials built around Japanese Plane Rectangular Coordinate Systems and domestic open datasets help users connect QGIS to their own work, which we hope encourages them to explore further on their own. We run this platform as a company — MIERUNE, a QGIS sponsor and voting member. This means we can commit sustained resources to content production. Our experience delivering QGIS training courses also helps us understand exactly where beginners stumble, which directly shapes what we write. If QGIS's UI is already translated into your language, consider writing beginner guides in that language. Our data suggests that onboarding content — helping newcomers past their first steps with confidence — reaches the widest audience. Writing code is not the only way to contribute to open source — a well-crafted tutorial in your language can help someone take their first step into QGIS, and that is how a user community grows. false https://talks.osgeo.org/foss4g-2026/talk/K33LPE/ Dahlia2 General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 How can companies engage with QGIS continuously while balancing community relationships and user support? Based on MIERUNE’s experience in Japan, this presentation shares practical insights into how companies can support and extend QGIS through user support, knowledge sharing, plugin development, and product development that expands its practical use. foss4g-2026-5226-a-japanese-company-s-journey-in-building-the-qgis-community Toshiya Kunou en QGIS is an open-source desktop GIS used around the world, and its use is also expanding in Japan across government agencies, companies, and educational institutions. For companies that want to engage with QGIS continuously and provide value, it is important to think not only about using it, but also about their relationship with the community, how they support users, and what role they should play. MIERUNE has been continuously involved with the QGIS project as a sponsor and commercial supporter. We established a dedicated QGIS team and have supported the practical use of QGIS in professional settings through training, support services, and plugin development. We also share knowledge and practical information through “QGIS LAB by MIERUNE.” In recent years, we have also been working on product development that expands the range of QGIS use cases. In this presentation, we share practical lessons drawn from real-world work in Japan on how companies can engage with QGIS on an ongoing basis. Through activities related to user support, knowledge sharing, community engagement, and the expansion of QGIS use in practice, we reflect on how companies can contribute to the QGIS ecosystem in sustainable ways. false https://talks.osgeo.org/foss4g-2026/talk/UWSLUK/ Dahlia2 General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 This session follows on from the QGIS "Feature Frenzy" talk and is your once-in-a-lifetime opportunity to ask Marco Bernasocchi (QGIS.org Chairperson) and Nyall Dawson (QGIS Core Contributor) anything about QGIS. foss4g-2026-5056-qgis-ask-me-anything-session Marco Bernasocchi en Start thinking about those burning questions that YOU want answered about QGIS and QGIS.org. Quiz us about features, how the project is run, challenges and what the future holds. We want the hard questions, the ones that keep you up at night. Don't hold back, nothing is off limits! false https://talks.osgeo.org/foss4g-2026/talk/P9XGVU/ Ran1 Lightning talk 2026-09-01T13:00:00+09:00 13:00 00:05 PoliMappers reflect on a decade of student-led open mapping at Politecnico di Milano. We share lessons on community sustainability, FOSS4G academic integration, and volunteer data quality. Discover how this YouthMappers chapter bridges the gap between university education and impactful, collaborative open-source geospatial contributions from a student perspective. foss4g-2026-5239-ten-years-of-polimappers-lessons-from-a-student-led-open-mapping-community Federica Gaspari en Founded in 2016, PoliMappers is a student volunteer group at Politecnico di Milano, Italy. Its mission is to promote open-geoscience and FOSS through mapping activities, as the first European chapter of YouthMappers, combining education, community-building and real-world mapping contributions. As the group celebrates 10 years as an evolving entity within the open global ecosystem, this presentation explores the challenges involved in empowering a long-term, sustainable student community and the decade-long journey in academia and civic society, featuring voices and contributions from current members, alumni, and collaborators. The challenge of fostering a long-term sustainable student community As a volunteer group rooted in an academic setting, PoliMappers faced the challenge of member turnover and continuity inherent in student organisations, where people come and go each year. Keeping momentum while training new members in both geospatial tools and collaborative practices requires structured and continuous outreach, supported by proper training and communication strategies. The integration of open-source geospatial tools into university curricula FOSS and collaborative mapping are often under-recognised within formal engineering and architecture programs. Making the case for their value, both technically and socially, remains an ongoing effort, especially when compared to proprietary workflows still prevalent in industry and education. This talk will highlight our initiatives that contributed to establishing a solid presence within geospatial-related courses in our university, through both traditional as well as innovative-teaching curriculum. The quality challenge of volunteer contributions Contributions to platforms like OpenStreetMap rely on volunteering, especially humanitarian and specialised mapping, requiring training and coordination, which can be hard to scale when resources are largely student-driven. Drawing on our experience, we will share insights from cross-faculty mapping activities, from campus to city scale. Developing a diverse network for a valuable community impact By partnering with local and international communities, PoliMappers has demonstrated how student initiatives can have a real impact beyond campus, connecting to global mapping efforts and advocacy for open geodata, aligning with principles of FOSS: technology as a shared, social resource, not a proprietary product. The contribution will also include quantitative insights from: Broad engagement and collaboration PoliMappers has organised numerous events ranging from local field mapping, remote mapathons, educational workshops, and international campaigns. These activities not only strengthened skills but also helped grow an active and diverse community of contributors, involving partners from humanitarian organisations, academic units, local projects, and schools. This show that student-led FOSS initiatives can reach multiple domains from global development to urban planning and small-scale decisions. Legacy and future growth Ten years on, the group stands as an example of how FOSS4G and participatory mapping can be embedded into both education and community practice, inspiring similar initiatives around Europe and reinforcing the idea that open mapping is a social activity. Entering the next decade, the key challenges remain connecting open practices with broader institutional support and translating volunteer energy into lasting impact, by finding new synergies and inspirations with similar groups from all over the world. false https://talks.osgeo.org/foss4g-2026/talk/3EPWQZ/ Ran1 Lightning talk 2026-09-01T13:05:00+09:00 13:05 00:05 The geonode-k8s chart is production-grade, community-driven solution for deploying GeoNode 4.x and 5. This talk presents a status update on the project’s progress, highlighting e.g. the upgrade to GeoNode 5 and the improvements on start up time of GeoNode. foss4g-2026-5346-project-update-geonode-k8s Marcel Wallschläger en geonode-k8s is a maintained, production-ready Helm chart for deploying GeoNode on Kubernetes, serving as the de facto standard for scalable, reliable, and reproducible GeoNode deployments in research, government environments. Designed from the ground up for modern cloud-native infrastructure, geonode-k8s provides a complete, modular, and automated solution for running GeoNode 4.x and GeoNode 5.0, ensuring compatibility with the latest features and security updates. Built on Helm 3, the chart abstracts the complexity of orchestrating GeoNode’s multi-component architecture—comprising the GeoNode application, PostgreSQL, Redis, Celery, NGINX, and GeoServer —into a single, version-controlled deployment. What sets geonode-k8s apart is its active development and growing community adoption. With regular updates, bug fixes, and feature enhancements, the project responds quickly to user feedback and evolving requirements. Interest in geonode-k8s continues to rise, with increasing usage across research institutions, national data infrastructures, and open science initiatives. Its role in enabling scalable, FAIR-compliant research data management platforms—especially in domains like agriculture, environmental monitoring, and urban planning—has solidified its importance in the GeoNode ecosystem. GitHub: https://github.com/GeoNodeUserGroup-DE/geonode-k8s Helm Repository: https://geonode-usergroup-de.github.io/geonode-k8s-helm/ false https://talks.osgeo.org/foss4g-2026/talk/8ADQ39/ Ran1 Lightning talk 2026-09-01T13:10:00+09:00 13:10 00:05 Computing a representative point that is guaranteed to lie inside a geometry is essential for labeling, geocoding, and spatial indexing. This talk introduces "interior-point", an open-source project that ports the JTS (Java Topology Suite) InteriorPoint algorithm to both TypeScript and Rust/WASM. foss4g-2026-5614-porting-jts-interior-point-algorithm-to-typescript-and-rust-wasm Ko Nagase en **Background** The "interior point" (or "representative point") problem - finding a point guaranteed to lie inside a given geometry - is a fundamental operation in GIS. It is used for polygon labeling, address geocoding, and spatial indexing. In Japan, the Digital Agency's mojxml2geojson tool uses GEOS's PointOnSurface function (via Python GDAL bindings) to compute representative points for cadastral parcel polygons. These representative points are published as part of the national Address Base Registry master data. **Motivation** While JTS and GEOS provide a robust solution, there is growing demand for performing such geospatial computations directly in the browser - for interactive web mapping, etc. The presenter previously gave a talk at FOSS4G Asia 2023 on GEOS-WASM ("[GEOS runs on web browsers - WebAssembly power for geospatial analysis](https://www.docswell.com/s/sanak/Z1JEL3-2023-11-30-100118)"), exploring the feasibility of running GEOS in the browser via WebAssembly. Building on that experience, this project takes a different approach: rather than compiling a large C++ library to WASM, port the specific algorithm we need - JTS's scanline-based InteriorPoint - directly to TypeScript and Rust. **The Project Status & Future Work** The project was just started and still needs enough tests with various data. * WebSite: https://sanak.github.io/interior-point/ * GitHub: https://github.com/sanak/interior-point I am planning a browser-based benchmark suite comparing accuracy and performance of multiple interior point / point-on-surface implementations: - JSTS (JavaScript port of JTS) - wasmts (JTS compiled to WASM) - geos-wasm (GEOS compiled to WASM, GEOSPointOnSurface) - georust/geo (interior_point - a different implementation in Rust) - Turf.js (pointOnFeature) This comparison will help the community understand the trade-offs between pure-JS, WASM-compiled, and algorithm-specific approaches for browser-based geospatial computing. false https://talks.osgeo.org/foss4g-2026/talk/8LCDE8/ Ran1 Lightning talk 2026-09-01T13:15:00+09:00 13:15 00:05 Did you know QGIS has a community hub for sharing styles, models, 3D models, projects, processing scripts, and more? This talk introduces [hub.qgis.org](https://hub.qgis.org) and the QGIS Hub Plugin, which lets you discover and use these resources directly from QGIS, without ever leaving the application. foss4g-2026-5093-qgis-hub-the-community-resource-ecosystem-you-might-not-know-about Ismail Sunni en Many QGIS users don't know that the project maintains a dedicated community resource platform: the [QGIS Hub](https://hub.qgis.org). It is a free, open web portal where anyone can share and discover reusable GIS resources — styles, processing models, 3D models, projects, layer definitions, maps, and processing scripts. The Hub is a two-way ecosystem: you can use resources shared by the community, and you can contribute your own work back for others to benefit from. Whether you've crafted a beautiful style, built a handy processing model, or assembled a reusable project template, the Hub is the place to share it with the global QGIS community. To make accessing the Hub even easier, the [QGIS Hub Plugin](https://github.com/qgis/QGIS-Hub-Plugin) brings the entire resource browser directly into QGIS. Instead of visiting the website, downloading files manually, and importing them, users can search, preview, and load any Hub resource into their project with a single click — all without leaving the application. In this lightning talk, we'll walk through the Hub website and show how the plugin works in practice through a short recording. The goal: make sure everyone leaves knowing this ecosystem exists and feels empowered to both use it and contribute to it. false https://talks.osgeo.org/foss4g-2026/talk/9CXKL7/ Ran1 Lightning talk 2026-09-01T13:20:00+09:00 13:20 00:05 Culverted waterways and minor drainage features are physically present but persistently absent in OpenStreetMap — limiting flood and dengue risk analyses alike. This lightning talk documents a YouthMappers-led effort to map hidden urban waterways infrastructure and shows how improved completeness changes spatial risk outputs. foss4g-2026-4960-mapping-what-s-hidden-youthmappers-and-urban-drainage-completeness-for-flood-and-dengue-risk-in-osm Feye Andal en Urban drainage infrastructure such as culverted waterways, minor canals, and stagnant water catchments is among the hardest features to map in OpenStreetMap, yet its absence quietly undermines spatial analyses across multiple risk domains. This lightning talk documents a YouthMappers-led effort to systematically improve drainage network completeness in OSM, motivated by two converging applications: flood exposure modeling and dengue risk analysis. We present practical methods for identifying, validating, and mapping physically hidden or under-represented drainage features using open geospatial workflows, and show how improved coverage meaningfully changes analytical outputs for both hydrological and public health applications. The talk closes with transferable lessons for community mapping programs targeting infrastructure that is present on the ground but invisible in open data. false https://talks.osgeo.org/foss4g-2026/talk/A7ZPQH/ Ran1 Lightning talk 2026-09-01T13:30:00+09:00 13:30 00:05 This presentation introduces a workflow for converting numerical weather prediction data from the Korea Meteorological Administration into real-time 3D visualizations. The process includes algorithmic transformation based on NWP models, optimization for lightweight performance, and display rendering, utilizing the open-source CesiumJS platform for 3D visualization. foss4g-2026-5083-integration-of-numerical-weather-prediction-models-a-3d-spatial-information-approach /media/foss4g-2026/submissions/AUE7KY/streamline_03pdy66_t6RzEIH.gif YimYooYeol en 1- Overview of the 3D Visualization System This system was developed to visualize numerical weather prediction (NWP) data from the Korea Meteorological Administration in a web-based 3D environment. The primary goal is to support meteorologists in their analysis of weather phenomena by rendering NWP data alongside topographical information and various weather elements. For 3D visualization, the system employs CesiumJS, a specialized open-source engine for geospatial data rendering. CesiumJS enables high-performance visualization of terrain, spatial models, and time-based data directly in a browser, providing an optimal foundation for the system. 2 - Processing Workflow of NWP Model Data The system includes an automated pipeline that handles the collection, transformation, and management of large-scale NWP data. This allows for fast and efficient visualization of real-time or periodically updated datasets. Manual reprocessing is also supported, ensuring adaptability and control when necessary. The processed data is categorized into two main types: - Isosurface -> GLTF - Streamline -> JSON 1. Isosurface Generation To extract isosurfaces from volumetric scalar fields, the marching cubes algorithm was applied. This method generates triangle mesh representations of 3D structures corresponding to specific threshold values in the data. The generated structures are then converted into GLTF format. Geospatial coordinates are embedded into these GLTF models to ensure accurate placement in the 3D environment. The models undergo a lightweight optimization process to reduce file size while preserving visual fidelity. This makes them suitable for smooth rendering in web browsers without compromising detail. 2. Streamline Visualization Streamlines are used to represent directional flow patterns such as wind. For this, a computed engine calculates flow data in real time on a per-frame basis, and the results are merged into a single texture. This process is executed through parallel computation, ensuring optimized performance even when handling large datasets. This approach allows for dynamic visualization responsive to user interaction and effectively conveys directional elements such as wind speed and direction. 3 - Significance and Presentation Goal The described techniques offer a visual aid in interpreting complex numerical weather data, helping meteorologists and researchers understand and utilize this information more intuitively. The system is under continuous development, with additional NWP models being processed and integrated. This presentation aims to showcase the data processing pipeline and visualization results of the system, introducing new possibilities in weather data interpretation and the technical approaches used to achieve it. false https://talks.osgeo.org/foss4g-2026/talk/AUE7KY/ Ran1 Lightning talk 2026-09-01T13:35:00+09:00 13:35 00:05 We turn custom Flutter widgets into small images and use them as MapLibre pins. This avoids managing many static image assets and keeps pin designs consistent with the app UI. We also briefly cover image caching and how to handle re-registering images when the map style changes. foss4g-2026-5582-rendering-custom-flutter-widgets-as-maplibre-map-pins-without-static-assets Ryutaro Iseki en In this lightning talk, we show a simple technique to render custom Flutter widgets as map pins in MapLibre. Instead of preparing many static image assets, we convert Flutter widgets into images at runtime. We render a widget off-screen, capture it as a bitmap, and register it as an image in MapLibre. Each post is then displayed as a symbol using the registered image, allowing the map pins to match the app UI design. To keep performance stable, we generate each image only once and reuse it through caching. We also handle style changes by re-registering images when necessary. This approach simplifies asset management while enabling flexible and consistent pin design. A short demo will show how the widget and map pin visually match. false https://talks.osgeo.org/foss4g-2026/talk/CNPWFN/ Ran1 Lightning talk 2026-09-01T13:40:00+09:00 13:40 00:05 Tracing the 16-year evolution of the QGIS Brazil community—one of the world's first official user groups. From initial 2005 translations to a network of 40,000+ members, we explore strategies for virtual integration, institutional maturity, and the impact of hosting the 2nd QGIS LATAM Meeting in 2024 foss4g-2026-5072-scaling-geospatial-communities-16-years-of-qgis-brazil-and-the-path-to-latam-2024 /media/foss4g-2026/submissions/CQHMXX/IMG_9688-scaled_AulArNq.jpg Narcélio de Sá en The Brazilian QGIS community represents a decade and a half of decentralized growth and technological sovereignty. This presentation details our journey from a single volunteer’s initiative to becoming a cornerstone of the global FOSS4G ecosystem. The Seed (2005–2010): Pioneering Early Adoption The journey began in 2005 with the first translation of QGIS 0.6 into Portuguese, which at the time consisted of only 600 strings. This initiative led to the official foundation of the community and the launch of the portal www.qgisbrasil.org on March 22, 2010. We discuss how this early focus on localization was essential to making GIS accessible to Lusophone users. Scaling through Virtual Integration To bridge Brazil’s continental distances, we prioritized digital platforms long before remote collaboration was a global standard. Today, our support network includes over 35,000 Facebook group participants, 5,000 Google Group members, and more than 300,000 accesses to the community website. This massive virtual infrastructure ensures that technical knowledge reaches every corner of the country. Institutional Maturity & Continental Leadership We detail the transition from a volunteer-led group to a formalized entity. In 2017, QGIS Brazil joined other international user groups as an official project sponsor. Following the success of local events like the 1st National Meeting in 2014, we reached a major milestone in 2024 by organizing the 2nd QGIS LATAM Meeting in Belém do Pará. This session provides a roadmap for other regional chapters: how to transform early localization efforts into a robust institutional framework that drives software development, professional training, and regional cooperation across Latin America. false https://talks.osgeo.org/foss4g-2026/talk/CQHMXX/ Ran1 Lightning talk 2026-09-01T13:45:00+09:00 13:45 00:05 With the focus on 10-year cycle data collections by the Spanish National Forest Inventory (NFI), remote sensing-based biophysical indicators such as leaf area index are integrated in a Discrete Global Grid System (DGGS) as a complementary tool for forest monitoring in Catalonia. foss4g-2026-5453-what-is-the-optimal-resolution-of-a-dggs-for-forest-cover-mapping-and-monitoring /media/foss4g-2026/submissions/D3JVMA/OEM_Logo_Color_1000x1000_transparent_DARK_OEM_logo_Black__Y0RNIf9.png Kaori Otsu en The main objective of this study is to explore the optimal DGGS resolution to represent forest cover polygons delineated by stand type. In addition, the selected DGGS resolution is assessed if it is acceptable to monitor additional forest stand functions such as leaf area index (LAI). Geospatial analysis and visualisation are demonstrated in the following workflow. - With Vgrid DGGS tools in QGIS, sensitivity analysis determines the optimal resolution level by changing spatial resolutions and analysing discrepancies in represented forest cover polygons. - Sentinel-2 based LAIs are collected and categorised in a DGGS to be assessed at the resolution level selected for forest cover mapping. The calculated LAIs are sourced from Open Earth Monitor Biodiversity and openEO BIOPAR. false https://talks.osgeo.org/foss4g-2026/talk/D3JVMA/ Ran1 Lightning talk 2026-09-01T13:50:00+09:00 13:50 00:05 Very High Resolution satellite imagery (eg WorldView3) provides insights into Earth surface processes, but suffers from limited spatial/spectral accuracy. While tools exist to correct these errors, there is currently no robust pipeline. Vhrharmonize is a Python library, command-line interface, and QGIS plugin that automates preprocessing and mosaic generation. foss4g-2026-5535-creating-large-scale-very-high-resolution-satellite-mosaics-with-high-spatial-and-spectral-accuracy Iosefa PercivalKanoa en Efficient processing of Very High Resolution (VHR) satellite imagery (such as that from WorldView-3 and Planet Labs constellations) enables large-scale analysis of detailed Earth surface processes, yet remains challenging due to limited tools for handling large data volumes and inherent inconsistencies in these datasets. Satellite imagery is affected by spectral errors due to atmospheric and surface effects and spatial errors due to geometric distortion. In VHR imagery, these issues inhibit analyses that depend on high spatial and spectral fidelity, particularly in multi-image and multi-sensor workflows such as mosaic generation and data fusion. There is currently no end-to-end open-source pipeline to preprocess WorldView-3 imagery and correct these errors. To streamline the creation of region-scale mosaics, we developed vhrharmonize as an open-source Python library, command-line interface, and QGIS plugin. The toolkit integrates established open-source tools with newly developed methods into a unified, modular pipeline that transforms raw imagery into analysis-ready products through radiometric calibration, atmospheric correction, orthorectification, pansharpening, cloud and shadow masking, co-registration, seamline generation, and relative radiometric normalization. By consolidating these steps into a single pipeline, the system enables efficient, reproducible, and idempotent processing, supporting rapid iteration at both the individual step level and across full mosaic generation workflows. This tool is designed for applications that rely on high-resolution satellite imagery but require strong cross-image consistency, including ecosystem and biodiversity monitoring, land cover and land use classification, change detection and time-series analysis, machine learning and computer vision workflows, disaster response and environmental assessment, and precision agriculture and resource management. By reducing both spectral and spatial inconsistencies, vhrharmonize facilitates more reliable downstream analysis across heterogeneous VHR datasets. false https://talks.osgeo.org/foss4g-2026/talk/DDYZRH/ Ran1 Lightning talk 2026-09-01T14:00:00+09:00 14:00 00:05 In this lightning talk, I’ll show how 3D data helps urban planning. I’ll outline key datasets and how open-source tools like GeoServer publish them as map tiles and GeoJSON. I’ll also introduce three uses: Cross-sections Analysis, Visibility Analysis, and 2D–3D Linking. foss4g-2026-5312-making-urban-data-explorable-3d-geoserver-and-oss /media/foss4g-2026/submissions/DHZ3R3/stacks_B7l3yvf.png Fukushima Shotaro en In this talk, I present a practical, OSS-centered workflow for bringing 3D geospatial data into urban planning and smart city initiatives. The core message is that 3D mapping and modeling turn abstract datasets into something you can explore, making planning discussions more concrete and easier to align on. In practice, building an explorable view means dealing with many different data formats—so I’ll briefly introduce the datasets as well. To connect those formats to web applications, I use GeoServer as the publishing layer. Rather than pushing raw GIS files to the browser, GeoServer can publish web-friendly outputs such as map tiles and GeoJSON.With GeoServer in place, I can also demonstrate attribute delivery and visualization—for example, showing station names and building names stored in GeoServer directly on the map. This enables searching, filtering, and interpreting features while staying grounded in geographic context. To close, I highlight three application patterns that are especially useful for planning: Cross-sections Analysis: Quickly understand elevation, slope, and how terrain and structures relate to each other. Visibility Analysis: Check what can be seen from a given point, supporting discussions around landmarks, safety, and view protection. Linking 2D and 3D: Link a 2D map with a 3D scene so users can move smoothly between overview and detail. On the day, I’ll play a short (~20-second) video to show how it works. Overall, combining 3D data with an OSS-based publishing approach makes urban data easier to access, easier to share, and more usable in real planning conversations. false https://talks.osgeo.org/foss4g-2026/talk/DHZ3R3/ Ran1 Lightning talk 2026-09-01T14:05:00+09:00 14:05 00:05 Developing QGIS plugins and using VS Code as your IDE? Get comfortable with DevTools for QGIS. foss4g-2026-4897-qgis-vs-code-devtools Maxim Dubinin en Developing QGIS plugins is a powerful way to extend the functionality of one of the world’s leading open-source GIS platforms. But despite the flexibility of Python and QGIS’s API, one thing has long been missing from the developer experience: an efficient, modern debugging workflow. QGIS DevTools is a toolkit for QGIS plugin developers. It allows you to launch a debugpy server directly from QGIS and connect to the process from VS Code. false https://talks.osgeo.org/foss4g-2026/talk/F9BXCZ/ Ran1 Lightning talk 2026-09-01T14:10:00+09:00 14:10 00:05 This lightning talk traces the inspiring journey of OSGeo Brazil, from informal online forums to hosting the monumental FOSS4G in the Amazon. Join us to explore how a passionate community united to democratize open-source geotechnology, build a local chapter, and map a collaborative future. foss4g-2026-5501-the-map-of-our-history-the-journey-of-osgeo-brazil /media/foss4g-2026/submissions/FRQRLV/54198141286_1f337ecc5d_b_CrVpZa0.jpg Narcélio de Sá en Every map starts with a single point, but for OSGeo Brazil, our map began in the digital trenches of late-night forums, mailing lists, and shared tutorials. This lightning talk takes you on a five-minute journey through the evolution of the Brazilian open-source geospatial community, showing how a shared passion for code transformed into an official institution. We will explore our roots during the "Years of Informality," a vibrant but fragmented era where dedicated groups for tools like QGIS, MapServer, and PostGIS operated as a distributed family. We proved daily that open-source software was as powerful as any proprietary alternative, driven by a genuine desire to share knowledge. As our community grew, so did the need for a unified voice. The creation of the official OSGeo Brazil Local Chapter marked a crucial transition from grassroots camaraderie to institutional maturity. This was not merely administrative bureaucracy; it was a collective coming-of-age that allowed us to dialogue with governments, universities, and the private sector on a national scale. By uniting our efforts under one umbrella, we consolidated our base and prepared for much larger endeavors. The pinnacle of this collaborative power was FOSS4G Belém. Bringing a global geospatial event to the heart of the Amazon was a monumental and symbolic triumph. It required immense resilience and coordination across a continental-sized country, but seeing hundreds of people debating the future of free geoinformation made it all worthwhile. Belém cemented OSGeo Brazil as a unified force capable of executing international-level projects while integrating global technology with local realities. Our journey, however, is far from over. The map of our future still holds uncharted territories. Today, our challenges involve ensuring project sustainability, attracting new generations from universities, and strengthening the open data ecosystem in Brazil. Through this talk, we aim to inspire other local chapters and share our ongoing mission to democratize geotechnology. The code is open, and the doors to our community remain wide open for whatever comes next. false https://talks.osgeo.org/foss4g-2026/talk/FRQRLV/ Ran1 Lightning talk 2026-09-01T14:15:00+09:00 14:15 00:05 Valhalla is my favorite routing engine. It's one of the most flexible options already. but we can make it even better! Help us build extensions that enable even more use cases and easier collaboration. foss4g-2026-5626-valinor-valhalla-meets-rust Ian Wagner en Valhalla is perhaps not as well known as OSRM or GraphHopper, but it's an incredibly flexible routing engine with a space-efficient tile format and dynamic routing profiles ("costing models"). But the codebase is relatively difficult for newcomers to understand, and it's relatively difficult to extend with new costing models. I and several other collaborators have been working on a library layer which aims to make the Valhalla ecosystem more approachable and more extensible. This lightning talk will outline my vision for the project, and how I think Rust and WASM can expand the Valhalla ecosystem. false https://talks.osgeo.org/foss4g-2026/talk/RVTHSV/ Ran1 Lightning talk 2026-09-01T14:20:00+09:00 14:20 00:05 This session introduces an open-source QGIS plugin that makes it easy to use basemaps, geocoding, and routing in everyday workflows without leaving QGIS. foss4g-2026-5261-amazon-location-service-plugin-for-qgis-basemaps-geocoding-and-routing Yasunori Kirimoto en QGIS is a widely used open-source desktop GIS, but adding basemaps, geocoding, and routing to everyday workflows often requires separate data sources, external services, or extra setup. For users who want to stay focused on their GIS work, this creates unnecessary overhead. The Amazon Location Service Plugin for QGIS is an open-source plugin that helps bridge this gap. By connecting QGIS with Amazon Location Service, users can access basemaps, geocoding, and routing directly within their familiar GIS environment. In this session, I will briefly introduce the plugin and demonstrate its three core features: Maps, Places, and Routes. Through this practical example, I will highlight how the open-source QGIS ecosystem can be extended to make useful geospatial capabilities easier to access. The plugin has been downloaded more than 5,000 times and is available as open source in the QGIS Python Plugins Repository. GitHub: https://github.com/MIERUNE/qgis-amazonlocationservice-plugin QGIS Python Plugins Repository: https://plugins.qgis.org/plugins/location_service Blog: https://aws.amazon.com/blogs/mobile/new-features-and-developer-experience-with-enhanced-amazon-location-service false https://talks.osgeo.org/foss4g-2026/talk/GAZ97Q/ Ran1 Lightning talk 2026-09-01T14:30:00+09:00 14:30 00:05 Building on previous research, we introduce a traffic simulation platform enhanced for real-world urban planning. By optimizing large-scale data processing and interactions, we demonstrate technical maturity as a practical decision-support tool, moving beyond visualization toward immediate, high-performance deployment in professional and production-ready environments. foss4g-2026-5173-web-based-high-performance-traffic-simulator-advancing-an-integrated-interaction-platform-for-urban-planning-decision-making /media/foss4g-2026/submissions/QY3TUS/IITP_Roadmap_Practical_Advancement_HfTn61D.jpeg Cheun-gill ParkKIMDEOKHYEON en Based on the 2D/3D visualization and scenario editing workflows established in previous years, the third year focuses on securing PERFORMANCE EXCELLENCE and OPERATIONAL EFFICIENCY required for professional environments. [Key Advancements] • WebGL-based High-Performance Engine and State Management Optimization : Optimized the WebGL rendering engine to process large-scale traffic networks and high-precision terrain data without latency. Designed a sophisticated State Management structure between Cesium and OpenLayers to ensure stable operation. • User-Centric Interaction Enhancement : Developed an interface to precisely control complex road structures, such as overpasses and underpasses. Improved UX to reflect professional workflows, increasing the convenience of simulation settings. • Flexible Data Compatibility : Strengthened Import/Export functionalities for seamless integration with external simulation engines and various GIS data formats, maximizing scalability and practical data utility. • Analytical Reporting System : Added a dashboard-based reporting feature to provide intuitive insights. This allows analytical data to be immediately utilized as evidence for urban policy-making. Technical Stack & Standards • 3D Geospatial Visualization: CesiumJS (Immersive 3D visualization, terrain rendering, and dynamic movement animation) • 2D Vector Rendering: OpenLayers (High-performance engine for WebGL-based 2D vector rendering) • Spatial Database: PostGIS / PostgreSQL (Robust spatial data storage, advanced indexing, and topological analysis) [Conclusion] In this presentation, we share our experience in building a PRODUCTION-READY PLATFORM that implements professional-grade functionality using open-source technologies, designed for IMMEDIATE DEPLOYMENT in actual urban planning processes. [Acknowledgment] This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (RS-2024-00459703, Development of next-generation AI integrated mobility simulation and prediction/application technologies for metropolitan cities) false https://talks.osgeo.org/foss4g-2026/talk/QY3TUS/ Ran1 Lightning talk 2026-09-01T14:35:00+09:00 14:35 00:05 This study proposes establishing “Young Mappers Clubs” in elementary schools in Addis Ababa to introduce OpenStreetMap mapping skills. Through interactive activities like sketch mapping and neighborhood exploration, the initiative aims to foster environmental awareness, geographic literacy, and early digital learning among young students. foss4g-2026-5201-introducing-osm-clubs-in-elementary-schools-building-young-mappers-for-a-better-future Aderaw Tsegaye Aniteneh en Youth Mappers has successfully built a global network of university student chapters that contribute to mapping underserved communities through OpenStreetMap (OSM). While this movement has empowered youth in higher education to engage with geography, technology, and civic responsibility, there is a growing need to extend this opportunity to even younger students. This proposal advocates for the expansion of OSM based clubs into elementary schools in Addis Ababa, Ethiopia, to cultivate environmental awareness and digital mapping skills from an early age. Children are naturally curious and observant of their surroundings. Starting OSM clubs at the elementary level will allow students to explore their neighborhoods, learn basic mapping concepts, and develop a sense of ownership and care for their environment. Through hands-on activities such as sketch mapping, local data collection, and digital storytelling, young students can begin to understand their communities in new ways. This not only enhances geographic literacy but also encourages early digital learning, problem-solving, and teamwork. The proposed pilot program, called “Young Mappers Club,” will be launched in selected primary schools in Addis Ababa. The club will follow a simplified and age-appropriate curriculum that introduces key concepts of OpenStreetMap, environmental awareness, and community mapping. Activities will be designed to be fun, interactive, and aligned with existing educational goals. With support from local YouthMappers university chapters, schoolteachers, and parents, the club will serve as a bridge between university-led initiatives and community-based learning. We believe that empowering students at a young age helps develop a lifelong interest in their environment, technology, and civic responsibility. This initiative will create a pipeline of future YouthMappers who are already familiar with OSM tools and values before they reach university level. It also contributes to a culture of participation and awareness within families and local communities. To bring this vision to life, we are seeking support from the YouthMappers and OSM community in the form of training materials, mentorship, and technical guidance. With collaboration and shared resources, Addis Ababa can become a model city for integrating OSM education into primary school systems. Let’s inspire the next generation of mappers—starting from the classroom. false https://talks.osgeo.org/foss4g-2026/talk/RHKBHX/ Ran1 Lightning talk 2026-09-01T14:40:00+09:00 14:40 00:05 GeoReports is a java servlet web application that provides a powerful tool for creating a geographically rich PDF Report containing a series of predefined pages relating to a location of interest. foss4g-2026-5155-georeports-web-application Simon Nitz en An administrator pre-defines one or more GeoReports config files (XML), each defining how a single PDF Report will be constructed. A single GeoReports config file contains all the logic required to produce a PDF Report on almost any subject matter. Examples of subject matter suitable for a GeoReports config file: * Property Report * Hazards Report * LIM (New Zealand Land Information Memorandum) Each page can contain combinations of maps and/or data derived from local data sources or via web services. One or more Templates (QGIS QPT Layout Temlates) referenced by the config file define the page layouts required throughout the PDF Report. Third party PDF documents can be inserted at any point either as defined pages, page ranges or an entire PDF document. false https://talks.osgeo.org/foss4g-2026/talk/RMZJHQ/ Ran1 Lightning talk 2026-09-01T14:45:00+09:00 14:45 00:05 By conducting a cartographic comparison of the transport infrastructure in two dissimilar cities utilizing space-time cubes and the cities’ transport index, this study aims to identify gaps in the multimodal transport systems and propose simple strategies for improving navigation in the cities. foss4g-2026-5624-cartographic-patterns-comparing-multimodal-transport-systems-in-diametrically-opposite-cities Poornima Badrinath en This study explores navigation patterns and transport infrastructure in two distinct urban environments—Bangalore, and Amsterdam, —through a comparative analysis. Using data visualizations such as space-time cubes and interactive maps, the research examines how time and space interact within each city's transport network. The objective is to identify gaps in multimodal transport systems and evaluate how effectively these systems support urban mobility. By comparing the spatial distribution and temporal efficiency of transport infrastructure across the selected cities, the study aims to highlight differences in urban planning and accessibility. Particular attention is given to how multimodal systems are integrated and how the built environment either supports or constrains seamless navigation. In addition, this research emphasizes the role of cartographic tools in understanding urban infrastructure and identifying areas for improvement. The ultimate goal is to propose simple, actionable strategies that enhance transport connectivity and contribute to more sustainable and efficient urban development. Now, the further research is also comparing cities of Berlin and Delhi and understanding the transport patterns. false https://talks.osgeo.org/foss4g-2026/talk/P9QD3K/ Ran1 Lightning talk 2026-09-01T14:50:00+09:00 14:50 00:05 geospatial-audio-js is an open-source JavaScript library that adds real-time 3D spatial audio to web maps. Sound sources placed at geographic coordinates respond to camera movement, opening new possibilities for accessibility, immersive audio guides, and soundscape data visualization. foss4g-2026-5530-hearing-the-map-spatial-audio-as-a-new-dimension-in-web-gis Tomoaki Hoshino en Geographic maps have always engaged our eyes. What if they could engage our ears too? We are developing a library called "geospatial-audio-js," an open-source JavaScript library that connects geographic coordinates to the Web Audio API's 3D sound engine (HRTF). Sound sources are placed at real-world lat/lon/altitude positions, and the listener's orientation automatically synchronizes with the map camera in real time — whether you're using MapLibre GL JS, Leaflet, or CesiumJS. Spatial audio opens meaningful possibilities beyond novelty: audio guides for tourism and cultural heritage sites, accessibility tools that let visually impaired users navigate a map by sound, soundscape layers tied to environmental field data, and sensory-rich integrations with XR and location-based applications. With just a few lines of code, any existing web map can gain a new sensory dimension. This talk introduces the library, walks through real-world use cases, and includes a live demo. false https://talks.osgeo.org/foss4g-2026/talk/TFWU9P/ Ran1 Lightning talk 2026-09-01T15:30:00+09:00 15:30 00:05 We present Z7 Explorer, a web application that computes IGEO7/Z7 grid indexes entirely client-side using a pure JavaScript port of DGGAL's ISEA projection engine. We demonstrate why IGEO7 — an equal-area, pole-seamless hexagonal DGGS — addresses fundamental limitations of H3 and conventional coordinate systems. foss4g-2026-5463-z7-explorer-why-igeo7-may-succeed-h3-as-the-standard-hexagonal-dggs Jorge S. Mendes de Jesus en Hexagonal grids have gained traction as a spatial indexing primitive across industry. H3, developed by Uber for ride pricing optimization, is now widely adopted in logistics, telecommunications, and platforms such as Snowflake, BigQuery, DuckDB, and CARTO. Yet H3 carries fundamental trade-offs. Its gnomonic projection is not equal-area: the area ratio between the largest and smallest hexagon at the same resolution grows from 1.2× at res 0 to nearly 2× at res 5 and above [1], with cell sizes varying by up to ±50% across the globe [4] — the distortion worsens with increasing resolution, precisely where spatial consistency matters most. Its 64-bit index spends 19 bits on metadata, leaving only 45 bits for spatial addressing, while Z7 implements a full 64-bit index pushing its maximum resolution to 20 and 6cm², which is 1,500× finer than H3 at max resolution of 15. The coordinate systems underpinning most geospatial workflows introduce further artifacts. WGS84 (EPSG:4326) suffers from polar compression and a discontinuity at the ±180° antimeridian that splits geometries spanning the Pacific. Web Mercator (EPSG:3857) inflates areas dramatically at high latitudes, excludes the poles beyond ±85.06°, and inherits the same antimeridian seam. IGEO7 sidesteps all of these problems [4]. The ISEA projection [2] maps the globe onto an icosahedron, guaranteeing equal-area cells at every resolution with no polar singularity and no antimeridian discontinuity. The Z7 indexing scheme devotes all 64 bits to spatial hierarchy — a 4-bit base cell plus 20 three-bit direction digits — encoding resolution implicitly with no wasted bits. Despite these advantages, IGEO7 adoption has been limited by tooling: computing Z7 indexes requires the full ISEA forward projection, historically available only through native C libraries (DGGAL [3], DGGRID). To lower this barrier, we ported DGGAL's ISEA projection engine and aperture-7 quantization to pure JavaScript, enabling Z7 lookups entirely in the browser. Z7 Explorer is the resulting open-source web application (Vue 3, D3.js). It provides interactive coordinate-to-Z7 lookup, resolution navigation (0–20), a 64-bit index visualizer, cell ID search, and vector-tiled basemap rendering — all computed client-side in a Web Worker with 100% accuracy against DGGAL reference data. By making IGEO7 accessible to a broader public, we intend to give developers and researchers a practical entry point into an equal-area DGGS that addresses H3's core limitations. Live demo: https://z7.terraops.org [1] Uber, "Tables of Cell Statistics Across Resolutions," H3 Docs. https://h3geo.org/docs/core-library/restable/ [2] Snyder, "An Equal-Area Map Projection for Polyhedral Globes," Cartographica, 29(1), 1992. [3] Ecere, "DGGAL — Discrete Global Grid Abstraction Library." https://github.com/ecere/dggal [4] Kmoch et al., "IGEO7: A new hierarchically indexed hexagonal equal-area DGGS," AGILE GIScience Ser., 6, 32, 2025. false https://talks.osgeo.org/foss4g-2026/talk/THWFLY/ Ran1 Lightning talk 2026-09-01T15:35:00+09:00 15:35 00:05 Jointly optimizing map styles and underlying data can significantly improve vector map performance. This talk shows how data- and style-driven techniques reduce tile size, speed up loading, and improve client rendering—without compromising visual quality - based on results from real-world datasets and styles. foss4g-2026-5500-styles-data-co-optimisation-for-better-performance Frank Elsinga en This talk explores how map styles and underlying data can be automatically optimized together to improve performance and resource efficiency. Using practical examples, it demonstrates how data- and style-driven techniques can reduce tile sizes, shorten loading times, and make client-side rendering more efficient. With the introduction of the OpenStreetMap Foundation’s vector tiles, the barrier to using high-resolution, client-rendered vector maps has never been lower. However, these maps often struggle with performance compared to server-rendered alternatives. How can we close this gap? One overlooked opportunity is the joint optimization of visual styles and underlying data. Existing systems - and even much of the research - treat these separately, leading to unnecessary data transfer, higher server load, and reduced client performance. In this talk, I present my master’s thesis, which approaches data- and style-driven optimization of client-rendered vector maps as a spatial query problem using database techniques. The result: reduced data size and complexity, and improved performance - without compromising visual quality, but at the cost of worse debuggability and flexibility. The talk focuses on the real-world impact of these optimizations on network usage, rendering performance, and energy consumption across actual map styles and datasets. false https://talks.osgeo.org/foss4g-2026/talk/U79GK9/ Ran1 Lightning talk 2026-09-01T15:40:00+09:00 15:40 00:05 In the winter of 2024, a corporate tech-for-good program was shut down, and with it nearly all its OS projects went dark. In this lightning talk, we’ll cover key lessons of what went right—and what didn’t—to help other OS efforts avoid the same fate. foss4g-2026-5655-lessons-from-the-os-grave-learning-from-the-collapse-of-a-tech-for-good-open-source-program Erin Stein en In the winter of 2024, a corporate tech-for-good program was shut down without advance warning, and with it nearly all its open source projects went dark. Thousands of hours of strategy and development by the team, its volunteers, and its partners, were dedicated to making open data accessible, and demonstrating open data’s potential. The future seemed bright—partners were excited by the concepts, volunteers offered to contribute, and peers were attending their presentations (including at FOSS4G!). When the tech-for-good program unexpectedly closed its doors, nearly all these OS tools suffered the same fate. While no one predicted the untimely demise, hindsight is 20/20. As one of the core team members, I’ll share insights into what went right and what didn’t so others can secure a more enduring OS legacy. false https://talks.osgeo.org/foss4g-2026/talk/UXRSLR/ Ran1 Lightning talk 2026-09-01T15:45:00+09:00 15:45 00:05 MAPME is a community-driven initiative promoting access to open GIS resources for planning, monitoring, and evaluation in international development. This lightning talk introduces its mission, key open-source tools, and use cases, showing how geospatial data drives development impact and how participants can join and contribute. foss4g-2026-5349-mapme-initiative-open-geospatial-ressources-for-development-impact /media/foss4g-2026/submissions/VGCM7M/MAPME_LoymJah.png Jin IgarashiAlison Lenaerts en The MAPME Initiative (Monitoring, Assessment, and Planning for Measurable Effectiveness) is a community-driven effort that promotes access to open GIS resources for planning, monitoring, and evaluating projects in international development cooperation. By connecting practitioners, researchers, and decision-makers, MAPME fosters collaboration and the co-creation of tools and solutions using Earth Observation and Geographic Information Systems (GIS). The MAPME Initiative connects global development actors through four complementary pillars: a collaborative community that shares knowledge and fosters synergies; open resources and analytical tools for monitoring and impact evaluation; real-world collaborative projects applying geospatial methods; and the Geo4Impact event, which promotes exchange, partnerships, and innovation around open GIS for sustainable development. In this lightning talk, we will introduce MAPME’s mission, showcase key open-source tools, and highlight concrete collaborations that demonstrate the initiative’s impact. Attendees will learn how MAPME empowers its members to transform geospatial data into actionable knowledge and how they can actively contribute to the initiative—whether by joining the community, sharing expertise, or applying MAPME tools to their own projects. false https://talks.osgeo.org/foss4g-2026/talk/VGCM7M/ Ran1 Lightning talk 2026-09-01T15:50:00+09:00 15:50 00:05 GIS is amazing, but there are many people who would like to create a map, who don't know GIS. gitRmap is an easy way of creating a simple map. It is designed for people who know a little bit of Git, but don't know any R or GIS. foss4g-2026-5408-gitrmap-guitar-map-easy-maps-for-git-users Nick Bearman en GIS is a great tool, but there are many people who would like to create a map, but don't know any GIS. gitRmap is an easy way of creating a simple map. It is designed for people who know a little bit of Git, but don't know any R or GIS. The idea is you can fork the repo, add some data (name, location, etc.) to a CSV file, and then GitHub Actions will automatically create a web map. I created a version of this for my attendance at the Open Science Retreat (https://open.science-retreat.org/), where I know there are many people who want to make a map, and have Git skills, but don't have GIS skills. This was based on Michele Tobias's Travelling GIS Chat Book map (https://github.com/MicheleTobias/traveling-gis-chat-book) which is a automated Leaflet webmap, where users can provide locations (city, state, country) in a CSV file, and GitHub actions will create the Leaflet map using an R script. I have now received £500 from OSGeo:UK GoFundGeo award (https://uk.osgeo.org/gofundgeo.html) to develop this into a reuable tool which I am in the process of doing. I will present my progress and the tool at FOSS4G. false https://talks.osgeo.org/foss4g-2026/talk/UYL89P/ Ran1 Lightning talk 2026-09-01T16:00:00+09:00 16:00 00:05 Testing a CityGML-tileset conversion pipeline using topologies does not work: tile writers can add or remove vertices and even change geometry types. We show how statistical + raster-based testing in Re:Earth flow catches bugs in its engine development. foss4g-2026-5168-testing-plateau-pipelines-in-re-earth-flow-when-topology-doesn-t-survive /media/foss4g-2026/submissions/VET989/Screenshot_2026-03-13_at_11.08.50AM_XKCJuOP.png Taosheng Qiu en ## Background Re:Earth Flow is an open-source geospatial workflow engine designed to replace proprietary tools like FME. One of its use cases is converting Japan's PLATEAU CityGML datasets into various tile formats including Cesium 3D Tiles and Mapbox Vector Tiles. ## Problem End-to-end testing is desired to ensure the correctness of final outputs after all post-processing operations. However, classical geometry-based test cannot work when topology changes. For example, MVT writers can crop geometries or potentially change geometry types, causing false-positives in classical geometry-based test. ## Solution We started from implementing classical geometrical comparison algorithms such as symmetric difference for polygons and Hausdorff distances. However, classical geometry-based tests cannot survive topology changes. For example, MVT writers can crop geometries or change geometry types entirely, causing false positives in any structure-based test. ## Implementation MVT tiles are converted to grayscale PNG using a geometry decoder and an antialiased rasterizer. Canvas size and error threshold can be adjusted to control tolerance: sub-pixel geometric differences are naturally absorbed by the canvas resolution, and pixel differences below 0.5 are ignored to filter out antialiasing noise. The testing framework separates conversion and comparison into two stages: the first produces human-inspectable intermediates, and the second compares them against truth files. When a test breaks, you can open the PNG and see exactly what changed. ## Conclusion Rasterization is the robust approach for visualization-focused tests. As a next step, we plan to introduce a rasterization pipeline for 3D tiles, possibly with LLVMpipe for headless rendering. false https://talks.osgeo.org/foss4g-2026/talk/VET989/ Ran1 Lightning talk 2026-09-01T16:05:00+09:00 16:05 00:05 This talk celebrates how women volunteers like us strengthen and diversify open source geospatial ecosystems, especially in underrepresented regions from novice starter to member and Ambassador of various open source communities. foss4g-2026-5254-women-in-open-source-volunteering-from-starting-out-to-making-a-real-difference Dibikshya Shrestha en Open source communities like OpenStreetMap, OSGeo, Youthmappers and Humanitarian OpenStreetMap always prioritize underrepresented groups and promotes inclusive collaboration for everyone. Yet women's participation remains low, often below 10% in major ecosystems (and even lower in some geospatial mapping projects historically). Still, this focus on inclusion has empowered active women to contribute, make a difference and get support and praise in the community. In this lightning talk, I share my own 4 year journey as a woman from Nepal, starting from hesitant first steps to creating real, lasting impact. I began exploring beginner-friendly contributions in geospatial open source projects (like OSM mapping and QGIS-related tools). Over time, my contributions expanded to roles that amplified community strength: advocating for open source applications in Nepal's context, organizing and contributing to events like OSM Hackfest, the Climate Change Fellowship, and Open Source Map Design Competitions. These efforts have helped build Nepal's open geospatial community, empowered youth and other women to join, and created tangible local impacts. This talk celebrates how women volunteers like us strengthen and diversify open source geospatial ecosystems, especially in underrepresented regions from novice starter to member and Ambassador of various open source communities. false https://talks.osgeo.org/foss4g-2026/talk/YDG3D7/ Ran1 Lightning talk 2026-09-01T16:10:00+09:00 16:10 00:05 This presentation introduces the R package seg developed nearly fifteen years ago. I would like to share the difficulties encountered while developing and maintaining a small academic package, especially as job changes and passions shift. foss4g-2026-4975-maintaining-a-small-research-tool-experiences-from-the-r-package-seg Seong-Yun Hong en Approximately fifteen years ago, I developed the R package seg as part of my doctoral research. The package implemented a set of spatial segregation measures used in geography and sociology. Although it remained on CRAN for many years, it received few updates, as I started a job and my interests changed to other areas. With the transition in R spatial infrastructure from sp to sf, the package became incompatible with newer versions and was eventually archived. At the time, I regarded its removal as inconsequential, assuming its utility had lapsed. However, after its removal from CRAN, I started receiving a few inquiries from users requesting an update. Motivated by these requests, I undertook the task of updating seg. While technically straightforward, the process was prolonged by the challenges of revisiting old code and producing adequate documentation. This presentation is about that experience: the reasons behind the package's long period of neglect, the effort required to restore it, and the difficulties I experienced in maintaining small-scale academic software projects. false https://talks.osgeo.org/foss4g-2026/talk/YLAYNZ/ Ran1 Lightning talk 2026-09-01T16:15:00+09:00 16:15 00:05 Discover how the QGIS Brazil community uses translation and local networking to democratize FOSS4G. This talk highlights how breaking the language barrier is essential for empowering local governments, students, and professionals in South America, turning translation efforts into massive software adoption and community growth. foss4g-2026-5054-bridging-the-language-gap-how-the-qgis-brazil-community-drives-open-source-adoption-in-the-global-south Narcélio de Sá en In many countries of the Global South, the English language remains a significant barrier to the adoption of Free and Open Source Software. In Brazil, the QGIS community has turned this challenge into a movement. As a member of the QGIS Brazil coordination team, I will share our experience in organizing translation sprints and maintaining a 100% localized software environment. We will look at how our ecosystem operates in practice: Localization as Inclusion: Maintaining QGIS in Portuguese isn't just about translation; it's about accessibility. By localizing the software, website, and docs, we remove the first and most significant barrier for public servants, students, and local researchers. A Living Support Network: Our community doesn't wait for official tickets. Through active channels on mailing lists, Telegram, and WhatsApp, thousands of users exchange knowledge daily, creating a massive, peer-to-peer technical support system. From Community to Governance: Our support network has made QGIS the standard in Brazil. It is now the primary tool for state environmental licensing, university research, and municipal administration. By providing a localized and reliable environment, we’ve shifted from being a 'low-cost alternative' to the first choice for professional public management and academia. This presentation shares the Brazilian experience to show that for non-English speaking regions, localization is not a secondary task—it is the foundation for technological sovereignty and professional growth. false https://talks.osgeo.org/foss4g-2026/talk/ZJCCFJ/ Ran1 General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 TerriaJS is an open-source TS/JS framework for building rich, 2D and 3D web-based geospatial data platforms such as Digital Earth Australia, the WA Digital Twin, and numerous government and research deployments worldwide. This talk presents the current state of the project. foss4g-2026-5506-state-of-terriajs Ana Belgun en After a year of deliberate consolidation in 2025 — focused on dependency modernisation, security hardening, React 18 and MobX upgrades, and build system improvements — 2026 has been declared the "year of the map," with a renewed focus on new features, expanded data format support, and growing the open-source ecosystem. This talk covers recent technical milestones including the completed React 18 migration, performance improvements for large ArcGIS FeatureServer datasets, PMTiles rendering enhancements, the new MapboxSearchProvider, and expanded internationalisation support. Beyond the code, we reflect on the broader lessons learned in sustaining an open-source geospatial project at the intersection of government adoption, commercial development, and community stewardship. false https://talks.osgeo.org/foss4g-2026/talk/JH38ZK/ Ran1 General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 PDAL is Point Data Abstraction Library. It is a C/C++ open source library and applications for translating and processing point cloud data. This is an update on the status of the library and new and updated features and capabilities over the last 2 years. foss4g-2026-5486-state-of-pdal Michael Smith en PDAL is Point Data Abstraction Library. It is a C/C++ open source library and applications for translating and processing point cloud data. It is not limited to LiDAR data, although the focus and impetus for many of the tools in the library have their origins in LiDAR. PDAL allows you to compose operations on point clouds into pipelines of stages. These pipelines can be written in a declarative JSON syntax or constructed using the available API. This talk will focus on the current state of the PDAL Pointcloud processing library and related projects such as COPC and Entwine, for pointcloud processing. Coverage of the most common filters, readers and writers along with some general introduction on the library, coverage of processing models, language bindings and command line based batch processing. First part will be covering new features for current users. Some discussion of installation method including Docker, binaries from package repositories, and Conda packaging. For more info see https://pdal.io false https://talks.osgeo.org/foss4g-2026/talk/HXMXN7/ Ran1 General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 An update on the OGC SensorThings API from the OGC standards working group: what's new in v2.0 (to be published in 2026), the open source servers and clients available today, real-world geospatial IoT use cases across sectors, and what's coming next. foss4g-2026-5092-the-state-of-ogc-sensorthings-api /media/foss4g-2026/submissions/QSFUVY/Gemini_Generated_Image_7ipoxl7ipoxl7ipo_EDjo9An.png Steve Liang en The OGC SensorThings API (STA) is an OGC standard for managing and sharing observations and tasking capabilities over the Web. Built on OGC Observations, Measurements and Samples (OMS / ISO 19156), STA provides a common data model and API for interconnecting IoT devices, monitoring platforms, and data applications -- whether you are running real-time sensor networks or exposing legacy observation databases. **What's New in v2.0.** We will walk through the key changes in the latest version of the standard: tighter alignment with OMS, a more flexible core data model with standardized extension points, new OData bindings, and enhanced query capabilities. We will explain what has changed since v1.1 and what it means for anyone building -- or planning to build -- scalable geospatial IoT applications with open source software. **Open Source Ecosystem.** SensorThings has a growing landscape of open source implementations. We will present the available servers and client libraries across multiple languages. Whether you need to stand up a standards-compliant sensor data endpoint or build applications that consume observation data, we will point you to the tools and show you where to get started -- including a live demo of standing up an IoT application in minutes. **Deployments Around the World.** From national environmental monitoring networks to smart city platforms, digital twins, and scientific research infrastructure, SensorThings is deployed across diverse sectors and geographies. We will highlight use cases that go well beyond traditional IoT -- including field inspection and repair, methane reduction operations, and integration layers over legacy databases -- showing the breadth of what the standard supports in practice. **Roadmap.** Finally, we will share what the SensorThings Standards Working Group is working on next: upcoming extensions, community priorities, and how you can get involved. Whether you already run SensorThings or are evaluating it for your data infrastructure, this talk will bring you current on the standard, the tools, and the community. false https://talks.osgeo.org/foss4g-2026/talk/QSFUVY/ Ran2 General session talk 2026-09-01T13:00:00+09:00 13:00 00:30 Learn of all that is new in MapLibre - changes to the web map renderer, the native, the Martin tile server, and numerous other changes including the short intro into MLT - new tile format. foss4g-2026-4986-the-state-of-maplibre-ecosystem-update Yuri Astrakhan en Over the past year, the MapLibre ecosystem has taken major steps forward, introducing innovations that modernize open-source mapping from the tile format all the way to client SDKs and servers. At the center of this evolution is MapLibre Tile (MLT) — a next-generation successor to the traditional MVT format. Designed for today’s graphics pipelines and large-scale geospatial workloads, MLT delivers significantly smaller tiles, faster decoding, and a foundation for future capabilities such as improved 3D support and more efficient GPU workflows. With a column-oriented layout and lightweight encodings, it improves compression and performance, enabling maps that load faster and scale better. Support for MLT is already landing across the MapLibre ecosystem, making experimentation and adoption practical today. Beyond the tile format, both MapLibre GL JS and MapLibre Native continue to advance. Recent improvements include better performance with large GeoJSON datasets, enhanced font and international text rendering, smoother symbol placement, and ongoing rendering backend work that prepares the stack for the next generation of graphics APIs. These updates benefit web, mobile, and embedded applications alike. On the server side, Martin, the open-source tile server, has matured into a production-ready solution capable of serving vector and raster tiles from multiple backends. With continued performance improvements and support for modern tile formats, it strengthens the end-to-end open mapping pipeline. This talk will explore how vector tiles are being reborn within the MapLibre ecosystem — smaller, faster, and more capable — and showcase the latest progress across web, native, and server components that are shaping the future of open mapping. false https://talks.osgeo.org/foss4g-2026/talk/VNPC9N/ Ran2 General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 MapStore is an open-source webgis built on React and Redux to create and share maps, dashboards, and geostories. It is mobile-ready and supports OpenLayers, Leaflet, and Cesium. This presentation covers current features, future roadmaps, and real-world case studies. foss4g-2026-5417-state-of-mapstore Lorenzo NataliTobia Di PisaStefano Bovio en MapStore is an open source product developed for creating, saving and sharing in a simple and intuitive way maps, dashboards, charts and geostories directly online in your browser. MapStore is cross-browser and mobile ready, it allows users to: Search and load geospatial content served using widely used protocols (WMS, WFS, WMTS, TMS, CSW, 3D Tiles) and formats (GML, Shapefile, GeoJSON, KML/KMZ etc..) Manage maps (create, modify, share, delete, search), charts, dashboard and stories directly online Manage users, groups and their permissions over the various resources MapStore can manage Edit data online via WFS-T with advanced filtering capabilities Deeply customize the look&feel to follow strict corporate guidelines Manage different application contexts through an advanced wizard to have customized WebGIS MapStore viewers for different use cases (custom plugins set, map and theme) You can use MapStore as a product to deploy simple geoportals by using the standard functionalities it provides but you can also use MapStore as a framework to develop sophisticated WebGIS portals by reusing and extending its core building blocks. MapStore is built on top of React and Redux and its core does not explicitly depend on any mapping engine but it can support both OpenLayers, Leaflet and Cesium; additional mapping engines could be also supported (for example MapLibre GL) to avoid any tight dependency on a single engine. The presentation will give the audience an extensive overview of the MapStore functionalities for the creation of mapping portals, covering both previous work (e.g. new features released during the last year) as well work for the future releases. Eventually, a range of MapStore case studies will be presented to demonstrate what our clients (like City of Genova, City of Florence, Halliburton, Austrocontrol and more) and partners are achieving with it. false https://talks.osgeo.org/foss4g-2026/talk/9WFWV8/ Ran2 General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 This session provides an overview of the GeoPlegma project, how it evolved the past year and what is in store for the next. foss4g-2026-5151-the-state-of-geoplegma Luís M. de Sousa en GeoPlegma is an ambitious project to provide a unified development platform for DGGS software. Written in Rust, it exposes an API to interrogate Discrete Global Grid Reference Systems (DGGRS) and create data structures based on DGSS. Besides a native DGGS implementation, GeoPlegma is also a bridge to other implementations, including: - DGGRID - DGGAL - H3 Ultimately, GeoPlegma intends to be the bedrock on which a new generation of GIS software may be build, in a geodesic paradigm, dispensing the trade-offs of traditional cartography with map projections at large scales. false https://talks.osgeo.org/foss4g-2026/talk/C8WCBB/ Ran2 General session talk 2026-09-01T14:30:00+09:00 14:30 00:30 The MapLibre Tile Format is a new open, community-governed successor designed to overcome compression, interoperability, and extensibility limits of old. This talk explains its design principles, and ongoing development, including compression, and tooling, while outlining the roadmap and opportunities for solving the hard problems we all face. foss4g-2026-5046-state-of-the-maplibre-tile-format /media/foss4g-2026/submissions/G8K8VT/maplibre-logo-white_s543bbc_CcQboMr.png Yuri AstrakhanFrank Elsinga en The MapLibre community is currently in the midst of developing the MapLibre Tile Format, a modern, open, and fully community-governed successor to the ubiquitous Mapbox Vector Tile (MVT) format. While MVT has served the mapping ecosystem well for over a decade, it also carries historical constraints that limit interoperability, formal specification quality, extensibility, and independence from proprietary platforms. As MapLibre continues to grow as the central open-source foundation for web-based map rendering, it has become increasingly clear that a future-proof, openly specified, and collaboratively designed tile format is essential. This talk will offer a look into why we initiated this engineering effort and what gaps the new format aims to close. I will explain the core design principles behind the specification—clarity, strictness where needed, optionality where useful, and full transparency throughout the process. Attendees will gain a technical understanding of how the format works, including its data model, feature encoding strategy, metadata approach, and compatibility considerations for existing infrastructure. Beyond the current specification draft, I will outline the major areas still under active development. These include discussions about schema evolution, advanced geometry representations, compression strategies, and interoperability with raster, elevation, 3D and non-geographic datasets. I will also provide insight into the collaborative workflow between maintainers, researchers, vendors, and the wider open-source community, highlighting where contributions and feedback are particularly welcome. Finally, the talk will cover how the rollout is progressing in practice. This includes early tooling support, reference implementations, testing frameworks, and real-world trials by organizations exploring migration paths away from MVT. The session will present an honest, up-to-date snapshot of the project’s status and a forward-looking roadmap for the next stages of development, helping the community understand both what is ready today and what is still on the horizon. false https://talks.osgeo.org/foss4g-2026/talk/G8K8VT/ Ran2 General session talk 2026-09-01T15:30:00+09:00 15:30 00:30 A practical tour of GeoServer 3 covering upgrade steps, new requirements, refreshed UI, and updated module structure. Learn what’s changed, what remains familiar, and how to transition from GeoServer 2.x efficiently, with a focus on real-world adoption and getting up to speed quickly. foss4g-2026-5466-geoserver-3-tour Andrea Aime en GeoServer 3 introduces a modernized foundation while maintaining the familiar concepts and workflows that users rely on. This session offers a guided tour of the new release, focusing on what has changed in practice and how to approach the transition from GeoServer 2.x. We’ll walk through the upgrade process, highlighting prerequisites such as updated Java and servlet container requirements, and what to expect when migrating existing installations. The session will also showcase the refreshed user interface, discussing its evolution and the improvements it brings in terms of usability and productivity. In addition, we’ll explore the updated module structure, what is included in the core distribution, and how extensions are organized in GeoServer 3. Rather than covering every detail, the goal is to provide an overview of the new system and help users quickly get up to speed. Join us for a practical overview of GeoServer 3, designed to make adoption straightforward and predictable for both new and existing users. false https://talks.osgeo.org/foss4g-2026/talk/DAFWU9/ Ran2 General session talk 2026-09-01T16:00:00+09:00 16:00 00:30 Rendering dynamic map typography is notoriously difficult. Standard engines rely on pre-baked PBF glyphs, causing network bloat and breaking complex text shaping (like Arabic). This talk explores these pitfalls and reveals how Navara abandons PBFs entirely, utilizing a dynamic rendering architecture for superior global maps. foss4g-2026-5134-making-maps-readable-a-dive-into-font-rendering-in-navara Adel Refaat en This presentation is a technical journey into the architecture of map engine typography, contrasting industry-standard methods with Navara’s custom approach. The talk is structured in three parts: - **The Problem (Why Map Text is Hard):** A quick look at the unique constraints of GIS typography, why map text is uniquely difficult compared to standard UI text - **The PBF Paradigm & Its Pitfalls:** We will break down how standard vector engines serve fonts via the SDF/PBF pipeline. I will explain why this "pre-baking" method was historically necessary, but also why it falls short today—specifically regarding network bloat for large character sets and the inability to properly handle Complex Text Layouts (CTL) dynamically. - **The Navara Solution:** We will pop the hood on Navara's rendering engine to show how we bypassed the PBF bottleneck entirely. I will detail our alternative architecture for loading fonts, executing client-side text shaping, and sending text to the GPU on the fly, sharing the performance trade-offs and lessons we learned along the way. false https://talks.osgeo.org/foss4g-2026/talk/MZZ9BS/ Ran2 General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 This talk presents a practical playbook for taking GeoServer to production, covering performance tuning, data preparation, caching strategies, and operational controls. Drawing from real enterprise deployments at GeoSolutions, including GeoServer Cloud, it provides actionable guidance to build stable, scalable, and high-performance geospatial services. foss4g-2026-5567-lessons-from-running-geoserver-at-scale Andrea Aime en Setting up GeoServer can be deceptively simple. Bringing it into production—stable, performant, and capable of handling real-world traffic—is a different challenge. This talk distills hands-on lessons from enterprise GeoServer deployments into a practical playbook, covering the full journey from initial setup to a production-ready service, including modern cloud-native approaches such as GeoServer Cloud. We explore the configuration decisions that matter most in production: selecting output formats to avoid network bottlenecks, preparing vector and raster data for the multi-resolution demands of web GIS, and tuning SLD styling to balance visual quality with rendering performance. We then move to caching strategies, demonstrating how to configure GeoWebCache effectively for background layers, and how to identify scenarios where caching can be counterproductive. Service limits, the control-flow extension, and the monitoring extension are presented as key operational tools for maintaining stability under real user load—helping identify slow requests, resource-intensive clients, and the services and layers that require closer attention. JVM sizing and container configuration are addressed at a practical level, focusing on actionable guidance rather than theory, with notes on how these considerations evolve in containerized and cloud-based deployments. The session concludes with real-world examples from enterprise deployments carried out by the speaker and colleagues at GeoSolutions, spanning government SDIs, environmental monitoring platforms, and large-scale humanitarian mapping systems. For each scenario, we highlight the configuration choices and tuning strategies that made a measurable difference: which caching approaches were adopted and why, how service limits were aligned with actual client behavior, and how load testing validated each improvement prior to go-live. Attendees will leave with concrete patterns they can immediately apply to their own installations. false https://talks.osgeo.org/foss4g-2026/talk/E3EWXC/ Ran2 General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 A tour of the latest-and-greatest in the STAC software ecosystem, with a focus on demonstrations and use-cases. foss4g-2026-5206-the-modern-stac-software-ecosystem Pete Gadomski en The SpatioTemporal Asset Catalog (STAC) specification celebrated its fifth birthday in May. A key part of STAC’s adoption story is its robust, community-supported ecosystem of software and applications, with clients and APIs written in a variety of languages. Without a single company or entity driving development, STAC software advances and maintenance have been a story of self-organization, individual initiative, and collaboration. The STAC software ecosystem can be complex and hard-to-navigate, especially to newcomers. In this talk, we’ll walk you through the key software repositories and applications, with a focus on practical demonstrations and use-cases. There’s been some advances over the last year that we’ll highlight: - Better tooling and support for stac-geoparquet - In-browser visualization advances in stac-browser and stac-map - A major update to the core Python STAC software package, PySTAC v2.0, including obstore support, cleaner read-write semantics, and better extension hooks We’ll also talk about plans for the coming year, and provide pointers on places where you or your organization can plug in! false https://talks.osgeo.org/foss4g-2026/talk/H9E37D/ Ran2 General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 This presentation demonstrates how GeoServer enables efficient management, discovery, and visualization of large Earth observation datasets. Through real-world examples, it covers indexing (STAC/OpenSearch), Cloud Optimized GeoTIFFs, mosaicking and filtering, data extraction (WCS/WPS), time-based animations, and band algebra, highlighting GeoServer’s latest capabilities in satellite imagery workflows. foss4g-2026-5472-serving-earth-observation-data-with-geoserver-addressing-real-world-requirements Andrea AimeSimone Giannecchini en Never before has such a vast and diverse collection of satellite imagery been available to both organizations and the general public. With missions such as Landsat 8 and Sentinel, the rapid growth of CubeSats, and the open availability of global datasets through programs like the European Copernicus initiative—alongside data captured by drones—we are now experiencing an unprecedented influx of Earth observation data. Effectively managing, discovering, and visualizing this volume of imagery presents significant challenges. This presentation explores how GeoServer addresses these challenges through real-world use cases, including: - Indexing and discovery of imagery using OpenSearch for EO and STAC protocols - Efficient and cost-effective management of large datasets with Cloud Optimized GeoTIFFs (COGs) - Visualization of image mosaics and creation of composites with flexible filtering and stacking strategies (e.g., most recent, least cloudy, or custom ordering) - Extraction of imagery at varying scales using WCS and WPS protocols - Generation and visualization of time-based animations over selected periods - Execution of band algebra operations using Jiffle Join this session for an overview of the latest GeoServer capabilities in the Earth Observation domain. false https://talks.osgeo.org/foss4g-2026/talk/REYCUC/ Conference Management Room1 General session talk 2026-09-01T13:00:00+09:00 13:00 00:30 Overview of OGC APIs implementation in GeoServer. OGC APIs offer a modern, modular, RESTful geospatial services using JSON and extensible building blocks. foss4g-2026-5471-ogc-apis-with-geoserver-3-implementation-avaialbility-and-next-steps Andrea Aime en The OGC APIs are a fresh take at doing geo-spatial APIs, based on WEB API concepts and modern formats, including: - Small core with basic functionality, extra functionality provided by extensions - OpenAPI/RESTful based - JSON first, while still allowing to provide data in other formats - No mandate to publish schemas for data - Improved support for data tiles (e.g., vector tiles) - Specialized APIs in addition to general ones (e.g., DAPA vs OGC API - Processes) - Full blown services, building blocks, and ease of extensibility This presentation will provide an introduction to various OGC APIs and extensions, such as Features, Styles, Maps and Tiles, Processes, STAC and CQL2 filtering. Some of specs are finalized and complete enough that they have a GeoServer supported extensions, while others are provided as community modules. Join us to find out the current state of implementation, our future steps, and how you can participate in it. false https://talks.osgeo.org/foss4g-2026/talk/KDWRCY/ Conference Management Room1 General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 pycsw project status presentation. Come and find out the latest news on the project as well as future plans, and how to get involved! foss4g-2026-4803-pycsw-project-status /media/foss4g-2026/submissions/KVXLTL/pycsw-logo-vertical_UCN9dGA.png Tom KralidisAngelos Tzotsos en pycsw is an OGC API - Records and OGC CSW server implementation written in Python. Started in 2010 (more formally announced in 2011), pycsw allows for the publishing and discovery of geospatial metadata via numerous APIs (CSW 2/CSW 3, OpenSearch, OAI-PMH, SRU), providing a standards-based metadata and catalogue component of spatial data infrastructures. pycsw is Open Source, released under an MIT license, and runs on all major platforms (Windows, Linux, Mac OS X).The project is certified OGC Compliant, and is an OGC Reference Implementation. The project currently powers numerous high profile catalogues such as EOEPCA, IOOS, NGDS, NOAA, US Department of State, US Department of Interior, geodata.gov.gr, Met Norway and WMO WOUDC. This session starts with a status report of the project, followed by an open question answer session to give a chance to users to interact with members of the pycsw project team. This session will cover how the project PSC operates, the current project roadmap, and recent enhancements focused on ESA's EOEPCA, Open Science Data Catalogue and OGC API - Records. false https://talks.osgeo.org/foss4g-2026/talk/KVXLTL/ Conference Management Room1 General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 GeoServer 3 completes a long-planned modernization effort updating core dependencies while preserving backwards compatibility. This talk revisits the transition, outlines how it was organized and delivered, shares lessons learned and future directions. foss4g-2026-5464-geoserver-3-complete-final-update Andrea AimeJody Garnett en GeoServer 3 marks the completion of a long-planned modernization effort aimed at keeping the project aligned with the current Java ecosystem, while preserving the stability and backwards compatibility that users rely on. Now that GeoServer 3 has been available for a few months, this session provides a final update on the work and its outcomes. We’ll revisit the initial drivers behind the transition, starting from the upgrade of core dependencies, how that cascaded to more updates, resulting in a coordinated effort across multiple projects. From there, we’ll outline the main phases of the work: how the upgrade was organized, funded, managed new needs, and ultimately delivered. The talk focuses on the process and its results: what it took to modernize a mature codebase while maintaining a high degree of compatibility, the challenges encountered along the way, and how they were addressed. We’ll also share early feedback from adoption and what users can expect when moving to GeoServer 3. Join us for a practical retrospective on the transition to GeoServer 3, and a discussion of how the project continues to evolve while staying true to its core principles. false https://talks.osgeo.org/foss4g-2026/talk/BTVQ8N/ Conference Management Room1 General session talk 2026-09-01T14:30:00+09:00 14:30 00:30 Open Software and Open Standards are complementary pieces of the geospatial ecosystem. In 2022, OSGeo and OGC signed a new Memorandum of Understanding (MoU) that aims to benefit the mission and goals of both organizations. This presentation will provide an update on collaboration, reference implementations, code sprints, and future plans. foss4g-2026-4887-osgeo-and-ogc-mou-update Tom KralidisAngelos Tzotsos en Open Software and Open Standards are complementary pieces of the geospatial ecosystem. In January 2022, OSGeo and OGC signed a new and updated version of the Memorandum of Understanding (MoU) that aims to maximize the achievement of the mission and goals of both organizations. Execution of joint Code Sprints, identifying free and open source technologies that could be used as Reference Implementations for OGC Standards and validating OGC compliance tests are examples of activities that can take place within the scope of the agreement. The current MOU can be found at https://www.osgeo.org/wp-content/uploads/MOU_OGC_OSGeo_2022_signed.pdf false https://talks.osgeo.org/foss4g-2026/talk/8JHWMS/ Conference Management Room1 General session talk 2026-09-01T15:30:00+09:00 15:30 00:30 We introduce landlensdb, an open-source Python package for managing proximity sensing imagery, including action cameras, 360° cameras, and UAVs, using PostgreSQL/PostGIS. It automates metadata extraction, corrects geolocation errors via road network snapping, and enables scalable spatial-temporal queries and visualization for large-scale geotagged image datasets. foss4g-2026-4951-landlensdb-a-python-package-for-managing-proximity-sensing-imagery Iosefa PercivalNarumasa Tsutsumida en Proximity sensing imagery, captured by action cameras, 360° panoramic cameras, and UAV-mounted cameras, offers unique human-scale perspectives on natural and built environments that complement traditional remote sensing. These images reveal fine-scale environmental features that overhead sensing typically misses. However, managing vast volumes of geotagged images presents technical challenges including GPS positional errors, varying camera geometries, and complex spatial-temporal relationships. We developed landlensdb, an open-source Python package for efficient management of geotagged proximity sensing imagery using PostgreSQL/PostGIS. The package supports diverse acquisition platforms within a unified framework, handling both local image directories and Mapillary API data sources. Core features include automated metadata extraction from EXIF data, geolocation correction by snapping image positions to reference geometries such as road networks, and storage in a PostGIS database with a standardized yet extensible schema. The camera_type field distinguishes between perspective, equirectangular (360°), and other modalities, enabling platform-specific handling. By leveraging PostGIS, landlensdb supports efficient spatial-temporal queries across millions of records via SQL or Python APIs, with export to any OGR-supported format. Interactive visualization tools for Jupyter notebooks facilitate exploratory analysis and data validation. Built entirely on open-source technologies including Python, PostgreSQL, PostGIS, and GeoPandas, landlensdb aligns with the FOSS4G philosophy and addresses the growing need for scalable, open systems for proximity sensing image management. false https://talks.osgeo.org/foss4g-2026/talk/M9FP9L/ Conference Management Room1 General session talk 2026-09-01T16:00:00+09:00 16:00 00:30 Introducing a deep learning-based rooftop solar model using open remote sensing data and OpenStreetMap. We will explain the design of the model and demonstrate its use in the Climate Action Navigator. foss4g-2026-5080-detecting-rooftop-solar-panels-with-deep-learning-using-open-remote-sensing-data-and-openstreetmap DanielleGefei Kong en How dependent is your town on the electricity grid? How many buildings are supplied by rooftop solar panels? How much unused potential is there to leverage the power of the sun? We built a deep learning model using FOSS4G and open remote sensing data for Germany. With this model, you can detect which buildings have rooftop solar panels at a neighbourhood level. The input data is orthophotos and OpenStreetMap building footprints, which we feed into a 4-channel image classification model. The results of the model are visualised in the Rooftop Solar assessment tool of the Climate Action Navigator (https://climate-action.heigit.org) from HeiGIT (https://heigit.org). In this talk, we will demonstrate our results through our assessment tool. We will also explain the design of our model and how we used OpenStreetMap tagging to significantly speed up the creation of training data for our supervised learning approach. false https://talks.osgeo.org/foss4g-2026/talk/T9MXUP/ Conference Management Room1 General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 We'll discuss recent advancements in fAIr. Specifically, we'll explore our approach to enabling developers to share their GeoAI models for humanitarian mapping purposes. We'll also dive into the development of our MLOps architecture, which leverages STAC, MLM, Zenml, and Kubernetes to run models independently in the cloud. foss4g-2026-5249-sharing-your-geospatial-models-mlops-architecture-in-fair Kshitij Raj SharmaOmran NAJJARSam Woodcock en fAIr is a humanitarian Geo-AI platform by HOT that enables feature extraction from very high resolution aerial imagery (buildings, roads, trees) for OpenStreetMap mapping. This talk provides information about the ML infrastructure layer & recent developments in the platform . We will cover the STAC collections avilable , advantages of using standards while describing the model and integrating it to k8s false https://talks.osgeo.org/foss4g-2026/talk/EJSQEP/ Conference Management Room1 General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 pygeoapi project status presentation. Come and find out the latest news on the project as well as future plans, and how to get involved! foss4g-2026-4802-pygeoapi-project-status /media/foss4g-2026/submissions/8WT3LU/pygeoapi-logo_tMsraP3.png Tom KralidisAngelos TzotsosJoana Simoes en pygeoapi is an OGC API Reference Implementation. Implemented in Python, pygeoapi supports numerous OGC APIs via a core agnostic API, different web frameworks (Flask, Starlette, Django) and a fully integrated OpenAPI capability. Lightweight, easy to deploy and cloud-ready, pygeoapi's architecture facilitates publishing datasets and processes from multiple sources. The project also provides an extensible plugin framework, enabling developers to implement custom data adapters, filters and processes to meet their specific requirements and workflows. pygeoapi also supports the STAC specification in support of static data publishing. pygeoapi has a significant install base around the world, with numerous projects in academia, government and industry deployments. The project is also an OGC API Reference Implementation, lowering the barrier to publishing geospatial data for all users. This presentation will provide an update on the current status, latest developments in the project, including new core features and plugins. In addition, the presentation will highlight key projects using pygeoapi for geospatial data discovery, access and visualization. false https://talks.osgeo.org/foss4g-2026/talk/8WT3LU/ Conference Management Room1 General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 ES|QL is a powerful new declarative query language for Elasticsearch, opening the door to PostGIS-like ease of use for Geospatial querying and analytics. foss4g-2026-5577-geospatial-es-ql-in-elasticsearch /media/foss4g-2026/submissions/PUJ7PM/geohex_6hPXPVD.jpg Craig Taverner en Elasticsearch has long offered powerful geospatial capabilities, yet it remains underutilised by the open-source GIS community. Two major developments in the past couple of years are changing this. First, Elasticsearch returned to an approved open-source license, reigniting interest among developers. More significantly, the introduction of ES|QL, a declarative query language, has paved the way for OGC-like geospatial functions. This shift makes Elasticsearch feel more familiar to users of tools like PostGIS. In this talk, we’ll explore the current capabilities of Geospatial ES|QL, demonstrate real-world geospatial search and analytics, and provide a glimpse into future developments that will further enhance Elasticsearch as a geospatial database. false https://talks.osgeo.org/foss4g-2026/talk/PUJ7PM/ Conference Management Room2 General session talk 2026-09-01T13:00:00+09:00 13:00 00:30 For years, browser-based raster visualization has depended on backend services to preprocess, reproject, and tile imagery. We built something different: a way to stream unmodified [COG](https://cogeo.org/) data directly from object storage, reprojecting the imagery _in the browser_ — without a server in the middle. foss4g-2026-5217-browser-based-raster-reprojection-with-gpu-accelerated-pixel-resampling Kyle Barron en Visualizing analytic raster data in the browser still typically depends on backend services that render pre-computed raster tiles (e.g. PNG/JPEG) and frontend layers that are reliant on those services. That approach works — but it adds infrastructure, security overhead, and limits how people can change rendering on demand. With our work on [deck.gl-raster](https://github.com/developmentseed/deck.gl-raster), we’re bringing high-performance, extensible raster rendering to the [deck.gl](https://deck.gl/) geospatial visualization library. This adds to our [existing work](https://github.com/geoarrow/deck.gl-layers) speeding up visualization of large geospatial vector data — with the goal of making it realistic to render both massive vectors and analytic rasters in the same interactive view. This talk dives into the technical bits of why client-side raster visualization is challenging and explains our implementation. In the process, we’ll learn about raster reprojection, how GPUs render image data, and how our reprojection implementation leverages mesh-based rendering for efficient and accurate reprojection. This talk will be decently technical, but endeavors to be accessible to anyone familiar with raster data like Cloud-Optimized GeoTIFFs. false https://talks.osgeo.org/foss4g-2026/talk/7WESPL/ Conference Management Room2 General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 Exploring Japan’s geospatial technology, history, use cases, and challenges through Japan Geospatial Times, an open-source blog documenting geospatial applications in Japan. foss4g-2026-5212-japan-geospatial-times-exploring-the-history-and-use-cases-of-geospatial-technologies-in-japan /media/foss4g-2026/submissions/XSVFVL/og-image_nQp31Lu.jpg Eita Horishita en Japan Geospatial Times is an open-source blog project that documents the technology, history, use cases, and challenges of geospatial applications in Japan. The development of geospatial technologies in Japan has been shaped by several factors, including strong demand for disaster risk reduction from hazards such as earthquakes, government-led development of national spatial data infrastructure (NSDI), and citizen-led activities around open geospatial data. However, the historical background and practical experiences behind these developments are not widely shared internationally. This talk introduces Japan Geospatial Times, an open-source blog project, and explores the characteristics and historical development of geospatial technologies in Japan. Drawing on articles published on the site, the presentation highlights representative use cases and technical challenges, including major GIS services, open data initiatives, satellite positioning systems, and urban digital twins. Finally, the talk presents Japan Geospatial Times as an open-source knowledge-sharing platform and encourages geospatial communities in other countries to document and share their own geospatial histories and practices. false https://talks.osgeo.org/foss4g-2026/talk/XSVFVL/ Conference Management Room2 General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 Awesome Spectral Indices (ASI) is an open, community-driven catalogue designed to make spectral indices easy to discover, document, and compute. Five years after its first release, ASI supports 260+ indices across multiple programming languages and platforms. This talk reviews its evolution, current state, and future directions. foss4g-2026-4826-from-ndvi-to-260-indices-five-years-of-awesome-spectral-indices David Montero Loaiza en Everything started around ten years ago with a simple question: *what other indices are out there besides NDVI?* The answer was: *a lot!* While the number of indices was large, access to them was fragmented, documentation was inconsistent, and programmatic use was rarely supported. Existing catalogues of spectral indices were often closed, outdated, poorly referenced, or not designed to be used programmatically. Five years ago, **Awesome Spectral Indices** (ASI) set out to change that by creating an open, comprehensive, and community-driven catalogue of spectral indices, built around a simple idea: indices should be easy to discover, clearly documented, properly referenced, and directly executable in scientific workflows. The first public release in 2021 (v0.0.1) included 66 spectral indices grouped into seven categories. Each index followed a clear standard, with attributes such as name, acronym, formula, application domain, date of addition, and bibliographic reference. A key design choice was the introduction of band standards aligned with commonly used satellite platforms (e.g. Landsat, Sentinel, MODIS), allowing indices to be defined using simple expressions like “`(N - R) / (N + R)`”. This made the catalogue not just readable, but executable. ASI was released together with open-source APIs to make this standard usable in practice: *spyndex* for Python and *spectral* for the Google Earth Engine Code Editor. Community uptake was immediate, and the project quickly grew beyond its initial scope. Today, ASI includes more than 260 spectral indices (v0.9.0), has over 1k GitHub stars, and more than 200k downloads across PyPI and conda-forge. The ecosystem has expanded with an official Julia API (*SpectralIndices.jl*) and community-driven implementations, including an R package (*rsi*), adoption by projects such as *openEO* and *EOReader*, and alignment with the electro-optical STAC extension. This talk will present the current state of *Awesome Spectral Indices*, reflect on key technical and community lessons learned over the past five years, and outline what comes next. Upcoming developments include improvements to the band standard to support additional sensors and indices, richer metadata for each index, expanded categorization, and API updates focused on interoperability and ease of use. false https://talks.osgeo.org/foss4g-2026/talk/FEEQDQ/ Conference Management Room2 General session talk 2026-09-01T14:30:00+09:00 14:30 00:30 MapLibre GL’s efficient "inline-shading" is ideal for standard maps. However, our Navara system’s dynamic day-night cycle, featuring constant light motion, exceeded standard coupled architectures. This necessitated a more flexible pipeline, leading us to implement a decoupled terrain normal generation approach for real-time, high-performance rendering. foss4g-2026-5128-hillshading-for-real-time-lighting hao en The motivation for this technical restructuring came from a core feature in our Navara system: the need for seamless, real-time day-night transitions. In this session, we will share the journey of moving away from traditional "inline-shading" (where pixel colors are computed in a single pass) to a deferred approach. The core of the talk covers: - GPU-based Normal Generation: How we adapted the Navara engine to extract high-precision surface normals from raw DEM data on the fly. - Unified Scene Integration: How these persistent normals serve a dual purpose—not only powering dynamic hillshading but also providing the geometric foundation for G-buffer pipelines and improving the precision of vector draping (polygons/polylines) on rugged terrain. false https://talks.osgeo.org/foss4g-2026/talk/GRW787/ Conference Management Room2 General session talk 2026-09-01T15:30:00+09:00 15:30 00:30 We're creating a new ecosystem for client-side raster data visualization in Python & the browser, enabling interactive WebGL rendering of [COG](https://cogeo.org/) and [Zarr](https://zarr.dev/) data. This talk presents a high-level overview of how this works and how to leverage it in your projects. foss4g-2026-5220-fast-interactive-customizable-raster-data-visualization-in-python-the-browser-with-deck-gl-raster Kyle Barron en Visualizing analytic raster data in the browser still typically depends on backend services that render pre-computed raster tiles (e.g. PNG/JPEG). Aside from being inaccessible — it requires organizations to host and maintain a service — it slows down the iteration cycle for visualization. Every visualization change requires a server round trip to fetch new data. With this new WebGL-based raster data visualization stack, changing visualization parameters is instant. We can seamlessly animate over time in Zarr data at 60 frames per second, even while applying custom band math. This browser-based data visualization for COG and Zarr is exposed to Python through Lonboard, the fastest and most capable interactive data visualization tool for Python. This talk will show how to use Lonboard for scalable raster data visualization, and discuss how new tools like [async-geotiff](https://github.com/developmentseed/async-geotiff) enable Python to proxy data to the browser. This allows for seamless, reliable visualization: _any_ COG or Zarr you can access in Python can be visualized. No need to set up a separate tile server. This talk will give a high-level overview of how these libraries work, and how you can use them in your own projects. false https://talks.osgeo.org/foss4g-2026/talk/9T3BJP/ Conference Management Room2 General session talk 2026-09-01T16:00:00+09:00 16:00 00:30 Terra Draw is a drawing library having unified interfaces for most of mapping libraries. This talk updates the state of TerraDraw and maplibre-gl-terradraw this year. foss4g-2026-4850-terra-draw-bring-drawing-feature-to-all-map-applications Jin Igarashi en Drawing on web map application can be a fundamental and critical feature, however bringing a drawing feature to a map is surprisingly complex, especially handling diverse different mapping libraries. Terra Draw was created by [James Milner](https://github.com/JamesLMilner) four years ago to bring simplified and standardized drawing functionality across major mapping libraries such as Leaflet, OpenLayers, Google Maps, Mapbox GL JS, and Maplibre GL JS. This talk will introduce the major functionalities of Tarra Draw and update the latest development since last year's FOSS4G. This talk will give you some insight how you can develop drawing features (basic drawing mode like point, line and polygon, as well as advanced functionalities such as snapping, rotation, etc) working across different mapping platforms easily. Furthermore, the speaker is also maintaining a maplibre plugin for TerraDraw library - maplibre-gl-terradraw. This talk will also introduce how you can bring advanced drawing functionality easily to your maplibre application. false https://talks.osgeo.org/foss4g-2026/talk/SHKRZZ/ Conference Management Room2 General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 Most of today's web maps are using the Web Mercator projection, which has a major distortion of area sizes far from the equator. This talk shows recent improvements in web mapping libraries for using the Equal Earth map projection in interactive web maps and discusses remaining obstacles. foss4g-2026-5200-interactive-web-mapping-with-equal-earth-projection Pirmin Kalberer en Most of today's web maps are using the [Web Mercator projection](https://en.wikipedia.org/wiki/Web_Mercator_projection). A major issue of Web Mercator is the distortion of area sizes far from the equator. In 2018 Bojan Šavrič, Tom Patterson and Bernhard Jenny published their work on the [Equal Earth map projection](https://www.equal-earth.com/), an equal-area projection for world maps. There is good support for it in many mapping libraries and tools and cartographers use this projection regularly for world maps. Equal Earth has the disadvantage, that it is not well suited for large-scale maps. So zoomable web maps have to adapt the projection according to the current view scale. Most of them do this already by switching between a globe projection and Mercator. But for thematic maps a globe is a bad solution since you only see half of the world at once. OpenLayers has recently added support for reprojection of vector tiles which allows using a dynamic projection dependening on the current view. This talk shows how to use the Equal Earth map projection for web mapping with different kind of data sources. It shows what can be already done with current technoolgy and discusses remaining obstacles. false https://talks.osgeo.org/foss4g-2026/talk/UETF3C/ Conference Management Room2 General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 This presentation explores how Large Language Models (LLMs) and Function Calling can transform web map experiences. Built with the open-source MapLibre GL JS, our application enables users to query geospatial data through natural language — designing smarter experiences that replace complex layer toggling with intelligent, AI-driven interactions. foss4g-2026-5385-designing-web-map-experiences-beyond-too-much-data-and-too-little-time-with-intelligent-ai-processing Mayurachat Saechan en Currently, web map systems are widely used for searching, analyzing, and displaying geospatial data with complex layers. Displaying analytical results and data often involves complicated steps, such as selecting and viewing data by date/time period, toggling layers on/off, and managing numerous data layers processes that can be time-consuming to access and create a poor map user experience. With the rapid advancement of Artificial Intelligence (AI) — particularly Large Language Models (LLMs) combined with Function Calling techniques that allow AI to directly execute system commands — we can now apply these technologies to geospatial data processing. AI interprets user needs through natural language, analyzes data within the system, and displays results on the map quickly and intelligently — reducing operational complexity and elevating the overall user experience. This presentation introduces a web application developed using the open-source MapLibre GL JS map library for user experience (UX) design, combined with AI to assist in processing and displaying geospatial data rapidly. The interface design focuses on simplicity and ease of use, enabling users to access the data they need quickly. The outcome of this project aims to explore a new approach to web mapping that integrates AI with UX design to simplify workflows, improve access to analysis, and display geospatial data faster and more efficiently. The project will be released as open source on GitHub including source code and documentation so that the FOSS4G community and developers worldwide can study, build upon, and contribute to it openly. false https://talks.osgeo.org/foss4g-2026/talk/TRUMLY/ Conference Management Room2 General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 The ZOO-Project is an open-source software platform that implements the OGC API Processes standard, providing a comprehensive solution for creating, deploying, and managing web processing services tailored to geospatial and Earth Observation (EO) applications. foss4g-2026-5006-zoo-project-news-about-the-open-source-generic-processing-engine Gérald Fenoy en The project began in 2008 with the implementation of the Web Processing Service (WPS), a standard defined by the Open Geospatial Consortium (OGC) that enables the exposure of geospatial processes through standardized web interfaces. This initial step established the ZOO-Project as a key tool for the geospatial community, facilitating the integration of geospatial workflows into web-based environments. As a core component of the European Space Agency’s EO Exploitation Platform Common Architecture (EOEPCA), the ZOO-Project plays a pivotal role in enabling interoperable workflows that can operate seamlessly across diverse platforms. By adhering to the OGC API Processes standard, the ZOO-Project ensures that workflows are compatible with a wide range of systems, facilitating integration into various applications without reliance on specific infrastructures or interfaces. The ZOO-Project’s support for the Common Workflow Language (CWL) further enhances its utility, enabling workflows to be described in a standardized format that ensures portability and scalability. This capability is particularly valuable in the context of EOEPCA, which promotes the sharing of data and services while advocating for the use of open standards. The ZOO-Project is actively sponsored by EOEPCA, transforming its vision into practical tools for researchers, developers, and platform operators. This presentation will explore the current state of the ZOO-Project, highlighting its ongoing evolution, including new features that enable the execution of workflows in containerized environments, such as high-performance computing (HPC) systems and Kubernetes, and the use of distributed platforms like Argo Workflows. These advancements ensure that the ZOO-Project remains at the forefront of addressing modern EO challenges, offering scalable and interoperable solutions for a broad spectrum of users. false https://talks.osgeo.org/foss4g-2026/talk/YRWWX7/ Conference Management Room3 General session talk 2026-09-01T13:00:00+09:00 13:00 00:30 The presentation describes processes and opensource tools employed by the author and his team to build and consume digital models for urban environments. Attendees will be presented with an overview of our work related to 3D data visualizations and a selection of use cases implemented for the MapStore WebGIS framework foss4g-2026-5419-explore-open-source-tools-to-create-digital-urban-models-for-mapstore Lorenzo NataliTobia Di PisaStefano Bovio en The presentation describes processes and open-source tools employed by the author and his team to build and consume digital models for urban environments. The results of these processes will be rendered in MapStore as 3D Tiles layers, an OGC community standard designed for streaming and rendering massive 3D geospatial content. MapStore WebGIS framework support for 3D Tiles and glTF models through the Cesium mapping library has been greatly enhanced to support a more powerful integration. The latest versions of MapStore also include improvements and tools for exploring 3D data such as Map Views, Styling, 3D Measurements, Annotations and more. Attendees will be presented with an overview of our work related to 3D data processing and visualization, and a selected city will be used to exemplify the processes. At the end of the presentation, attendees will be able to use the presented processes, tools and workflows to replicate them in different urban scenarios, finally visualizing them with the 3D tools of the MapStore WebGIS application. false https://talks.osgeo.org/foss4g-2026/talk/RQJBQ9/ Conference Management Room3 General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 The geospatial ecosystem has evolved, introducing formats optimized for rendering, cloud-native access, and large-scale analytics. This talk provides a structured overview of formats including MVT, MLT, 3D Tiles, PMTiles, FlatGeobuf, and GeoParquet, explaining their core concepts, trade-offs, and how to combine them in modern geospatial workflows. foss4g-2026-5581-beyond-shapefile-a-taxonomy-of-modern-geospatial-data-formats Markus Tremmel en Shapefile has long been the industry standard, but the geospatial ecosystem has evolved rapidly. A new generation of data formats has emerged, each optimized for specific workloads such as visualization, cloud-native distribution, or large-scale analytics. This talk provides a structured introduction to the modern geospatial format landscape. We cover rendering-optimized formats like MVT, MLT, and 3D Tiles, cloud-native containers such as PMTiles, and analytics-oriented formats including FlatGeobuf, GeoParquet, and GeoArrow. For each format family we explain the core concepts, design trade-offs, and typical use cases. We also show how these formats complement each other and can be combined in real-world workflows — from data processing pipelines to interactive web map applications. Whether you are building maps, processing large datasets, or designing cloud-native geospatial systems, this talk gives you a clear mental model of the modern format landscape and practical guidance for choosing the right tool for the job. false https://talks.osgeo.org/foss4g-2026/talk/SZA7P8/ Conference Management Room3 General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 Beyond LiDAR and automated sensors, human photography captures the "why" of a place. This talk draws inspiration from Panoramio and Flickr, questioning whether human-captured imagery remains a vital layer for modern mapping and exploring what this looks like today. foss4g-2026-5512-the-human-lens-in-a-machine-mapped-world /media/foss4g-2026/submissions/7GAD89/PXL_20260123_062552551_rgLndxb.jpg Edoardo NeerhutChristopher Beddow en What is the role of photos in 2026 and beyond? Why does a medium that has existed since the 1800s still hold relevance today? In a world where attention is gravitating towards video, and short-form content in particular, do photos still have a *place*? This project firmly believes they do, and that "place" is the operative word. The photos we advocate for represent a conscious framing by a human to record a particular location at a specific moment in time. This is a deliberate departure from the era of "total mapping," where every street is scanned by LiDAR and every meter of Earth is monitored by low-earth orbit satellites. While these myriad imagery sources, from self-driving cars to robots, are incredibly valuable for building the geometry of our world, they lack a qualitative nuance. Automated data extraction captures the what, but human photography captures the why. What does a human see in this world and, more importantly, what do they choose to memorialize? A robot classifies a street corner as a set of coordinates and obstacles; a human captures the vibe of a neighborhood, the social significance of a mural, or the lived reality of a public square. What if the human angle remains a critical layer of the modern map, providing context that sensors alone cannot replicate? **The Evolution of Place-Based Photography** Historically, platforms like Flickr and Panoramio emerged to serve these distinct needs. One championing the creative endeavor of the photographer, the other anchoring images to the physical world. However, as the broader digital landscape shifted toward the attention economy, these missions drifted. Flickr, while remaining an essential repository for photographers, has often struggled with shifting corporate priorities, at times feeling more like a quiet archive than a primary engine for discovery. Panoramio, which pre-dated the smartphone era, was eventually sunsetted and absorbed into much larger, utility-driven mapping ecosystems. While those global platforms are impressive feats of engineering, they often treat photography as secondary metadata, a utility for navigation or business verification rather than a window into the lived experience of a place. Pure photography, the art of seeing and sharing a location for its own sake, has been overshadowed by the "search and go" nature of modern apps. Does place-centric photography have a role to play in modern mapping or will it become an nostalgic footnote? false https://talks.osgeo.org/foss4g-2026/talk/7GAD89/ Conference Management Room3 General session talk 2026-09-01T14:30:00+09:00 14:30 00:30 Open data projects like OpenStreetMap and OpenAddresses have become some of the best data sources in the world. But translating from data to real-time guidance remains challenge. Learn how we're tackling this with Ferrostar, a cross-platform FOSS navigation SDK. foss4g-2026-5618-ferrostar-tackling-navigation-challenges-through-collaboration Ian Wagner en FOSS has spawned world-class solutions in domains from programming languages to operating systems. Even in the geospatial domain, we have world-class projects like MapLibre and Valhalla. But real-time navigation guidance remains a challenge. In this talk, I'll discuss the journey over the past few years leading the development of Ferrostar. We'll cover the technical challenges ranging from software to real-world constraints on GPS. From the human and organizational challenges of collaboration to under-served use cases beyond cars. And the main thing these all have in common? Collaboration can help create better outcomes for everyone. This talk will stay mostly high-level, but I won't shy away from a few technical topics throughout the talk. false https://talks.osgeo.org/foss4g-2026/talk/V3BYZ7/ Conference Management Room3 General session talk 2026-09-01T15:30:00+09:00 15:30 00:30 Although an alpha version has been available on the Leaflet website for over a year, it remained unclear for a long time what we could actually expect. Now there is more information -let's take a close look at what is changing with Leaflet 2.0. foss4g-2026-5637-leaflet-2-0-is-coming-it-s-official Numa Gremling en Although an alpha version has been available on the Leaflet website for over a year, it remained unclear for a long time what we could actually expect. A bit of a mystery! Now there is more information - and in this talk, we’ll take a close look at what is changing with Leaflet 2.0. Can I continue using my existing applications, or do I need to adapt them? What new features and tools are there? What is being removed? And will Leaflet remain as simple as before—or is everything about to become more complicated? Let's find out! false https://talks.osgeo.org/foss4g-2026/talk/VK8SSV/ Conference Management Room3 General session talk 2026-09-01T16:00:00+09:00 16:00 00:30 Apache Airflow manages tasks in a data pipeline from data ingestion and preprocessing to storage in ZARR format for multidimensional satellite imagery. ZARR supports efficient management of large-scale datasets and parallel processing, while Airflow automates and monitors workflow tasks. foss4g-2026-5325-end-to-end-satellite-data-pipeline-airflow-orchestration-zarr-storage-and-landtrendr-analysis Tanaporn Songprayad en At present, satellite imagery data is widely used to monitor and analyze spatial changes in various domains, such as land use change detection, natural resource monitoring, and environmental surveillance. However, satellite data typically exists as time-series data with large volumes and multidimensional structures. As a result, the storage, processing, and management of such data remain significant challenges in terms of data infrastructure and geospatial data processing. Apache Airflow is used as a tool to control and manage the sequence of tasks in the data processing workflow (workflow orchestration), covering stages from data ingestion, data preprocessing, to data storage in Zarr (ZARR) format, which is a storage format suitable for satellite imagery and multidimensional geospatial data. The use of the Zarr storage format improves data accessibility, facilitates the management of large-scale datasets, and supports parallel data processing efficiently. In addition, Airflow enables automated workflow management, allowing monitoring of processing status and systematic control of task dependencies within the data pipeline. This makes it well suited for managing large-scale satellite datasets that require multi-stage processing. Satellite imagery data stored in Zarr format can further be used as input for analyzing forest change using the LandTrendr method. LandTrendr is a time-series analysis technique designed to detect trends and long-term changes in land cover using satellite imagery data. Since satellite datasets are typically large and multidimensional, the use of Airflow for managing the data pipeline ensures that data preparation and storage processes are well organized. This enables the data to be efficiently utilized for LandTrendr analysis. Overall, the implementation of a data pipeline using Apache Airflow for satellite data management—from data ingestion and processing to storage in Zarr format—enhances the efficiency of handling large-scale satellite datasets. It also enables automated data processing workflows with systematic monitoring capabilities, while supporting the application of the data for forest change analysis using the LandTrendr method. false https://talks.osgeo.org/foss4g-2026/talk/WD3DHU/ Conference Management Room3 General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 First, an history of CRS at OGC: how WKT and abstract model evolved in parallel, what GML can do, why it nevertheless became legacy encoding. Then a look ahead: how OGC handles JSON encoding in future standards, implications for a CRS JSON, and revision of ISO 19111 abstract model. foss4g-2026-5480-ogc-iso-standards-for-crs-a-look-back-and-a-look-ahead Martin Desruisseaux en Our capability to exchange geospatial data depends critically on our capability to describe Coordinate Reference Systems (CRS) in a way which is both implementation-neutral and sufficiently detailed for setting up the mathematical formulas to use. The earliest attempt by OGC was a Well Known Text (WKT) format defined 27 years ago, which came with ambiguities that are still passed to new data formats even today. Clarifications were attempted in 2001, then 2015 with WKT 2, but couldn’t remove all sources of confusion. In parallel with the WKT encoding, a richer conceptual model was defined using the Unified Modelling Language (UML). Because the WKT and the conceptual model started from different roots, they do not match even if they overlap. WKT 2 tried to conciliate the two worlds, but was constrained by compatibility and brevity goals, resulting in remaining mismatches that still confuse users. While the conceptual model allows the definition of three- and four-dimensional CRS, including CRS attached to vehicle, drone, pipeline or satellite in space, unequal software support and ambiguities in popular data formats are still impediments to the interoperability of data that are associated to other CRSs than the common two-dimensional geographic and projected CRSs. The first part in this talk will look back to the history of CRS at OGC: how WKT and the abstract model evolved in parallel, the principles behind the Geography Markup Language (GML), what GML can do that WKT cannot do, why GML nevertheless became a legacy encoding standard, the OGC Testbed that explored how these standards can be used in space with the NASA Double Asteroid Redirection Test (DART) as an experiment, and how that Testbed has show the void left by the absence of replacement for GML. The second part of this talk will look ahead: first, the popularity of JSON encoding has created a demand for guidance in order to reduce the heterogeneity observed between OGC standards. It resulted in OGC’s “UML to JSON Encoding Rules” best practice paper. Together with the “JSON schema implementation of metadata fundamentals” (ISO 19115-4), they provide a way to define a CRS JSON encoding which can replace GML and be consistent with the JSON encoding of related OGC/ISO standards. A public CRS JSON draft will be presented, keeping in mind that there is no final decision by OGC members yet about whether to accept this draft. Then, an implementation on top of GeoAPI interfaces will be demonstrated. The same implementation is executable with the PROJ library (through PROJ-JNI), Apache SIS, GeoTools and PROJ4J, which brings CRS JSON support to those four libraries when used from the Java language. A second look ahead is about modifications of the conceptual model for addressing the needs of national agencies who produce CRS definitions, and for incorporating some proposals from the Testbed about CRS in space. The concepts that may be revised include datum ensembles, datum epochs, a simplification attempt of the way that “derived datum” are defined, and more. false https://talks.osgeo.org/foss4g-2026/talk/XJFH9N/ Conference Management Room3 General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 A development update from the latest version of GeoNetwork, as a 20-year-old project prepares for the challenges of the next decade! foss4g-2026-5147-geonetwork-5-reimagining-the-spatial-data-catalog-for-the-future Antonio CercielloJody Garnett en For over 20 years, the GeoNetwork opensource project has been a proud pillar of the FOSS4G community and a mature catalog application for discovering resources across any Spatial Data Infrastructure (SDI). As the geospatial ecosystem evolves toward cloud-native architectures and modern standards, our tools must evolve in harmony with it. The GeoNetwork team warmly invites you to see what we have been building over the past twelve months. Alongside showcasing our recent community-driven features, this session will serve as the official introduction to GeoNetwork 5, a ground-up rewrite of the platform's codebase. Driven by the need to transition away from old libraries, GeoNetwork 5 represents a major generational leap. It has been re-engineered using Java 21 and Spring Boot to support modern web technologies, microservices, and cloud deployments. Join us to discover how these architectural improvements ensure GeoNetwork remains the premier open-source metadata integration hub for the next decade, and learn how you can participate in the version 5 journey. false https://talks.osgeo.org/foss4g-2026/talk/ZCSEMV/ Conference Management Room3 General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 Pacific Ocean Portal 2.0 is an open-source platform enabling seamless access to ocean data, advanced GIS-based data management, and real-time visualization. It supports collaboration, interoperability, and decision-making, strengthening ocean services and climate resilience across Pacific Island countries. foss4g-2026-5542-pacific-ocean-portal-2-0 Divesh Anuj en The Pacific Ocean Portal 2.0 is a transformative digital platform advancing ocean science and ocean data management across the Pacific region. It serves as a centralized hub for the integration, dissemination, and visualization of oceanographic information, supporting evidence-based decision-making for National Meteorological and Hydrological Services (NMHSs) and key sectors including tourism, fisheries, coastal monitoring, sea level analysis, and coral reef management. The portal provides seamless access to a comprehensive suite of datasets, including forecasts, near real-time and historical data, and in situ observations. At its core is a robust geospatial data infrastructure that ensures efficient ocean data management, interoperability, and high-quality data integration for advanced visualization and analysis. This is enabled through the adoption of open standards and technologies, including pygeoapi for OGC-compliant data access and metadata management, which supports the discovery, indexing, and sharing of ocean datasets. Built on a modular, open-source architecture—leveraging THREDDS, GeoServer, FastAPI, and a Next.js frontend—the platform empowers developers and partner countries to design and deploy tailored, country-specific tools and applications. Recent enhancements include time series extraction from NetCDF datasets, dynamic on-the-fly map generation, near real-time in situ monitoring capabilities, an integrated resource library, and a directory of regional ocean experts. The platform also incorporates secure data governance mechanisms, enabling controlled access to restricted datasets such as high-resolution wave and coastal inundation forecasts for designated users. As part of its expanding ocean data management capabilities, the portal integrates a dedicated data management system powered by pygeoapi, which has successfully indexed LiDAR datasets and is being progressively deployed across Pacific Island countries to strengthen national data stewardship and regional collaboration. false https://talks.osgeo.org/foss4g-2026/talk/UKVGAU/ Conference Management Room4 General session talk 2026-09-01T13:00:00+09:00 13:00 00:30 This is a personal account of a two-month sprint to attempt to rebuild our corporate GIS system in the Brazilian Federal Police using modern tools, solo. I will touch on tools, efficiency, burnout, and the golden pot at the end of the rainbow. foss4g-2026-5526-three-years-in-two-months-lessons-learned-in-re-building-a-120kloc-gis-system-from-scratch-as-a-10x-engineer Daniel Araújo Miranda en This is a personal account of a two-month sprint to attempt to rebuild our corporate GIS system in the Brazilian Federal Police using modern tools, solo. I will touch on tools, efficiency, burnout, and the golden pot at the end of the rainbow. For context, this is a March 2026 snapshot of a reality that is changing blazingly fast. Some of it may not make sense in August/September, when FOSS4G Hiroshima takes place. Therefore, I have selected topics that I see as more likely to be long term lessons than point-in-time hacks. The problem: - A geonode fork for which we aspired to hire an italian company to maintain. - The inability to find a viable legal path in Brazil to do it. - An essential piece of infrastructure with large development overhead. - An overwhelmed team unable to submit improvements to upstream. A new type of solution just became possible, since current dev tools are nothing short of sorcery. General principles: - Saying what you mean - Extracting domain knowledge from the P.O. - Extracting general knowledge from the A.I. - Guidelines, Guardrails and Railroading - do's and don'ts - Testing with "eyes" - Finally hitting "play" (setting up a dev loop) - Human workload management As a result, a viable replacement for our current system was prototyped with most of the core features working. There are gaps, but also ther are features we have not been able to implement for years (quotas, observability, multi-tenancy, sophisticated audit logging, ISO-19115/MGB compliance, low latency dev cycle for UI, automated testing for frontend AND backend, API keys ...). The current prototype is a single repository with under 80 thousand lines of functional code aiming to replace a 120 thousand lines codebase. There is already a full feature (1.5kloc) waiting for refactoring before submitting to pycsw upstream, with more upstream contributions to other projects coming. Whether the switch itself will prove to be viable is still up in the air... but you will get the chance to know in FOSS4G Hiroshima! **This abstract is entirely human-written using a simple text editor. No review, no prior or posterior opinion or any input from I.A., not even auto-correct** false https://talks.osgeo.org/foss4g-2026/talk/VP8HKH/ Conference Management Room4 General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 PINOGIO connects gPocket for field data collection with a web platform for project management, map production, and story-map publishing. This talk shows how open-source technologies, offline workflows, and external GIS interoperability were combined into a practical GIS workflow that non-developers could use. foss4g-2026-5120-from-field-data-collection-to-web-based-analysis-and-sharing-building-the-pinogio-geospatial-platform-with-open-source-technologies Sohee Kang en 1. Problem This Talk Addresses In many organizations, field collection, photo records, route tracking, map editing, spatial analysis, and publishing are handled in separate tools. Field teams capture location, attributes, photos, and movement paths, while office teams later review, interpret, map, and share the same data again. When that process is split across paper notes, messenger attachments, spreadsheets, standalone GIS tools, and web reports, the same information gets re-entered, updates become slow, and the field context is easily lost. The gap becomes even more visible in unstable network conditions. This talk explains how we reduced that gap by connecting a mobile app, a web workspace, and a platform backend into one practical workflow. 2. What We Built or Applied - We built `gPocket` as a field mobile GIS app for collecting and editing spatial data, with encrypted offline storage, project-based working data, GeoPackage working copies that can be modified in the field and reflected back to the server, and tracking records that can be exported as files and reused in external GIS tools such as `QGIS`. - We use `PINOGIO platform` to describe one connected workflow rather than two separate products: `pinogio` provides the platform core for projects, datasets, layers, maps, geocoding, analysis, and sharing, while `pinogio-studio` provides the browser-based workspace where people actually organize data, style layers, build maps, and publish outputs. - We designed `pinogio maps` not as a simple map viewer, but as a story-map style output where map layers, explanatory text, photos, and interpretive panels can be combined into something readable and shareable. - We connected all three through open standards and open-source GIS components so they behave like one operational ecosystem. 3. Outcomes and Lessons Learned - Mobile and web should not provide the exact same experience; each works better with a focused role. - OGC standards and GeoPackage made it possible to design online workflows, offline workflows, and file-based interoperability with external GIS tools together. - Open-source geospatial components can be combined into a product-grade platform. - The real challenge was not only the GIS engine, but designing a project-centered experience that non-developers could actually use. 4. What the Audience Will Gain - A practical architecture pattern for connecting field apps, web tools, and backend GIS services - A real-world example of turning open-source geospatial building blocks into an end-user product - Ideas for combining field collection, offline synchronization, web review, analysis, and publishing in one workflow - A community-oriented perspective on why open standards matter in product design false https://talks.osgeo.org/foss4g-2026/talk/Z7GTH9/ Conference Management Room4 General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 SedonaDB is an open-source single-node analytical database engine for GIS. But, with established options like PostGIS and DuckDB, why do we need yet another engine? This talk will provide an overview of the current FOSS4G database landscape and explain why SedonaDB is a name you need to know. foss4g-2026-5509-sedonadb-why-yet-another-geospatial-database-engine Hiroaki Yutani en SedonaDB is a powerful open-source, single-node analytical database engine specifically designed for modern GIS workloads. However, with industry titans like PostGIS and the rising popularity of DuckDB-Spatial, you might find yourself asking—or being told by your boss—"Don't we already have a geospatial database at home?" Why would we need yet another tool in an already crowded ecosystem? As of this writing, one of the most convincing answers is probably that SedonaDB can handle both raster and vector data without requiring a complex setup like PostGIS. But, honestly, it's hard to give a definitive answer because the technology is advancing so rapidly that today's advantage might disappear tomorrow. For example, SedonaDB was previously the only database among PostGIS and DuckDB to have a native GEOMETRY type. However, since DuckDB v1.5.0, the GEOMETRY type is now native. So, in this talk, rather than focusing on advertising SedonaDB based on current status alone, I will explore the recent progress of open-source databases with geospatial support, such as DuckDB, PostGIS, LanceDB, and SedonaDB. false https://talks.osgeo.org/foss4g-2026/talk/KPBRS7/ Conference Management Room4 General session talk 2026-09-01T14:30:00+09:00 14:30 00:30 high-performance, serverless spatial analytics framework using GeoArrow, DuckDB-Wasm, and Parquet. By shifting from row-based GeoJSON to a columnar architecture, the method achieves a 10x–50x performance boost, enabling the seamless browser-based visualization and processing of over ten million records without backend infrastructure. foss4g-2026-5294-breaking-the-browser-s-limit-high-performance-spatial-analytics-with-geoarrow-duckdb Phanukorn Kongphet en Current spatial analytics within web browsers face severe performance bottlenecks when handling large-scale datasets, primarily due to reliance on row-based formats like GeoJSON. These structures incur significant serialization overhead and excessive memory consumption. This paper proposes a transition toward a columnar architecture leveraging GeoArrow, a spatial extension of the Apache Arrow standard. By utilizing binary in-memory storage, GeoArrow enables "zero-copy" data transfer and optimizes memory locality—key factors in overcoming the hardware constraints of modern browser environments. This study demonstrates the seamless processing and visualization of over ten million spatial records directly within the browser. By querying Parquet files hosted on Google Cloud Platform (GCP) via DuckDB-Wasm and GeoArrow, this approach achieves a 10 - 50x performance increase compared to traditional methods. It allows for the rendering of massive raw datasets while maintaining high interactivity and fluidity, all without the need for intermediary API servers or backend infrastructure. By eliminating architectural complexity and maximizing execution speed, this methodology establishes a robust foundation for the next generation of serverless, high-efficiency GIS web applications. false https://talks.osgeo.org/foss4g-2026/talk/T7TNGZ/ Conference Management Room4 General session talk 2026-09-01T15:30:00+09:00 15:30 00:30 Discover all QField new features and Explore how QField empowers people to map and understand the world—supporting daily tasks, global challenges, and the UN SDGs through open-source, intuitive, and collaborative mobile geospatial tools. foss4g-2026-5058-mapping-the-world-empowering-people-with-qfield Marco Bernasocchi en QField helps people map and understand the world—enabling them to solve everyday tasks and tackle global challenges. In this talk, we’ll dive into how QField is used in diverse contexts: from conservation and climate action to infrastructure planning and public service delivery. You’ll hear how the app’s open-source foundation, intuitive interface, and powerful features are making high-quality field data collection accessible to all. We’ll also showcase how QField supports the UN Sustainable Development Goals and fosters a global community of practitioners working for impact. Whether you're new to QField or a long-time contributor, this session will show how the vision of "mapping the world, empowering people" is being realized every day. false https://talks.osgeo.org/foss4g-2026/talk/ZV3TLY/ Conference Management Room4 General session talk 2026-09-01T16:00:00+09:00 16:00 00:30 **[City2Graph](https://github.com/c2g-dev/city2graph)** is an open-source Python library bridging GIS, network science, and geospatial artificial intelligence (GeoAI) with Graph Neural Networks (GNNs). It offers a unified pipeline to construct, analyse, and visualise graphs from diverse data sources, with conversion between GeoPandas, NetworkX, and PyTorch Geometric. foss4g-2026-5047-city2graph-open-source-python-library-for-geoai-with-graph-neural-networks /media/foss4g-2026/submissions/LV8FCQ/social_preview_city2graph_P38FGuG.png Yuta Sato en Urban systems are inherently complex, composed of interacting entities such as street networks, transit systems, and human mobility flows. GNNs have recently gained significant attention as a promising direction for GeoAI, providing a powerful framework to model these interactions. Yet their practical application is hindered by a fragmented software ecosystem. Researchers and spatial data scientists often face technical barriers when constructing reproducible graphs that represent multiple types of urban elements simultaneously, lacking a unified pipeline to convert geospatial objects into the tensor formats required for GNNs. This work presents City2Graph, an open-source Python library that bridges GIS, network science, and GeoAI. It standardises graph construction from diverse data sources and formats, including OpenStreetMap/Overture Maps data, GTFS schedules, and mobility flows as origin–destination matrices. The library facilitates seamless bidirectional conversion between GeoPandas, NetworkX, and PyTorch Geometric, preserving geometries for interpretation and visualisation. City2Graph also enables users to analyse complex connections across different network layers as 'metapaths' (e.g. multimodal accessibility between areas via streets and bus transit), together with utilities such as plotting network structures and generating multimodal isochrones. City2Graph is available under a BSD 3-Clause License, hosted on **[GitHub](https://github.com/c2g-dev/city2graph) (1,100 Stars, as of April 15th)**. Documents are hosted on **https://city2graph.net**. Any contrubution from FOSS4G community is always welcomed, such as supporting new data formats (e.g., GBFS, GTFS Realtime, PyTorch Geometric Temporal, etc.), graph database (e.g., Cypher-based solution like Neo4j), and expanding scheme (e.g., spatial knowledge graph, GraphRAG on LLM, etc.). false https://talks.osgeo.org/foss4g-2026/talk/LV8FCQ/ Conference Management Room4 General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 FOSS4G software often has an international user base.Therefore, many FOSS4G software programs include internationalization features when they are created. These internationalization features are used to localize the software for various languages. This presentation will discuss the history, current status, and challenges of Japanese localization. foss4g-2026-5210-current-status-and-challenges-of-foss4g-s-japanese-localization Yoichi Kayama en FOSS4G software often has an international user base. Therefore, many FOSS4G software programs include internationalization features during development. These features are used to localize the software into various languages. In Japan, information on FOSS4G software with extensive localization is widely available, but the availability of Japanese documentation for software that hasn't been localized is limited. QGIS has a high rate of Japanese translation in its documentation, making it widely used in Japan, and numerous Japanese explanatory books have been published. However, Japanese resources for other FOSS4G software are very scarce. In the past, when using Japanese in computer software, there were multiple Japanese character encoding systems, and complex methods for handling them existed. Furthermore, various problems arose when dealing with such Japanese codes during localization and document creation processes. With the advent of Unicode, a standard environment for handling Japanese on computers was established, and standard methods for software Japanese localization have also been established. This presentation will cover the history, current status, and challenges of localizing FOSS4G software and documentation into Japanese. false https://talks.osgeo.org/foss4g-2026/talk/RRJEBF/ Conference Management Room4 General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 Re:Earth is an open source, no-code WebGIS platform built on Cesium, developed by Eukarya Inc. This talk presents two educational case studies — high school students participating in urban planning and elementary school students creating a disaster risk map — showing how open source geospatial tools empower non-expert users. foss4g-2026-5103-re-earth-empowering-non-experts-with-open-source-webgis-case-studies-from-educational-settings Arisa Mikumo en Re:Earth consists of three tools: Visualizer (a no-code map authoring tool), CMS (a geospatial data management tool), and Flow (a data transformation tool). Built on Cesium and fully open source, Re:Earth is designed to make GIS accessible to users without technical expertise. --- **Use Case 1: High School Students Participating in Urban Planning** Many Japanese municipalities face population decline and seek to engage younger generations in local governance. In this project, high school students used Re:Earth Visualizer to design their vision of a future town and presented their proposals to the local mayor as policy recommendations. To lower the barrier to participation, a custom Visualizer plugin was developed, enabling students to simulate and visualize urban landscapes without account registration or technical setup. This highlights how Re:Earth's open plugin architecture can be tailored to specific community needs. --- **Use Case 2: Elementary School Students Creating a Disaster Risk Map** In a series of three workshops, elementary school students developed disaster preparedness awareness and hands-on experience with digital tools. Students conducted fieldwork along evacuation routes near their school, identifying hazardous locations and recording observations using tablets. The collected data was visualized in Re:Earth Visualizer and overlaid with existing hazard datasets — including tsunami and flood risk layers — to create a "Digital Disaster Risk Map." Students then simulated evacuation route selection under different disaster scenarios, making abstract risks concrete and actionable. --- **Conclusion** These case studies demonstrate that open source geospatial tools, when designed with accessibility in mind, can extend GIS far beyond the expert community — turning it into a tool for civic participation and education. false https://talks.osgeo.org/foss4g-2026/talk/XBBZJF/ Conference Management Room4 General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 We introduce "Virtual Shizuoka," a high-density 3D point cloud dataset published as open data by a prefectural government, Shizuoka prefecture. This open data has driven innovation and changed our community. We present its use cases, focusing mainly on transforming the community through collaborative efforts, and discuss its outlook. foss4g-2026-5523-the-challenge-of-open-point-cloud-data-what-has-a-local-government-brought-to-our-communities Kazuya SatoNAOYA SUGIMOTOYusuke　Koike en Virtual Shizuoka is a high-density, colorized 3D point cloud dataset captured and published as open data by a prefectural government, Shizuoka prefecture. The project began in 2019 and achieved full coverage of the entire prefecture by 2025. This point cloud data is utilized for various purposes, including visualization, analysis, and simulation, and driving significant innovation as open data. In this presentation, we will explore how this open data has expanded its use cases and transformed the community. We will introduce case studies on streamlining consensus-building using advanced software like game engines, optimizing resource allocation by empowering back-office support to reduce the burden on the frontline, and community collaboration in disaster response. We will also introduce the cultural shift of the community thanks to the Digital Twin. Furthermore, we will discuss our outlook on gathering further insights and building collaborative frameworks based on these successful case studies. false https://talks.osgeo.org/foss4g-2026/talk/SH8MRB/ Conference Management Room5 General session talk 2026-09-01T13:00:00+09:00 13:00 00:30 What does it look like to teach programming and GIS entirely online? This talk shares stories from running the Kartoza internship remotely, guiding university students from basic computer science concepts to building simple web GIS applications with Python and Django. foss4g-2026-4965-from-binary-to-web-maps-teaching-gis-programming-in-the-kartoza-internship Zulfikar Akbar Muzakki en This talk shares the experience of running a sixteen-week geospatial programming internship fully online. All sessions are conducted remotely via Google Meet. We begin with foundational computer science concepts: binary numbers and computational thinking, before moving into Python and geospatial programming. Students learn about functions, object-oriented concepts, shapefile validation, vector and raster handling, and eventually build simple web GIS applications using Django and Folium. But teaching remotely changes the dynamics. You cannot easily see confusion. Silence can mean understanding or complete loss. Debugging happens through screen sharing. Breakthrough moments happen quietly, sometimes in chat messages instead of raised hands. A successfully rendered web map might be followed by muted microphones and a simple “Finally…” in the chat. Rather than presenting a polished educational framework, this session reflects on what it means to mentor students in programming and GIS in a fully remote environment: the challenges, the awkward moments, the small wins, and the human side of teaching technical skills through a screen. false https://talks.osgeo.org/foss4g-2026/talk/RBBMRZ/ Conference Management Room5 General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 Presentation introduces the SLA4GIS concept – its idea, scope, technical support principles, membership certification process and request handling mechanism. It highlights how the initiative redefines FOSS4G support, driving digital transformation across business and government while dispelling the mythical argument about the lack of official support for OpenSource GIS. foss4g-2026-5411-sla4gis-international-association-of-organizations-providing-technical-support-for-opensource-gis /media/foss4g-2026/submissions/ZRWFRZ/SLA4GIS_n51LHgq.png Michal Zugajewicz en Defined problem: OpenSource GIS software is becoming increasingly popular in commercial, government, and educational organizations. Over the last 2-3 years, there has been a sharp increase in interest in FOSS4G - mainly due to economic reasons (macroeconomic situation in Europe and worldwide), and recently also for political reasons (technological dependence on a single vendor, data security). However, there is something that has been a major Achilles' heel for FOSS4G for years and the main argument from closed software GIS users against switching to open source software - namely the lack of standardized and clearly defined technical support. Unlike closed commercial solutions, there are no universal standards regarding the scope and principles of support for FOSS4G. And precisely in response to this criticism, we present a new initiative: SLA4GIS - an international association of companies providing SLA services for OpenSource GIS. The goal of the project is to eliminate the main barrier to implementing the FOSS4G ecosystem in corporate and government environments, as well as to ensure its reliability. Key objectives of the SLA4GIS project: 1. Standardization of SLA for FOSS4G - the association defines worldwide standards for technical support, its scope and guarantees consistency and professionalism 2. Quality control - continuous monitoring and audit of member institutions (existing and new ones wishing to provide support services) 3. Exclusivity of the association - SLA4GIS members can only be institutions that have been operating in the market for at least 10 years and are actively engaged in the development of open source GIS software 4. Knowledge exchange - cyclical meetings (online and offline during international conferences), as well as a dedicated channel for knowledge and experience sharing (e.g. Telegram group) 5. Quality-assured initiative - obtaining official approval from QGIS, OSGeo, and OGC organizations false https://talks.osgeo.org/foss4g-2026/talk/ZRWFRZ/ Conference Management Room5 General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 Training is often treated as an afterthought in the geospatial industry, yet it is essential for real adoption of open tools. This talk share practical lessons from delivering and creating training for GIS, remote sensing, and earth observation, what worked and what didn’t, and what I now do differently foss4g-2026-4944-training-without-barriers-lessons-for-building-geospatial-capacity Seabilwe Pontso Leah Tilodi en Many of us become trainers in the geospatial industry without formal teaching qualifications. We get asked to onboard a team to QGIS/PostGIS, introduce Earth observation workflows, or create material that people can actually follow. I’m not presenting this as “the one right way” to train, rather as lived experience: the ups, the mistakes, and the approaches that consistently help, participants (or students) leave with confidence. The talk takes the audience through a set of lessons I learned while delivering training and building learning material in GIS, remote sensing, and earth observation, often using open and free tools. I’ll cover: • Start with foundations, not features: why spatial concepts (scale, CRS, uncertainty, raster vs vector, metadata) make or break learning outcomes, especially for beginners • You don’t have to be a domain expert: how: spatial thinking” helps you train across sectors (urban planning, agriculture, environment) by listening for goals, decisions, and patterns rather than trying to master every discipline. • The responsibility of teaching (and writing): holding someone’s hand for three days or a week is significant, so is publishing training material that others will trust and reuse. • Learners are diverse: how different personalities, confidence levels, and learning styles shape pacing, explanations, and support, whether your participants are a graduate intern or the CEO. • Capacity building as part of governance: training is not only a workshop; it affects how organisations handle data, documentation, reproducibility, and continuity when staff changes. • Common challenges with clients: unclear scope, missing context, and late information, and how to reduce risk without overcomplicating delivery. I will include a short demonstration example drawn from prior training and training material (not a hands-on exercise), and close with a practical checklist and reflection questions that participants can apply to their training and knowledge-sharing work, so we keep learning open, accessible, and without barriers. false https://talks.osgeo.org/foss4g-2026/talk/TSERMZ/ Conference Management Room5 General session talk 2026-09-01T14:30:00+09:00 14:30 00:30 To provide geospatial mapping training and raise awareness on the use of geospatial tools to empower Pacific Women — including young women, women with disabilities, and women from outer islands in mapping to access, utilize, and apply mapping resources for community development and decision-making. foss4g-2026-4852-the-pacific-geospatial-women-network Jacqueline Singh en The Pacific Geospatial Women Network (PGWN) is endorsed under the Oceans Management and Literacy Programme at the Pacific Community (SPC) and reports to the Pacific Geospatial and Surveying Council (PGSC). The network was established to promote women’s capacity, participation, and leadership in Geospatial Science and Earth Observation (EO) across the Pacific. Its overarching goal is to raise awareness, celebrate achievements, and create a strong support network for women working in, or aspiring to enter, the geospatial field. In 2024, PGWN successfully piloted its community-based approach with two local women’s groups in Fiji. The pilot integrated geospatial capacity-building into existing community-led environmental activities, recognising that women and marginalised groups often face systemic barriers to accessing technology, technical training, and decision-making spaces. By intentionally engaging women from rural communities and those with diverse abilities, the initiative helped bridge the digital divide and promoted inclusive and equitable participation in climate resilience efforts. Through its regional mandate, PGWN works to ensure that women are not left behind in this digital and data-driven age. Network activities focus on hands-on learning, introductory mapping and Earth Observation awareness, digital literacy, and the practical application of geospatial tools for decision-making, community planning, and environmental resilience. By equipping local women’s groups with these skills, PGWN supports inclusive development and enables women to actively contribute to shaping the future of their communities. In 2025, PGWN expanded regionally for the first time, including the delivery of a tailored training and awareness programme for Kiribati Women in Mapping (KWIM). This initiative promoted the integration of traditional knowledge with geospatial technologies, empowering local women to engage in inclusive data collection and community-led planning. The Kiribati activities demonstrated the value of combining local knowledge systems with geospatial science to support national and regional resilience efforts. Building on this momentum, PGWN plans to expand its activities to additional Pacific Island countries in 2026. The network aims to strengthen regional collaboration, peer learning, and knowledge exchange among women geospatial practitioners, while adapting its methodologies to local contexts and priorities. While geospatial science is well established globally, its application in rural Pacific contexts is still evolving. PGWN addresses this gap through capacity-building, awareness campaigns, and partnerships with universities, non-profit organisations, and regional institutions. Planned initiatives include mentorship and internship opportunities for female geospatial graduates, supporting women’s leadership in STEM and contributing to a more inclusive and sustainable future for the Pacific. false https://talks.osgeo.org/foss4g-2026/talk/RXDKBG/ Conference Management Room5 General session talk 2026-09-01T15:30:00+09:00 15:30 00:30 A non-technical talk in regards to the structure of OSGeo, it's members, local chapters, and financing. The work of OSGeo:UK, the prospect of OSGeo Europe, and our plans for FOSS4G 2027 in Bristol. The impact of Gen AI for OSGeo and Open Source licences. foss4g-2026-4877-where-next-for-osgeo Dennis Bauszus en I would like to present my view on the current structure of OSGeo, it's local chapters, and planned advancements in regards to our financing crisis. The idea of an OSGeo Europe entity is appealing with funding available through the European Union Horizon programme. I would like to present the work we are doing at OSGeo:UK in regards to membership, partnerships, and our plans for FOSS4G 2027 in Bristol. We would like to highlight the need for better cooperation with organisational partners and event sponsors. Of particular interest is the cooperation with multinational organisations who are not considered as spatial but depend on geographical information, processes, and software. We will also discuss the advent of Gen AI and the impact on Open Source licences. false https://talks.osgeo.org/foss4g-2026/talk/HZXWPH/ Conference Management Room5 General session talk 2026-09-01T16:00:00+09:00 16:00 00:30 This talk introduces two QGIS plugins designed to boost OGC API adoption, which may be hindered by a lack of tools. The "pygeoapi configurator" simplifies publishing data, while the "QGIS OACS" plugin helps users discover and visualize datasets. foss4g-2026-4996-new-ogc-api-plugins-on-the-block /media/foss4g-2026/submissions/8XAUVP/screenshot-resources_3kOhrdF.png Joana SimoesRicardo Garcia SilvaKateryna Konieva en OGC API has been around since 2017, with the “promise” of improving and eventually replacing the first generation OW*S services (e.g.: WMS, WFS, WMTS, etc). However it is still far from having become ubiquitous yet, partly due to a still under-developed ecosystem of tools to publish and consume data (and metadata) using OGC API. In this talk we will show you two plugins designed to support users in publishing and consuming data in OGC API, using one of everyone’s favorite tools, QGIS. The pygeoapi configurator plugin lets users create or update their pygeoapi configuration file, lowering the barrier to use this powerful OGC API server implementation. The QGIS OACS plugin is a client for OGC API - Connected Systems servers. This initial version allows users to discover and visualize datasets exposed by those services as resources. We hope these plugins can motivate more users to get their hands on OGC APIs, and look forward to having contributions from the community to keep improving them and adding more features. We also aim to add more OGC API QGIS plugins in the near future. Stay tuned! false https://talks.osgeo.org/foss4g-2026/talk/8XAUVP/ Conference Management Room5 General session talk 2026-09-01T16:30:00+09:00 16:30 00:30 Comparing differences between basemaps or datasets in QGIS is difficult when only one map can be viewed at a time. QMapCompare solves this by bringing interactive map comparison into QGIS. The plugin supports multiple visualization methods to quickly reveal changes across styles and datasets. foss4g-2026-5205-seamlessly-compare-maps-on-qgis-with-the-qmapcompare-plugin /media/foss4g-2026/submissions/B9GUVT/Qmapcompare_Z5mlSb0.png Raymond Lay en While many web tools make it easy to compare map styles, QGIS has long lacked a stable, user-friendly comparison feature. This presentation introduces QMapCompare, a plugin that brings fast visual comparison directly into QGIS, letting you evaluate layers or styles in a single canvas without constantly toggling visibility. It offers the following comparison modes: - Mirror - Split - Lens This presentation also showcases practical use cases: - Comparing basemap styles - Comparing datasets - Exploring historical maps - Before/after disaster analysis false https://talks.osgeo.org/foss4g-2026/talk/B9GUVT/ Conference Management Room5 General session talk 2026-09-01T17:00:00+09:00 17:00 00:30 Think of a map as an artistic still life. Our framing often excludes items just out of view. Being completely factual is not the same as being factually complete. Let’s learn skills to enhance our digital stories from 3D environments. New perspectives lead to improved perception. foss4g-2026-5016-not-on-your-google-maps-the-map-is-not-the-territory /media/foss4g-2026/submissions/ZT7P3A/Screenshot_2026-03-05_at_12.06.12_t3NvfBn.png bonny p mcclain en Instead of simply focusing on Euclidean geometry when comparing polygons represented as urban forms, let's explore urban form and the impact of the built infrastructure in defining persistent barriers across the built infrastructure. 'As the crow flies' measures don’t move anyone closer to a bus stop or a market when blocked by a parking lot, building or highway. Cinematic stories guide us through the steps for formulating data questions that bring social demographic datasets into play within 3D environments in Blender, Cesium, Unreal Engine and QGIS. The contextual conversation around environment, culture, and sociopolitical issues. The compassion — the “why we should care” layer of geospatial. false https://talks.osgeo.org/foss4g-2026/talk/ZT7P3A/ Conference Management Room5 General session talk 2026-09-01T17:30:00+09:00 17:30 00:30 This talk presents a fully open-source workflow for geospatial modeling of satellite-derived chlorophyll-a data using QGIS, GDAL, and R. We demonstrate how large raster datasets can be processed, interpolated with kriging, and visualized with uncertainty, entirely within the OSGeo ecosystem. foss4g-2026-4889-from-geotiff-to-kriging-a-fully-open-source-remote-sensing-workflow /media/foss4g-2026/submissions/RYK73W/Data_flow_015_Klfh44J.png Junji Yamakawa en Satellite remote sensing provides essential information for environmental monitoring, yet spatial gaps caused by cloud cover often limit continuous analysis. This presentation demonstrates a complete, reproducible geospatial workflow for kriging-based modeling of satellite-derived chlorophyll-a concentrations using only open-source software. The case study focuses on MODIS-derived data from the Gulf of Finland. Large GeoTIFF datasets were processed and resampled using GDAL and QGIS, where bathymetry and coastline distance were also prepared as spatial covariates. Spatial statistical modeling, including variogram fitting and Universal Kriging (Kriging with External Drift), was conducted in R using the “gstat” package. Beyond presenting the modeling results, this talk emphasizes practical implementation aspects: coordinate transformations with PROJ, handling large raster data under memory constraints, variogram fitting challenges (WLS vs GLS vs REML), and interpreting kriging variance in cartographic outputs. We also discuss practical issues such as negative predictions in Universal Kriging and how to manage them responsibly in environmental applications. The presentation highlights how researchers and practitioners can build a complete GIS–statistical pipeline without proprietary software. This workflow is suitable not only for marine environmental monitoring but also for broader earth observation applications, education, and reproducible geospatial research. false https://talks.osgeo.org/foss4g-2026/talk/RYK73W/ Conference Management Room6 General session talk 2026-09-01T13:00:00+09:00 13:00 00:30 How can open-source GIS support large-scale and collaborative validation of urban water networks? This talk presents a scalable workflow built with QGIS, Giswater, PostGIS and QFieldCloud, enabling structured field data collection, multi-organization collaboration and high-quality datasets for hydraulic modelling in a national water utility. foss4g-2026-4973-designing-a-scalable-open-source-workflow-for-water-network-field-validation-lessons-from-costa-rica Albert Bofill en This presentation describes the evolution of the enterprise GIS workflow at AyA (Instituto Costarricense de Acueductos y Alcantarillados, Costa Rica) to support large-scale validation and surveying of urban water distribution networks using a fully open-source stack. The initial system focused on inspection visits and partial network review. However, it did not support full asset surveying when required, lacked isolation from the production database, and was not designed for simultaneous collaboration between multiple external organizations. As the need grew to update and validate large portions of the network, a more robust and scalable approach became necessary. The new workflow is based on QGIS, Giswater, PostgreSQL / PostGIS, and QField with on-premise QFieldCloud. A dedicated campaign schema was introduced to separate field operations from production data, ensuring stability and controlled validation before integration. Field campaigns and work packages are generated through custom tools in the Giswater QGIS plugin. Each package defines a geographic area and is assigned to a specific organization and field team. This design allows several external contractors to work in parallel while ensuring data isolation and preventing visibility between organizations. Using QFieldCloud, projects are directly connected to the central PostgreSQL database via pgservice. Field teams update pipes, valves, pumps, tanks and other network assets in real time, comparing existing GIS data with actual field conditions and creating new assets where necessary. Changes become immediately available for validation in QGIS Desktop, reducing synchronization issues and improving transparency. The workflow is currently designed to support large-scale surveying in Costa Rica, with ongoing work in parts of San José and full coverage planned for other cities. At present, two external organizations are actively working alongside AyA, involving around 20 concurrent users between field and office roles. Key technical design choices to support operational needs include: - Image management via WebDAV, automatically mounted on the server. A relational intermediate table allows unlimited images per asset, ensuring structured multimedia documentation. - Implementation of a PostgreSQL session role mechanism, enabling the database current_user to reflect the authenticated QField user. This allows precise change tracking, user-based filtered views, and improved accountability. - Custom PostgreSQL functions supporting topological control directly from QGIS, including detection of duplicate or orphan nodes and bulk pipe splitting using pre-existing nodes. Once campaigns are completed, edits are reviewed and validated before controlled integration into production. The resulting datasets significantly improve network quality and enable reliable hydraulic modelling with Giswater, supporting strategic decision-making and long-term water management. Although presented through the AyA case, the architecture and design principles are transferable to other utilities managing distributed infrastructure assets. The session will highlight reusable patterns and lessons learned to help organizations design collaborative, database-driven field workflows using open-source technologies. false https://talks.osgeo.org/foss4g-2026/talk/WKMWXY/ Conference Management Room6 General session talk 2026-09-01T13:30:00+09:00 13:30 00:30 Japan has witnessed a surge in human-bear interactions, raising concerns about public safety and wildlife conservation. This phenomenon can potentially be traced back to dual pressures of climate change, urbanization significantly altering bear habitats and food sources. foss4g-2026-4818-drawing-the-lines-visualizing-the-role-of-climate-change-and-urbanization-shaping-human-bear-interactions-in-japan /media/foss4g-2026/submissions/THTSHW/Screenshot_2026-01-28_at_16.00.14_yAnSMmr.png bonny p mcclain en Urbanization and agricultural expansion in hilly and mountainous regions have led to significant habitat loss for bears. In Japan, areas like the Japanese Alps and the mountainous regions of Honshu and Shikoku have experienced extensive deforestation, reducing bear habitats and pushing them toward human settlements. This cinematic conversation will explore how data visualizations can illuminate these complex dynamics and enhance our understanding of the human-wildlife conflict. By analyzing geographic and temporal datasets related to bear encounters, habitat loss, and shifting climate patterns, we can identify critical areas where interventions are necessary. Average temperatures have risen by approximately 1.4°C over the past century, with projections indicating further increases. This warming trend has led to more intense typhoons and heavy rainfall, resulting in elevated flooding and landslide risks. Coastal areas are facing rising sea levels, threatening ecosystems and infrastructure. Agricultural productivity is also declining due to heat stress and erratic rainfall, leading to economic challenges for farmers. The bear's encroachment into urban areas is not simply an inconvenience; it serves as a poignant reminder of the fragile balance we have disrupted requiring us to reflect on our responsibilities as stewards of the environment before we find ourselves unwittingly cohabiting with wildlife in ways we never anticipated. We will explore the intersection of climate change, urbanization, and wildlife interactions through the lens of spatial data visualization. Using Japan's recent surge in human-bear encounters as a case study, we will utilize open-source tools to analyze and visualize the effects of habitat loss, urbanization and climate science. Attendees will gain practical skills visualizing data live utilizing QGIS, Blender, Cesium and Unreal Engine. The goal is to experience the power of immersive storytelling through spatial simulations to build interactive 3D maps that provide a real-time view of bear movements and human developments in Japan, allowing for a dynamic exploration of the spatial relationships and trends at the edge of urbanization and ecological systems impacted by resource reallocation and changes in climate. false https://talks.osgeo.org/foss4g-2026/talk/THTSHW/ Conference Management Room6 General session talk 2026-09-01T14:00:00+09:00 14:00 00:30 A QGIS plug-in is developed for supporting designing culverts in civil engineering projects so that manual work currently adopted by the industry can be automated. foss4g-2026-5587-making-drainage-engineers-life-easy-a-culvert-designers-plug-in-for-qgis /media/foss4g-2026/submissions/GZPBN3/Screenshot_2026-03-23_at_10.22.11pm_M9isSkM.png BlakeNimalika Fernando en Accurate design and pricing of drainage components is important in providing compliant, long lasting and cost-effective solutions in civil engineering projects. These solutions are required to prevent critical infrastructure from flood inundation. Current processes in the industry rely heavily on manual manipulation by engineers, leading to variable and time-consuming design procedures. We design an automated, repeatable and reliable procedure to support drainage design as a QGIS python plug-in to address the above issues. Hydrologic and hydraulic design procedures are combined to suggest location, size and quantity of corrugated steel pipe culvert structures along a road or rail alignment. The automated plug-in is intended to deliver cost savings from improved design efficiency and optimised culvert quantities. The plug-in has now been undergoing testing in Australian industrial setting, focusing on Western Australia and expects to expand to adapt to different rainfall locations and culvert types in a variety of geographic regions. false https://talks.osgeo.org/foss4g-2026/talk/GZPBN3/ Conference Management Room6 General session talk 2026-09-01T14:30:00+09:00 14:30 00:30 DGGS gaining popularity in the GeoAI space, we'll look at pros and cons of storing your vector and raster data in couple of indexing algorithms. foss4g-2026-4924-storing-your-satellite-in-a-dggs json singh en Earth observation using remote sensing (or satellite data) comes with its own fair bit of challenges. From managing large amounts of data to dealing with projections, inconsistencies with resolutions and more. While there's no silver bullet to solve all these problems, we'll look at one possible of modelling your GIS data (not just satellite data) in a Discrete Global Gird System (DGGS). In DGGS, you divide the earth into multiple cells and store the data in each cell. There are few advantages of using this approach - Single data structure for all your GIS (vector or raster) - Rasters are represented as array of Cell ids with each cell having a value - Vectors are represented as Points => Cells, Lines => Array of Cells, Polygon => Array of Cells - A single base frame with multiple layers of data built on top - Easy visualization and analysis - Easy storage and retrieval based on Cell IDs It's not all rainbow in the DGGS land, you lose: - Existing tool set not built around DGGS - Distortions/Errors going from point data to cells But still it's an interesting way to look at data. We'll use - DuckDB as our database and query engine - Sample data from OSM - Sample data from Sentinel-2 false https://talks.osgeo.org/foss4g-2026/talk/XSK3HK/ Phoenix Hall General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 Open-source software was used to develop water flow and tracing models of Te Awarua o Porirua (Porirua Harbour), Aotearoa-New Zealand. To facilitate model outputs’ interpretation by the local community, an opensource dashboard was developed together with 3D visualisations of the data. All developed products remained with the community. foss4g-2026-5547-open-sourcing-the-ivory-tower-or-how-to-remove-barriers-between-scientific-modelling-and-communities-through-open-source-software /media/foss4g-2026/submissions/3KXZCA/Screenshot_2026-03-23_094441_A2DPWFA.png Matthew WilsonMaria Cecilia Vega Corredor en Scientific modelling of the environment is useful to explain or predict phenomena that otherwise can be difficult to infer through direct observation. However, model complexity and their difficulty to be understood by non-scientific people often restrict their ability to help solve critical problems, creating a barrier between research and end users. Research findings may never reach/be accessible to those who really could benefit from them, e.g. the local populations. Broken or non-existent communication channels, high software costs, and profit pressures, make it difficult for the communities to have access to these tools that could make a significant impact in their planning and decision making. In this project, open-source software and open data were used to address environmental challenges that an indigenous Māori community had experience with their local harbour, Te Awarua o Porirua. Ngāti Toa Rangatira is a Māori iwi (tribe) of Aotearoa-New Zealand and are the Kaitiaki (customary guardians) of Te Awarua o Porirua. The harbour plays a fundamental cultural and historical role and has spiritual significance for the iwi; it has also sustained them as a source of māhinga kai (food gathering) as well as being a place for cultural practice and recreation. However, since the 1940s, Te Awarua o Porirua has undergone significant environmental degradation resulting from cumulative impacts including land reclamation, pollution and wastewater discharge, changes in upstream land use and land cover, commercial activities and rapid urban expansion. In response, Ngāti Toa Rangatira have worked in partnership with local authorities and the wider community to develop and implement restoration strategies grounded in both indigenous knowledge and contemporary environmental management, such as Te Wai Ora o Porirua, an agreement between local partner organisations to improve the health of the harbour for future generations. To help understanding the complexity of the harbour’s processes, the Geospatial Research Institute (GRI) was commissioned by the National Institute of Public Health and Forensic Science (PHF Science) to support their Indigenous-led wai (water) programme He Wai Māpuna. This programme focuses on Māori-led initiatives supported by dual knowledge systems of mātauranga Māori (Māori knowledge) and modern science. This partnership worked in conjunction with Te Runanga o Toa Rangatira to develop open-source modelling software, which simulates the harbour flow and enables water source tracing of its main constituents: river inflows, rainfall and tide. In addition, a user-friendly dashboard was developed for data visualisation, based on Cesium/ TerriaJS. The final products (open-source models and dashboard) together with the data remained with the community for their use and benefit. In this talk, we will present the modelling work completed and provide reflections on the experience of the deep engagement between the Ngāti Toa Rangatira and the development team. This project is a successful example of how open-source can enable complex science to be transferred, made accessible and easy to interpret by local communities in need and would benefit from it. Our aim is for this work to be disseminated and replicated elsewhere. false https://talks.osgeo.org/foss4g-2026/talk/3KXZCA/ Phoenix Hall General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 This presentation introduces the public release of vector tile data for the Actual Vegetation Map 2024 and the launch of the Satellite-based Vegetation map 2030. It outlines technical design, open data distribution, and interoperability with open-source GIS to support biodiversity and environmental applications. foss4g-2026-5574-open-vector-tiles-for-the-actual-vegetation-map-2024-and-launching-the-satellite-based-vegetation-map-2030 /media/foss4g-2026/submissions/9RN8DJ/veg2024class10_BUncEo6.png Shingo YAMASHITA en National-scale vegetation maps are essential geospatial datasets for biodiversity conservation, environmental assessment, and spatial planning. To enable effective reuse by developers and GIS practitioners, these datasets must be provided as open data using scalable and modern delivery methods. This presentation introduces two recent initiatives in Japan to enhance open vegetation data infrastructure. The first is the public release of vector tile data for the Actual Vegetation Map 2024, which represents current national vegetation conditions at a detailed scale. The vector tile format enables efficient web visualization, improved performance, and smooth integration with open-source GIS tools. The second initiative is the start of the Satellite-based Vegetation Map 2030, a new national effort that utilizes satellite remote sensing to improve update frequency and timeliness. This satellite-based map is designed to complement the existing vegetation map, together forming a multi-temporal vegetation mapping framework. This presentation focuses on technical design, open data provision, and interoperability with open-source GIS to support biodiversity and environmental applications. false https://talks.osgeo.org/foss4g-2026/talk/9RN8DJ/ Phoenix Hall General session talk 2026-09-02T14:30:00+09:00 14:30 00:30 In Nagaoka, Japan, 23 citizens aged 11–70+ with no GIS experience mapped the 1945 air raid using Re:Earth, an open-source WebGIS platform. This talk explores how no-code CMS architecture enabled real-time "input-to-visualization" workflows, transforming community members into active contributors to geospatial heritage preservation. foss4g-2026-5130-citizen-driven-war-memory-mapping-enabling-non-experts-to-build-geospatial-archives-with-re-earth-cms-and-visualizer Haruka Yasuda en Eighty years after the Nagaoka Air Raid of August 1, 1945, the city is renovating its War Memorial Museum. As part of this initiative, TISSUE inc. and Eukarya Inc. collaborated to create a citizen-participatory digital archive—a map that visualizes survivor testimonies, historical photographs, and evacuation routes on period maps. **The Challenge** Traditional GIS workflows require technical expertise that excludes most community members from contributing. Our goal was to design a system where non-experts—from 11-year-old students to museum volunteers in their 70s—could directly input geospatial data without accounts, training, or coding. **Technical Approach** We leveraged Re:Earth CMS and Visualizer with two key design principles: *Instant Input-to-Visualization Workflow*: Data entered in Re:Earth CMS is immediately reflected in the Visualizer map. Participants could see their contributions appear in real-time, providing immediate feedback and motivation. *No-Code Geospatial Data Entry*: We designed CMS schemas that allow users to select coordinates by clicking on a georeferenced historical map overlay, attach photographs from a pre-uploaded asset library, and add textual annotations—all without writing code or understanding coordinate systems. **Workshop Design** Workshop 1 (January 25): Participants examined wartime photographs, identified locations through group discussion with museum experts, and registered them in CMS with coordinates and contextual notes. Workshop 2 (February 23): Participants read survivor testimonies, extracted movement sequences, and mapped evacuation routes as polyline data in CMS. **Results** Twenty-three participants across diverse demographics (ages 11–70+, including students and teachers) successfully contributed geospatial data. The output will be integrated into the museum's permanent exhibition opening in May 2026. Participant feedback revealed the educational and social impact of this approach. One participant noted: "Converting text into a map felt like re-experiencing someone's memory during the air raid. I found deep meaning in archiving the memories of ordinary people, not just famous historical figures." Another shared: "Compared to traditional peace education where you just listen to stories, actually working with my hands and tracing the paths made the war experiences feel much closer to me." **Key Takeaways for the FOSS4G Community** This project demonstrates practical patterns for enabling community participation in geospatial projects: - No-code CMS design patterns that abstract coordinate complexity into simple click-to-locate interactions - Real-time feedback loops as a motivational tool for non-expert contributors - Intergenerational workshop formats combining local knowledge with digital tools I will share our CMS schema designs, workshop facilitation methods, and lessons learned for community-driven geospatial heritage projects. false https://talks.osgeo.org/foss4g-2026/talk/GJEVKW/ Phoenix Hall General session talk 2026-09-02T15:30:00+09:00 15:30 00:30 Every serious MapLibre project hits this wall: a monolithic style.json nobody fully owns. Designers want a GUI, developers edit JSON by hand, version history is one huge diff. Mapstrata is a visual editor that makes collaboration possible: spec validation, variant support, and a project format built for git. foss4g-2026-5499-a-new-approach-to-maplibre-style-editing Stephanie May en If you maintain a MapLibre stylesheet with more than a few dozen layers, you already know the problem. A color change that should take five minutes takes fifty because you need to search and replace, plus visually review, across dozens of layers. Designers iterate in Maputnik, but complain about it, meanwhile developers won't touch it — it applies edits in ways that make source control of the style JSON impossible. So instead they edit JSON by hand with a live reload and consider the visual editor part of the problem, not the solution. Nobody has a full picture of the style, version history is indecipherable. This isn't a tooling preference — it's a workflow gap that shapes how the MapLibre ecosystem develops. Right now there's nothing between a visual editor that hides the JSON and a text editor that gives you all of it with no structure. Writing layers requires an encyclopedic and up-to-date knowledge of the style spec, superhuman grasp of how expressions work, *and* instantaneous visual feedback on what incremental changes produce. The WYSIWYG approach has real limits of its own: it typically generates verbose JSON, obscures optimization opportunities, and doesn't integrate with the code, reload, commit workflow that experienced style authors actually prefer. Mapstrata takes a different approach. Instead of replacing the JSON workflow with a visual one, it makes the JSON workflow maintainable. Import a stylesheet and Mapstrata decomposes it: layers become reviewable, diffable, attributable in git. Build variants and maintain them. Iterate on changes live with a MapLibre preview pane. Export, commit, and collaborate on the Mapstrata files or the composed style JSON. No cloud dependency, no lock-in, no proprietary format. The design philosophy is that the editor should make the underlying structure legible, not hide it. Variables use embedded references directly in layer JSON, so you can read any layer file and immediately see which properties are variable-driven versus hardcoded. The build engine validates against the MapLibre style spec on every edit, catching errors before they produce a blank map. This talk will demo the editor, share what I know about what style authors actually need versus what we assumed they'd want, and outline where the project is headed — source management, layer property editing, and variant support for theme switching. Come to the talk and sign up to test the editor — we're looking for contributors and teams willing to put Mapstrata through its paces on real stylesheets. false https://talks.osgeo.org/foss4g-2026/talk/TB3DZV/ Phoenix Hall General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 Open-source geospatial data and Overture Maps can generate detailed dwelling, transport, and service models supporting decision making. Case studies from Australia, New Zealand, Fiji, and Spain demonstrate applications for accessibility analysis, disaster risk reduction, and resilience, highlighting data strengths, limitations, and infrastructure needs. foss4g-2026-5156-impactful-data-international-cooperation-to-increase-the-value-of-open-source-data-software-including-in-hazard-scenarios Jesse WhiteheadFiu Etike Penjueli en Accurate data and modelling can provide useful information that is valuable for good decision making. International networks of people and infrastructure have contributed to the development of global assets like Open Street Maps. Now, the completeness, accuracy, and timeliness of such data often equals or surpasses some commercial and governmental datasets. Arguably, open geospatial data collected through Open Street Map and Overture curation processes is now of sufficient quality to be integrated into some government data processing pipelines and support government decision making. However, challenges remain. Even in ‘high-income’ countries such as Australia, significant gaps in geospatial digital infrastructure remain, particularly in remote areas, contributing to data inequity. Open data will have limited benefit unless there is a clear purpose. The value of data is directly related to how it is used to support human activities. This paper describes how open-source data, and national administrative data can be integrated to create modeled residential dwelling reference frames, complex transport network models, and service location registers that enable the modelling of service access metrics. Case-study examples from Australia, New Zealand, Fiji and Spain are provided. The Australian case study outlines the modelling of the location of dwellings in northern remote Australia, where government data sources are non-existent or incomplete. This work has been supported by the Australian Bureau of Statistics (ABS) with the aim of reducing error in the Census process though a better understanding the location & occupancy of dwellings in Australia . In New Zealand we have developed a proof-of concept approach using open-source data, to estimating the impact of extreme weather events on the spatial accessibility of health services. We expand on this approach to model the impact of floods under different climate change scenarios. This provides important information to support disaster risk reduction and resilience in an area that is little researched in the NZ context to date. Spain increasingly experiences road network disruption from flash floods. Fiji is an archipelago nation comprising more than 330 islands, of which around 110 are permanently inhabited. According to the most recent 2017 Fiji Population and Housing Census, the population was counted at 884,887 with nearly three quarters residing on the island of Viti Levu, which is also home to the capital city, Suva. Population dynamics affect every aspect of our nation – whether its economic growth, employment, social security, health, education or environment sustainability. In Fiji, the spatial data for population are based on statistical boundaries (enumeration areas) which are compatible with the administrative boundaries. The Fiji Bureau of Statistics (FBoS), with support from the Statistics for Development Division (SDD) of the Pacific Community (SPC), have made significant progress in developing and refining Fiji’s population grid. The grids are derived from the most recent 2017 Population and Housing Census data, estimated to the year 2025. This collaboration with SPC, CARA and Waikato University has shown significant development on the experimental service access metrics for Fiji using open-source data. false https://talks.osgeo.org/foss4g-2026/talk/FZMMN9/ Phoenix Hall General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 As Open Data and FOSS4G grow, "open" does not always mean "accessible" due to language and local technical barriers. This presentation highlights these "invisible walls" and explores how Generative AI can bridge these gaps, making local geospatial data truly usable for the global community. foss4g-2026-5558-breaking-the-barriers-using-generative-ai-for-global-geospatial-data-accessibility Takahiro Endo en In Japan, premier platforms such as the Digital National Land Information and the Geospatial Information Center provide essential datasets for disaster management, urban planning, and environmental analysis. However, these portals often lack comprehensive English interfaces or metadata, creating a formidable Language Barrier. Beyond translation, a Technical Barrier exists in the form of localized standards. Japan’s Plane Rectangular Coordinate System (consisting of 19 distinct zones) and unique Map Sheet Codes (Zukaku-codes) are often undocumented in international contexts. For a developer outside Japan, interpreting a dataset encoded in "Zone 9" or navigating a Japanese-only download manual requires niche expertise that traditional translation tools cannot provide. By integrating Generative AI into the FOSS4G workflow, we can lower the entry barrier for global collaboration. This session will demonstrate how AI can assist in navigating Japanese data portals and automating the processing of local datasets. Our goal is to move toward a truly inclusive geospatial ecosystem where local wisdom—stored in regional data—is accessible to the entire global community, regardless of language or local technical standards. false https://talks.osgeo.org/foss4g-2026/talk/CTUHKX/ Phoenix Hall General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 GeoAgent is a QGIS plugin that integrates large language models to enable natural language interaction with geospatial data. It allows users to explore layers, perform spatial analysis, and automate geoprocessing workflows through conversational commands, improving accessibility and efficiency in GIS tasks. foss4g-2026-5111-geoagent-a-qgis-plugin-for-natural-language-driven-geospatial-analysis /media/foss4g-2026/submissions/FRJANH/geoagent_overall_architecture_0Wyw0th.png Tek Bahadur KshetriRabin Ojha en GeoAgent is an open-source QGIS plugin that enables users to perform geospatial analysis through natural language commands, powered by large language models (LLMs). The plugin supports multiple LLM backends (Ollama for local inference, OpenAI, and Google Gemini for cloud services) and operates in two modes: General mode for data exploration and querying attributes, and Processing mode for automated geoprocessing workflows. By abstracting traditional GIS complexity through conversational interaction, GeoAgent lowers barriers to advanced geospatial analysis for non-specialist users while maintaining transparency through detailed chat transcripts and automatic result visualization in QGIS. false https://talks.osgeo.org/foss4g-2026/talk/FRJANH/ Phoenix Hall General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 Discover how I built a custom QGIS plugin integrating YOLOv8 and DeepLabV3+ to automate palm oil tree counting, road, and drainage detection from massive drone orthophotos. Learn about overcoming Out-of-Memory challenges and increasing spatial QC efficiency from 200 to 1,500 hectares per day in precision agriculture. foss4g-2026-5243-from-manual-to-ai-building-a-custom-qgis-plugin-for-automated-tree-counting-and-infrastructure-detection Bayu Muhammad Nabiil Makarim en In precision agriculture, extracting actionable spatial data from high-resolution drone imagery is a massive bottleneck. At Terra Drone Agri, traditional Object-Based Image Analysis (OBIA) for tree counting required extensive manual Quality Control (QC), processing only 200 hectares per day. Similarly, infrastructure mapping (roads and drainage) relied heavily on slow, manual digitization. To solve this, I developed a comprehensive, all-in-one QGIS plugin designed to automate these workflows using state-of-the-art Deep Learning models directly within the desktop GIS environment. Our QGIS plugin features three main capabilities: 1. Multi-Variant Tree Counting (YOLOv8): I developed three specific tools tailored for 5-7 cm and 2 cm resolution orthophotos. This includes standard tree counting, a 3-class health classification (Healthy, Yellowish, Dead), and a precision center-point detection tool. The precision tool specifically extracts X,Y coordinates of the palm canopy centers, which are directly exported as flight missions for drone-based precision spraying against Oryctes pests. The model achieved a high mAP50 of 0.971. 2. Infrastructure Detection (Semantic Segmentation): For road and drainage networks, we evaluated U-Net, Pre-trained U-Net, DeepLabV3+, and SegFormer. DeepLabV3+ yielded the best results for road detection (69% accuracy compared to manual digitization). We integrated a complex post-processing pipeline including skeletonization, pathfinding algorithms, and query cleaning to automatically convert pixel masks into clean, connected .shp polylines. 3. Overcoming Technical & Environment Bottlenecks: The core challenge in this development was integrating heavy Deep Learning libraries (PyTorch) into the QGIS environment and performing inference on gigabyte-sized orthophotos without triggering Out-of-Memory (OOM) crashes. To bypass this, we modified the SAHI (Slicing Aided Hyper Inference) library's source code to force sequential tiling for YOLO object detection. For the heavier semantic segmentation tasks, we engineered a memory-swapping mechanism utilizing SSD temporary files to compensate for RAM limitations. This presentation will walk through the end-to-end development process: from data preparation in Roboflow, model training in cloud environments, UI creation with Qt Designer, PyQGIS integration, to deploying a stable local environment. By integrating AI into QGIS, we successfully shifted the workflow from manual digitization to rapid automated QC, boosting tree counting efficiency to 1,500 hectares per day. false https://talks.osgeo.org/foss4g-2026/talk/BN83SS/ Himawari General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 OpenDR 1.0 is a cloud-native framework designed to close the disaster response "latency gap." It automates multi-sensor fusion and GeoAI hazard detection while integrating humanitarian exposure modeling and real-time field validation via OGC API standards, empowering regional agencies with geospatial sovereignty. foss4g-2026-5497-open-disaster-response-opendr-1-0-real-time-cloud-native-geoai-and-multi-sensor-fusion-for-humanitarian-intelligence /media/foss4g-2026/submissions/7TW8PW/OpenDR_1.0_-_Real-Time_GeoAI_Framework_dU2E8RT.png Bayilla Geda en How can we close the "latency gap" during the first golden hours of a disaster? Traditional disaster response workflows are often desktop-bound, siloed, and dependent on a "download-then-analyze" paradigm that fails in resource-constrained environments. This presentation introduces OpenDR 1.0, a fully open-source, event-driven framework built to automate the journey from satellite acquisition to actionable humanitarian intelligence. We demonstrate how we operationalized complex scientific logic—including Adaptive Otsu Thresholding for flood extraction and Harmonic Regression for rangeland monitoring—by porting them from platform-locked environments into a modular, independent FOSS4G stack. Attendees will explore a functional five-tier architecture: Ingestion: Automated discovery of STAC endpoints for multi-sensor data (Sentinel-1 SAR, Sentinel-2, Landsat, GOES-16, and GRACE) managed as Cloud-Optimized GeoTIFFs (COGs). Orchestration: Managed by Apache Airflow, utilizing Directed Acyclic Graphs (DAGs) to trigger specialized analytical pipelines the moment new data is acquired. Big Data Compute: Distributed processing using Dask-Geo on Kubernetes, executing PyTorch U-Net models for hazard segmentation and high-speed imagery unmixing. Mediation: Serving results via pygeoapi and OGC API standards to ensure seamless interoperability with command centers and mobile tools. Feedback: A unique "Loop-in-the-Citizen" cycle where field responders using KoboToolbox provide real-time validation to iteratively refine model weights and reduce false positives. We will share practical implementation lessons from three cross-continental case studies: simulating urban flood resilience with Project PLATEAU 3D CityGML in Tokyo; transboundary health and flood monitoring in East Africa; and 15-minute tactical wildfire tracking. OpenDR 1.0 is a call to action for building reproducible, platform-independent infrastructure. It ensures that regional agencies can own their analytical logic, maintain data privacy, and achieve true Geospatial Sovereignty through the power of the FOSS4G ecosystem. false https://talks.osgeo.org/foss4g-2026/talk/7TW8PW/ Himawari General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 Madagascar uses open-source tools such as DHIS2 and QGIS to geospatialize health data, enabling database management, automated indicators, spatial analysis, dashboards, and interactive web platforms for territorial planning and epidemiological surveillance within a One Health approach in resource-limited settings. foss4g-2026-5184-dhis2-to-qgis-geospatial-analysis-of-climate-associated-health-data-in-madagascar /media/foss4g-2026/submissions/DVYWLZ/Session_image_DHIS2_Madagascar_-_QGIS_l4p9byq.jpg Miora Harivony RAKOTONDRABE en This presentation is based on the use of open-source tools linking DHIS2 with QGIS for the geospatial analysis of health data, based on a case study conducted in Madagascar. Although DHIS2 is widely used as a national system for health database management and health information analysis, its integration with GIS tools remains limited, particularly in resource-constrained settings. The workflow covers the extraction of indicators and metadata through the DHIS2 API, followed by data processing and spatial joins, and finally cartographic visualization in QGIS using open standards. Metadata from DHIS2 enable the mapping of health facilities, the analysis of spatial disparities in health service coverage, and the visualization of the geographic distribution of disease cases for epidemiological surveillance and territorial planning. The presentation highlights interoperability mechanisms, including the alignment of organizational units with administrative boundaries to automate spatial analysis. Based on FOSS4G principles, this interoperable approach is transferable to other countries and can integrate climatic, environmental, or socio-economic data within a One Health framework. false https://talks.osgeo.org/foss4g-2026/talk/DVYWLZ/ Himawari General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 Impact-based flood forecasting asks not "how intense?" but "who gets hit, and how badly?" This talk documents a open-source pipeline — Python, PostgreSQL, and QGIS — that translates rainfall forecasts and hazard layers into impact assessments for critical facilities, with honest discussion of what works, what doesn't, and why. foss4g-2026-4959-operationalizing-impact-based-flood-forecasting-using-open-geospatial-pipelines-in-resource-limited-contexts Feye Andal en Impact-based flood forecasting reframes the core question of disaster risk from "how intense is this hazard?" to "who and what will be affected, and how badly?" Despite growing policy momentum behind this shift, operational implementations using fully open geospatial stacks remain scarce in the literature — particularly in areas where exposure data is fragmented, hazard layers arrive in inconsistent formats and resolutions, and institutional capacity constrains both tooling choices and maintenance overhead. This talk presents a reproducible, production-oriented pipeline for automated impact-based flood forecasting built on three open tools: Python for orchestration and operational logic, PostgreSQL for data management, and QGIS for precalculating facility exposure to high-resolution probabilistic hazard layers. The workflow applies configurable thresholds derived from river basin warning levels to produce categorized impact assessments for schools and critical infrastructure at both facility and administrative unit levels. We discuss the key engineering and institutional decisions that dominate real deployments but rarely appear in conceptual frameworks — including performance tradeoffs driven by high-resolution hazard data, and how to embed automated workflows within government operations in ways that build local ownership. We are honest about where the pipeline succeeds, where it struggles, and what we would do differently. Attendees working on climate risk, disaster preparedness, or humanitarian response will leave with a transferable framework and a realistic picture of what open geospatial tools can deliver in heterogeneous data environments. false https://talks.osgeo.org/foss4g-2026/talk/C87BGX/ Himawari General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 We introduce the first global dataset of road surface type and width for 9 million km of arterial roads using open-source GeoAI workflows. Supporting a Humanitarian Passability Index, it is openly available via HDX (by UN OCHA), enabling scalable infrastructure intelligence for development and climate resilience. foss4g-2026-5015-mapping-the-changing-surface-of-the-world-s-roads-open-source-geospatial-intelligence-for-humanitarian-logistics-and-climate-resilience /media/foss4g-2026/submissions/FT8ZP7/Pavedness_Grid_ydX9E1R.png Sukanya Randhawa en Road infrastructure is the physical backbone of development, disaster response, and humanitarian access. Yet a consistent, global, and open baseline of road surface conditions has long been missing. We present a first-of-its-kind open dataset mapping road surface type, width, and passability across more than 9 million kilometers of arterial roads worldwide, derived from PlanetScope satellite imagery (2020 & 2024) and enriched with Mapillary street-level imagery. Our fully open-source pipeline integrates scalable remote sensing workflows, spatial databases, and deep learning models to generate multi-scale infrastructure intelligence: • Planetary scale: Road pavedness trends correlate strongly with national development indicators (HDI correlation = 0.65), offering a measurable proxy for infrastructure inequality. • National scale: Mapping unpaved corridors reveals systemic vulnerabilities in trade connectivity and humanitarian access. • Local scale: Case studies from Northeast India, Ghana, and Pakistan show how governance, conflict, and political prioritization shape road development — directly affecting disaster logistics and climate resilience. We also introduce a Humanitarian Passability Index, combining surface type and road width to support operational logistics planning in disaster-prone and conflict-affected regions. The methodology builds on two peer-reviewed studies (2024, 2025), including one forthcoming in Nature Communications, while translating that research into openly accessible, reproducible geospatial workflows for the global community. All outputs are released under an open license, enabling practitioners, researchers, and humanitarian actors to reuse, validate, and extend the dataset for applications in disaster risk reduction, sustainable development monitoring, and infrastructure equity analysis. The data is available on HDX (Humanitarian Data Exchange), helping make it more accessible to humanitarian actors and analysts worldwide. Road network and access data are essential in humanitarian settings, helping responders assess logistics routes, identify access constraints, and better plan the movement of people, aid, and supplies. This talk demonstrates how open-source geospatial technology can transform multi-source imagery into actionable infrastructure intelligence — and how collaboration between the FOSS4G and humanitarian data communities can scale impact globally. false https://talks.osgeo.org/foss4g-2026/talk/FT8ZP7/ Himawari General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 We present an open geospatial workflow that adapts LLMs for structured geospatial extraction from SNS posts and uses optimization to generate timetable aware multi stop rail itineraries with guaranteed route optimality. foss4g-2026-5395-text2transit-from-sns-posts-to-timetable-aware-multi-stop-train-itineraries-with-open-geospatial-tools /media/foss4g-2026/submissions/QVDNDJ/text2transit_session_image_HyifYep.png Takuya Kurihana en Social networking service (SNS) posts increasingly influence urban travel choices, but they are challenging to use directly for multi stop itinerary planning because the information is informal, unstructured, and often distributed across mixed language captions. In train-oriented cities, users may find several places in a single post yet still face the burden of identifying locations, structuring them as geospatial data, and deciding an efficient visiting order. This talk presents an open-general workflow that addresses both problems. First, we use and adapt LLM based extraction to convert unstructured SNS posts (in this talk we focus on extraction of café spots) into structured geospatial point of interest data. The workflow is designed for Japanese and English Instagram style captions. We introduce Segmentation Aware Geospatial Extraction (SAGE), a segmentation based extraction prompting technique, to identify multiple cafe locations from noisy SNS text. This method addresses structural failure modes that prompting alone cannot resolve, especially the segmentation of long, mixed language captions containing multiple points of interest. Second, we use dynamic programming to compute the best visiting order over the extracted locations in a timetable aware rail network. The routing stage is formulated as a traveling salesperson type problem, which allows us to guarantee the optimal route for the extracted set under exact solving conditions. In particular, the routing stage maps extracted points of interest to nearby train stations and builds a generalized travel time matrix that combines walking access with timetable-based train travel. Because train services follow scheduled departures and arrivals, the underlying network is treated as a time dependent graph in which edge availability and travel cost vary with time. The itinerary is then solved as an asymmetric traveling salesperson type problem, where non symmetric costs arise from transfer penalties, service frequencies, and direction dependent travel times. Using an exact optimization method such as dynamic programming or mixed integer programming, the framework guarantees the optimal route for the extracted set under the defined travel cost assumptions. The talk emphasizes how open source LLMs can be used effectively not only for text understanding but also for practical geospatial data structuring, and how optimization provides a principled foundation for reliable itinerary generation. The overall contribution is a reproducible open geospatial workflow connecting SNS text, structured point of interest extraction, station mapping, and optimal rail itinerary planning. false https://talks.osgeo.org/foss4g-2026/talk/QVDNDJ/ Himawari General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 Open-source GIS tools such as QGIS and PostGIS excel at spatial analysis, but in earthquake-prone regions like Japan, layered maps alone are often insufficient. This talk introduces a practical extension that adds relational modeling to existing workflows, improving transparency and decision support in complex disaster scenarios. foss4g-2026-4981-from-map-layers-to-knowledge-models-extending-open-gis Koji Annoura en Open-source geospatial tools such as QGIS, PostGIS, GeoServer, and OpenStreetMap have made spatial analysis accessible to everyone. We can visualize hazards, overlay infrastructure, and perform powerful spatial queries. For many tasks, this is enough. But in disaster-prone regions like Japan, real-world situations are more complex than overlapping polygons. When earthquakes strike, bridges affect evacuation routes. Power stations affect hospitals. Administrative responsibility affects response speed. These are not just spatial intersections—they are chains of dependency. In several disaster modeling projects, I encountered a recurring problem: spatial layers showed *where* things were, but not clearly *how they depended on each other*. Critical relationships were scattered across reports, spreadsheets, and institutional documents. In this talk, I introduce a practical extension to standard open-source GIS workflows that adds relational modeling alongside geometry. The approach keeps PostGIS as the geometry engine and integrates a lightweight graph layer to represent infrastructure dependencies, responsibility links, and cascading effects. This is not a replacement for GIS. It is an enhancement built entirely on open-source components. You will see: - How to link spatial features with dependency structures - How hybrid queries combine spatial predicates and relational traversal - How this improves explainability in disaster-response scenarios - How the approach remains reproducible and open All examples are based on open datasets from a seismically active region in Japan. Code, schema definitions, and sample datasets will be openly released. This session is aimed at practitioners, developers, and researchers who work with open GIS tools and face complex decision contexts. Whether you are building disaster dashboards, infrastructure models, or urban analytics systems, this approach offers a practical way to make spatial reasoning more transparent and structured. false https://talks.osgeo.org/foss4g-2026/talk/7VZ7LE/ Himawari General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 Using cheap 250 g consumer drones introduces challenges when processing imagery at scale. At HOTOSM we have been refining workflows alongside OpenDroneMap to process areas of <100 km² captured using drones such as the DJI Mini series, combining drone-specific correction techniques with scalable Kubernetes-native processing pipelines. foss4g-2026-5373-city-scale-imagery-generation-using-a-handful-of-cheap-consumer-drones Sam Woodcock en Consumer drones have made aerial imagery collection accessible to communities, NGOs, and local governments. However, once image collections reach city scale, processing thousands of photographs into orthomosaics, point clouds, and digital surface models becomes a significant computational challenge. This problem is compounded by the limitations introduced when using lightweight consumer drones designed primarily for hobbyist use. This talk explores how the open-source project ScaleODM enables scalable processing of large drone datasets by orchestrating OpenDroneMap workloads across Kubernetes clusters. By distributing photogrammetry tasks across multiple nodes, ScaleODM allows teams to process large mapping projects using commodity infrastructure while maintaining fully open workflows. Using a real-world example of city-scale drone imagery, this session will demonstrate: - Challenges encountered when processing very large drone datasets - How ScaleODM structures distributed photogrammetry pipelines - Deploying Kubernetes-native processing for OpenDroneMap - Performance considerations and cluster scaling strategies - Specific challenges introduced when processing imagery from lightweight drones such as the DJI Mini series - Lessons learned from running large-scale drone processing in practice The presentation will also discuss how these tools enable humanitarian mapping, urban analysis, and community mapping projects by reducing the infrastructure barriers traditionally associated with large photogrammetry workloads. Attendees will gain a practical understanding of how open-source geospatial tools can scale from small drone surveys to city-wide imagery processing while keeping infrastructure costs accessible. false https://talks.osgeo.org/foss4g-2026/talk/7FML9X/ Dahlia1 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 This framework simplifies STAC data access by using Gemma 3 to translate natural language into queries. It automates GISTDA’s disaster data retrieval and MapLibre visualization, transforming complex geospatial imagery into instant, actionable intelligence. This human-centered AI approach ensures rapid, expert-free decision-making during crises for societal safety. foss4g-2026-5383-title-natural-language-querying-of-stac-catalogs-using-llms-for-geospatial-visualization /media/foss4g-2026/submissions/89D8XX/unnamed_XHqodPR.jpg Pakpoom Najanthom en This work proposes a framework to simplify access to STAC data under the concept of “Bridging Geospatial Technology and Humanity,” with the aim of delivering timely information and support to people, particularly during disaster situations where every second matters. A key element of modern data management is the open-source STAC standard, which can be compared to an “intelligent knowledge repository” that systematically records changes occurring in disaster-affected areas. In Thailand, the Geo-Informatics and Space Technology Development Agency (GISTDA) has adopted this standard as a core component of its disaster management initiatives to organize spatial data related to floods, hotspots, and drought conditions. This approach enables decision-support systems to access critical information more efficiently while significantly reducing the time required for data retrieval. To address the challenge of accessibility, we developed a human-centered AI communication approach that leverages the Gemma 3 large language model (LLM), deployed via Ollama, as the interaction and orchestration layer. The system is designed to receive natural language input from users and analyze both spatial and temporal context. It then automatically translates the request into STAC API-compliant query commands. This process removes the traditional dependency on technical experts and reduces the complexity of geospatial data discovery, allowing users to access information simply through natural-language questions and responses. Once the system retrieves vector coordinates and satellite imagery (raster data) from GISTDA’s data repositories, the information—typically in the form of GeoJSON or metadata—is passed to a dynamic visualization process. Olama performs spatial statistical analysis to determine the event bounding box and generates JSON-based style instructions for MapLibre GL JS, which automatically configures the layer structure using vector tiles. This enables the creation of interactive maps that can instantly visualize geospatial information through a simple prompt. The system then overlays risk-related layers, such as flooded areas or thermal hotspots, onto the map along with easily interpretable statistical summaries. As a result, complex geospatial datasets are transformed into clear visual intelligence, enabling decision-makers to quickly and accurately assess ongoing crisis situations. This work demonstrates that when advanced geospatial technologies are combined with a deep understanding of human needs, and mediated through intelligent systems such as Olama, it becomes possible to create innovations that go beyond simply storing data. Instead, these systems can truly “listen to” and “assist” people, ultimately contributing to the safety and long-term sustainability of Thai society in the future. false https://talks.osgeo.org/foss4g-2026/talk/89D8XX/ Dahlia1 General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 Ensuring reliable map tiles is critical for modern web mapping applications. This talk presents how MapLibre approaches tile quality using automated testing and CI pipelines, sharing practical lessons and reusable patterns for open-source geospatial projects. foss4g-2026-4928-ensuring-tile-quality-in-maplibre-through-automated-testing-and-ci Shreeharsh Shinde en Map tiles are the foundation of modern web mapping and visualization, directly impacting correctness, performance, and user trust. As open-source geospatial projects scale in usage, maintaining consistent tile quality across diverse data sources, styles, and rendering scenarios becomes increasingly challenging. This talk explores the quality mechanisms used around MapLibre tile workflows, with a focus on how automated testing and continuous integration help detect regressions early and support long-term project sustainability. We will discuss the different categories of tests used in practice—such as unit tests, integration tests, and rendering-related checks—and how they are combined to validate tile behavior across platforms and environments. Drawing from contributor experience and maintainer insights, the session highlights common failure modes encountered in tile pipelines, trade-offs in test design, and lessons learned when balancing coverage, performance, and developer productivity. The talk emphasizes practical approaches rather than theory, showing how quality checks are integrated into everyday development workflows. The goal of this session is to share actionable insights that other open-source geospatial projects can apply when designing or improving their own testing and CI systems. Attendees will leave with a clearer understanding of how automated quality mechanisms can improve reliability, reduce regressions, and strengthen trust in geospatial software at scale. false https://talks.osgeo.org/foss4g-2026/talk/8TCYWN/ Dahlia1 General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 This talk introduces Kartore, a project centered around a style editor for MapLibre. It covers the motivation and design approach, as well as current editing capabilities, style management approaches, and planned extensions such as sprite and glyph generation, exploring possibilities for improving cartographic workflows. foss4g-2026-5604-kartore-a-style-editor-and-toolkit-for-maplibre Taiyu Yoshizawa en Creating and maintaining map styles for MapLibre can be complex and often involves multiple tools and workflows. Kartore is a project centered around a style editor for MapLibre, designed to improve the experience of creating and managing map styles. In this talk, I will present the motivation behind developing Kartore as an independent project, including observations from existing workflows and considerations for making styling more streamlined and manageable. The session will introduce the design and architecture of the editor, followed by a demonstration of its current capabilities, including practical workflows for style editing and managing style configurations effectively. This talk is aimed at developers and cartographers working with MapLibre who are interested in improving their styling workflows. In addition, I will discuss ongoing and planned extensions, such as sprite and glyph generation, and how these features aim to support a broader range of cartographic workflows. The project is being developed openly, with its implementation available on GitHub ( https://github.com/Kartore ), and aims to contribute to the broader open geospatial community. Finally, I will share lessons learned from building a tool in the open geospatial ecosystem and outline future directions. false https://talks.osgeo.org/foss4g-2026/talk/8VJF3X/ Dahlia1 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 Maplibreum: framework that makes the creation of web maps quite easy in a Python environment, making it well suited for interactive tools. Similar to Folium, but using the ever-growing MapLibre instead of Leaflet. I will present my motivations and the development of this new contribution to the FOSS4G community! foss4g-2026-4967-maplibreum-like-folium-but-based-on-maplibre Kauê de Moraes Vestena en As a cartographer, I began my career already in the digital era, and I have always been fascinated by beautiful web maps and their power to reveal data insights that go far beyond traditional paper-based cartography. Naturally, I became interested in creating my own. My first attempt was with the qgis2web plugin, which introduced me to Leaflet and OpenLayers. I quickly realized that if I truly wanted to go beyond templates, I would need to learn how to code it. Although I had some programming experience, I considered myself — and still consider — an average developer. JavaScript felt intimidating at the time (this was the pre-AI era, when Stack Overflow was the primary lifeline). During that period, Jupyter Notebooks and Google Colab were becoming very popular, and that’s when I discovered Folium. It was love at first sight. With just three lines of Python code, I could generate a full-fledged HTML file containing all the JavaScript, CSS, and markup that previously felt so foreign to me. From there, I started exploring the colorful world of interactive features: clickable tooltips with charts, hover highlighting, search bars — the full package. I built my first prototype for a project called OpenSidewalkMap, which focused on sidewalk geometries. I began with a small dataset — just eight street blocks — and it looked absolutely beautiful. But who wants only eight blocks when you could map an entire city? That’s when I learned about scaling. To handle large datasets efficiently, you need tiles — vector tiles, specifically. And that’s when the tiling nightmare began. Leaflet is fantastic, but vector tiles are not exactly its strongest feature. I experimented with several adaptations, none of them fully satisfying. By that point, I had already heard about MapLibre and its highly optimized rendering engine. I also discovered the relatively new PMTiles format, which fit perfectly with many of my projects and integrates seamlessly with MapLibre. So I migrated. Today, I write MapLibre code "directly". But what about the “old Kaue,” who just wanted to create a web map with three lines of Python? That question led to MapLibreum. Well — not exactly me alone. If you look at the official Folium repository, you’ll see more than 160 contributors. Building something similar as a solo developer would be unrealistic. So I took a different path: I used AI agents to help me do the work. What started as pure curiosity — a side project I would never otherwise have had time to pursue — evolved into an important laboratory for learning how to effectively use AI agents in large, real-world projects. And in our presentation, I’ll tell you all about it. false https://talks.osgeo.org/foss4g-2026/talk/DMYBML/ Dahlia1 General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 Cogniscape is an open source iOS and Android app combining OpenStreetMap pedestrian data, spatial audio beacons, and on device AI obstacle detection for eyes free navigation. This talk covers the OSM data pipeline, offline routing, binaural audio, and real time computer vision for visually impaired users. foss4g-2026-5573-eyes-free-navigation-osm-powered-spatial-audio-and-ai-navigation-for-visually-impaired-users Subhash Dulla en Most navigation apps are built for people who can see the screen. Cogniscape is built for people who cannot. It is an open-source iOS and Android application that combines OpenStreetMap pedestrian data, 3D spatial audio beacons, and on-device AI obstacle detection — enabling visually impaired users to navigate real-world environments entirely without screen interaction. The spatial audio layer is inspired by Microsoft's SoundScape research. Audio beacons placed at destinations and waypoints shift left, right, or ahead based on the phone's compass bearing. Face your destination and the sound comes from in front. Turn away and it drifts behind you. The user always knows where they are going — by listening, not looking. On top of this, a second AI layer continuously scans the camera feed and delivers immediate directional audio warnings when obstacles — steps, poles, parked vehicles, uneven surfaces — are detected ahead. Both layers run fully on-device with no network dependency, preserving privacy and full offline capability. The talk is structured in three parts: Part 1 — OSM as an audio data source. Accessibility-relevant OSM tags — sidewalk, crossing, tactile_paving, kerb, surface — are inconsistently mapped globally, with South Asian cities among the least covered. I walk through how OSM data is extracted via Overpass API, transformed into a pedestrian routing graph, and enriched with POI data for beacon placement. Building Cogniscape exposed specific data quality gaps, and I describe the targeted mapathon workflows we designed to address them at a community level. Part 2 — Offline architecture, spatial audio, and on-device AI. Routing uses embedded GraphHopper with OSM PBF extracts. Tiles are served via MapLibre Native with MBTiles offline packs. The audio engine uses Android Oboe and iOS AVAudioEngine with HRTF approximation for binaural rendering, with azimuth computed from compass and accelerometer sensor fusion. Obstacle detection runs via TensorFlow Lite on Android and Core ML on iOS — same model, two runtimes, no cloud calls. Part 3 — Cross-platform design for non-visual interaction. Shipping on both Android and iOS required reconciling TalkBack and VoiceOver interaction models, two spatial audio APIs, and two on-device ML runtimes — while maintaining a single shared OSM data and routing layer. I cover the architectural decisions that enabled cross-platform parity and what building for blind users teaches any developer about accessible geospatial tool design. Open source projects: OpenStreetMap, Overpass API, GraphHopper, MapLibre Native, Android Oboe, TensorFlow Lite, OSMAnd. false https://talks.osgeo.org/foss4g-2026/talk/QMWJTZ/ Dahlia1 General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 “Find me sunny buildings near Hiroshima Station.” This may sound easy for AI, but handling spatial relationships and 3D information such as which way building surfaces face is not straightforward. This talk presents a portable DuckDB-based approach to 3D city model search that combines spatial, semantic, and geometric cues. foss4g-2026-5649-portable-spatial-semantic-rag-for-3d-city-models-using-duckdb Ryosuke Aoki en **The Problem** Imagine asking: “Find me sunny buildings near Hiroshima Station.” It sounds simple, but it actually involves two different problems. The first is a spatial problem, such as “near Hiroshima Station.” Standard RAG can capture textual similarity, but it cannot handle spatial relationships such as distance, containment, or area-based search. For example, if you ask for buildings near Hiroshima Station, it treats `near` as just another word. The second is a 3D problem, such as “sunny.” To answer this, we need some understanding of which way building surfaces face. Project PLATEAU, Japan’s urban digital twin initiative, provides detailed 3D building data in CityGML, including semantic surface labels for walls, roofs, and ground surfaces. In practice, PLATEAU data is often converted to simpler formats like GeoPackage so it works in standard GIS tools. In this process, all surfaces of a building are merged into one geometry — which is much easier to work with, but those surface type labels are lost along the way. **Our Approach** We built a portable workflow that handles both spatial and semantic search in a single pipeline. To address the first problem — spatial relationships such as “near Hiroshima Station” — we use DuckDB with its `spatial` and `vss` extensions so that geometry, attributes, embeddings, and indexes can be managed in a single `.duckdb` file. This allows geocoding, location-based filtering, and semantic ranking to be handled in one flow, without setting up a separate database server. To address the second problem — conditions related to 3D building shape, such as “sunny” — we added a step that recovers lost surface information from geometry. More specifically, we analyze each polygon face in the 3D geometry, compute a normal vector, classify each face as roof, ground, or wall, and estimate wall direction. We then encode this recovered information — together with attribute data — into searchable building descriptions and use them to generate embeddings. This face-level step is implemented by combining DuckDB with Python-based geometry processing. As a result, the overall workflow remains portable without requiring a separate spatial database server. **What This Talk Covers** We validated this workflow on PLATEAU data covering the area around Hiroshima Station. The talk introduces the full workflow, shares what worked well and what was difficult, and discusses how this approach can be used in practice. false https://talks.osgeo.org/foss4g-2026/talk/ER7ZFX/ Dahlia1 General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 geo-base is a self-hostable open-source geospatial platform that combines a tile server, a management dashboard, and an MCP connector, enabling small teams to manage and query their own spatial data in natural language without relying on external cloud services. foss4g-2026-5589-a-self-hostable-open-source-geospatial-platform-for-small-teams-with-natural-language-querying-via-mcp Noboru Otsuka en Many small teams working with geospatial data—municipal planners, field surveyors, local governments, and non-profit organizations—face a common problem: they collect and maintain geographic data internally, but lack the engineering capacity to build and operate their own spatial data infrastructure. Existing solutions either require significant server administration expertise or push data into external cloud services where teams lose direct control. geo-base is an open-source, self-hostable geospatial data platform designed for exactly this context. It provides a tile server, a web-based management dashboard, and a built-in Model Context Protocol (MCP) server as a single deployable system. Teams can upload raster and vector data in open formats (GeoTIFF, PMTiles, GeoJSON), preview and manage tilesets through a browser interface, and query their data using natural language via MCP-compatible AI clients—without sending data to third-party services. The entire stack is built on open-source components: a tile server API, a web-based management dashboard, an MCP connector layer, a spatial database, and a browser-based map renderer. The system is designed to be deployable by a single person with basic command-line familiarity, without dedicated infrastructure or database administration staff. This talk shares the motivation behind geo-base, the design decisions that prioritize operational simplicity over feature breadth, and honest reflections on where the approach works well and where it falls short. The session includes a live demonstration showing how a non-technical team member can upload a dataset and immediately query it in natural language through an AI agent. We hope this sparks discussion on how the open-source geospatial community can better serve small, non-IT organizations as primary users. false https://talks.osgeo.org/foss4g-2026/talk/FYY87V/ Dahlia2 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 Maplat is an open-source platform enabling bidirectional coordinate transformation between historical or illustrated maps and modern maps. Celebrating its 10th anniversary, this talk covers the project's technology, real-world applications, and philosophy — including ongoing developments and open challenges we would love to explore together with the community. foss4g-2026-5518-bringing-old-maps-illustrated-maps-to-the-web-10-years-of-maplat-and-turning-misalignment-into-innovation KOHEI OTSUKA en Historical maps and hand-drawn tourist illustrated maps are difficult to handle with conventional GIS tools due to their distortion, scaling, and rotation. Maplat addresses this through a proprietary coordinate transformation engine built on Japanese-patented technology, achieving high-accuracy bidirectional coordinate transformation between any such map image and modern maps — within 2D space. ## 10 Years of Maplat: Where We Came From and Where We Stand * Why conventional GIS tools struggle with historical and illustrated maps * Real-world use cases (Niigata Kagai Project and others) * Current state of development and commercialization by Nayuta Inc. ## Turning "Misalignment" into a Positive Force Historical and illustrated maps never align perfectly with modern maps. Rather than treating this as a defect, Maplat embraces it as meaningful information — a reflection of the era, purpose, and culture in which each map was created. By transforming this misalignment into a bidirectional coordinate transformation system within 2D space, Maplat has delivered a practical solution for historical GIS, cultural heritage, and tourism applications. This philosophy is at the heart of Maplat's innovation. ## Future Directions and Open Challenges * Ongoing development including expanded support for vector data * The exciting yet unsolved challenge of 3D extension — the "misalignment as a feature" approach that works elegantly in 2D breaks down when carried into 3D space, and this remains a fundamental open question * We would love to hear ideas and insights from the audience on how to move forward Maplat is still a relatively unknown project, but for those interested in humanities-oriented GIS — history, culture, and tourism — and for anyone who sees potential in embracing the "imperfection" of maps, we hope this talk sparks an inspiring discussion. false https://talks.osgeo.org/foss4g-2026/talk/33TQQC/ Dahlia2 General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 Reproducibility in geospatial science is hindered by opaque data, proprietary software, and complex machine learning workflows. This talk highlights challenges in deep learning reproducibility and presents practical strategies, tools, and documentation practices to create transparent, repeatable experiments using open‑source technologies. foss4g-2026-5204-reproducibility-in-geospatial-research-a-case-study Rosa Aguilar en Computational reproducibility is the ability to obtain consistent results from the original work using the same input data and methods, and to conduct them by different researchers. To conduct the experiments, the input data and methods must be transparent. However, transparency and reproducibility remain a challenge across several research domains, including geoscience, hindering trust in findings. In the field of geospatial science, numerous algorithms, such as statistical analysis and machine learning, are used to analyse and extract valuable insight from geospatial and remote sensing data. This analysis can be performed by using GIS software or through custom coding. While sharing codes and datasets may appear sufficient for reproducing the research, the researchers may still encounter compatibility issues when executing the code, e.g., incorrect file paths, missing libraries, or computational environment issues. In addition to these technical barriers, factors that hinder the reproducibility are the unavailability of data and code, using proprietary software, and time required to reproduce others’ work. Hence, open-source technologies are crucial for implementing reproducible workflows. Recently, machine learning, especially DL, has been applied to numerous geoscience studies. However, ensuring reproducibility is more challenging in this context due to their complexity and many components or configurations are involved, which can exhibit non-deterministic behaviour.  Some of the main challenges in machine learning that affect reproducibility are model uncertainty and the training method. In this talk, we discuss how to mitigate these challenges and explore best practices for documenting model architecture and computational environments. We describe our experience using open-source technologies and best practices for reproducibility while developing a deep learning workflow for a case study. false https://talks.osgeo.org/foss4g-2026/talk/8JWRBE/ Dahlia2 General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 Geospatial data powers critical decisions . TrustChain is an open-source implementation of the OGC IPT framework that makes trust computable: every dataset carries a cryptographic fingerprint proving its origin, integrity, and provenance. The data answers that question itself. foss4g-2026-5246-trustchain-implementing-the-ogc-identity-provenance-trust-framework-as-an-open-source-geospatial-pipeline Pajarinee Songthammarat en A government agency receives a flood risk map. The analysis looks credible. The colors are clear. But can anyone in that room answer: Where exactly did this data come from? Has it been altered since collection? Who validated it, and how? If the answer is we trust the vendor that is confident without evidence. Geospatial professionals have spent decades improving how we process, analyse, and visualise spatial data. One fundamental question remains largely unsolved in practice: how do we make trust in geospatial data something that can be proven, not assumed? This is not a theoretical problem , it is a governance one. When a flood early-warning system triggers an evacuation, the official signing that order needs to know the risk map is verified, not just plausible. When a land parcel platform determines ownership, the affected community needs to know the boundary data is traceable, not just available. When a supply chain certifies deforestation compliance, the regulator needs to know the provenance is auditable, not just claimed. In each case, the geospatial data exists. What is missing is proof that it can be trusted. TrustChain is an open-source implementation of the OGC Identity–Provenance–Trust (IPT) framework — Trust = Identity + Integrity + Provenance built as a working geospatial pipeline. Identity proves the source cryptographically. Integrity guarantees nothing has changed since collection. Provenance traces every processing step back to the original sensor. Every dataset leaving the pipeline carries a blockchain-anchored cryptographic fingerprint — independently verifiable by anyone, anywhere. The data answers that question itself. false https://talks.osgeo.org/foss4g-2026/talk/NPZLCH/ Dahlia2 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 Invasive canopy-smothering vines such as Merremia peltata threaten tropical forests but are difficult to map under persistent cloud cover. This talk presents a SAR-based workflow using Sentinel-1 and open-source geospatial tools to detect and map infestation across Pacific islands, supporting reproducible, transferable monitoring for management. foss4g-2026-5546-mapping-invasive-canopy-smothering-vines-in-the-tropical-pacific-using-sar-and-open-source-geospatial-tools Iosefa Percival en Merremia peltata is an invasive canopy-smothering vine that spreads rapidly through tropical forest and is difficult to map reliably with optical imagery alone, especially in persistently cloudy regions. This talk presents a SAR-based approach for detecting and mapping vine infestation in selected areas across the Pacific using freely available Sentinel-1 data and open-source geospatial tools. We use grey-level co-occurrence matrix (GLCM) textural metrics derived from SAR imagery to characterize canopy pattern and structure associated with vine-infested forest and to distinguish infestation from surrounding vegetation. The workflow is implemented with open-source geospatial software and is designed to be reproducible and scalable across multiple sites. Results show that SAR texture metrics can identify infestation patterns that are difficult to detect consistently with optical imagery alone, while also improving monitoring through more complete time series in cloud-prone regions and supporting more timely management responses through higher temporal coverage. The talk will show where the approach performs well, where confusion remains, and what this means for operational vegetation mapping in the region. false https://talks.osgeo.org/foss4g-2026/talk/77WDWM/ Dahlia2 General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 Apache SIS is a Java library for metadata, referencing, feature and grid coverage services with a focus on implementing OGC/ISO abstract models. This talk shows how SIS can handle some non-trivial cases such as non-linear localization grids and rasters crossing the anti-meridian. foss4g-2026-5540-apache-sis-for-integrated-metadata-referencing-and-grid-coverage-services Martin Desruisseaux en Apache Spatial Information System (SIS) is a Java library for geospatial applications with a focus on the implementation of the standards published jointly by the Open Geospatial Consortium (OGC) and the International Organization for Standardization (ISO). While the most widely-known standards are data formats or web services, they are often derived from abstract models defined by other OGC/ISO standards in the form of Unified Modelling Language (UML). Apache SIS uses these abstract models for some of its API, as a way to inherit a fraction of the insights from geodesists, scientists and other members of the expert groups who designed them. In the past, this approach saved GeoTools and Apache SIS (which can be seen as a continuation of GeoTools for the referencing service part) from the temptation to use a universal hub for coordinate transformations, and for the future this approach is guiding the support of epoch in dynamic datums. Apache SIS provides metadata, referencing, features and grid coverage services. In SIS, these services are closely integrated. For example, ISO 19111 (referencing) depends on ISO 19115 (metadata) for describing the authority and domain of validity, and on ISO 19157 (data quality) for describing the positional accuracy. Apache SIS provides a nearly complete implementation of ISO 19115, which can be useful alone (for catalogues), but this implementation is also tuned for being a convenient support for ISO 19111, features and grid coverages. Another example is the resampling of a raster that crosses the anti-meridian. Identifying which values need to be shifted by 360° requires an exchange of information between the referencing and coverage modules, especially when the longitudes to shift are buried under map projections. This talk will present the services offered by Apache SIS, show how the API is designed from the ground for multi-dimensional CRS and data-cubes, and how it handles some difficult cases such as resampling a raster where pixel coordinates are defined by a non-linear localization grid. This talk will also show how Apache SIS takes inspiration from Java Advanced Imaging (JAI), without depending on it, for implementing deferred tile computations on top of the standard Java2D API. For example, a BigTIFF file can be opened as if the RenderedImage was fully in memory. Then chains of operations, such as resampling the image to a different CRS, are applied as if the full images were manipulated in memory. But actually only the requested tiles are resampled, which in turn read only the needed tiles from the file (a classical pull model), with in turn may be read thought HTTP range protocol or from S3, thus allowing some work on the cloud. The talk will also show how SIS leverages the standard Java stream API for giving access to the data of a PostGIS database, how some operations on the stream are converted to SQL statements, and how SIS handles complex features. false https://talks.osgeo.org/foss4g-2026/talk/3CEATB/ Dahlia2 General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 Japanese map culture has evolved through a pursuit of intuitive map designs as sophisticated infographics. We are redefining this cultural legacy within the digital space using FOSS4G technology to ensure its inheritance and evolution for the next generation. foss4g-2026-5191-japanese-map-culture-inheritance-through-foss4g-technology Ayumi Shibamoto en During the 1990s, the Japanese map industry developed unique designs to intuitively convey information within limited spaces. By perfecting techniques such as purposeful exaggeration, simplification, and strategic color schemes - treating the map as sophisticated infographics - a distinct Japanese map culture was established. MAPPLE is now redefining traditional cartography in the digital realm, using FOSS4G as our technological foundation. Maps possess the power to solve social issues; we aim to unlock their inherent "intuitive clarity" through digital means, linking their potential to a better future. Furthermore, we will actively contribute to the FOSS4G community through these initiatives. false https://talks.osgeo.org/foss4g-2026/talk/3KU8Y9/ Dahlia2 General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 This work explores how geospatial systems can move beyond map-centric interfaces by transforming complex GIS data into interpretable visual narratives. It introduces the concept of a Spatial Narrative Interface and examines how structured visual explanations can support human understanding and decision-making. foss4g-2026-5328-when-maps-are-not-enough-exploring-spatial-narrative-interfaces-for-geospatial-insight Nattacha Theeranaew en As technology advances and AI becomes increasingly integrated into digital products, the way people interact with information is changing. Modern systems emphasize personalization, automation, and direct answers, delivering results to users rather than requiring them to manually interpret complex data. As a result, user expectations are shifting toward fast, interpretable insights instead of time-consuming exploration. This shift raises an important question: are maps still the most effective primary interface for understanding spatial data? This work investigates how GIS outputs can be translated into visual insights that better support human interpretation and decision-making. It introduces the concept of a Spatial Narrative Interface—an interface design approach that communicates spatial insights through structured visual explanations, such as summarized signals, evidence-based visuals, and contextual maps, rather than relying solely on exploratory map interaction. The concept is explored through a case study and a preliminary usability evaluation examining how different visual representations affect users’ ability to interpret complex spatial datasets. This perspective invites a reconsideration of the role of maps in next-generation spatial platforms, particularly as technologies evolve and users increasingly expect direct, interpretable insights rather than complex exploratory analysis. By examining how visual explanations can support the interpretation of spatial data, this work highlights new possibilities for designing geospatial systems that better support understanding in increasingly data-rich environments. false https://talks.osgeo.org/foss4g-2026/talk/87LWRA/ Ran1 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 We built NASA's SHERPA lunar mission planner as a pure Julia desktop GUI using Dear ImGui and a custom OpenGL wrapper, replacing a clunky React/Julia split. This talk covers the stack, a live demo, and practical advice for building interactive geospatial and scientific applications entirely in Julia. foss4g-2026-5036-building-production-geospatial-desktop-guis-in-julia-at-nasa-with-dear-imgui-and-mirage-jl Zach Booth en When our team needed an interactive planning tool for lunar surface missions, we started with what seemed like the obvious approach: a React web frontend backed by a Julia server. It worked, technically, but the developer experience was painful. Every piece of data had to be serialized over HTTP. UI bugs could live in JavaScript, Julia, or the communication layer between them. Adding a feature meant touching two codebases in two languages. For a small team, the overhead was eating us alive. So we tried something different: build the whole thing in Julia. The result is a standalone desktop application for planning rover routes on the lunar south pole. The GUI renders terrain maps with sun illumination overlays, lets users place and adjust waypoints, and visualizes time-varying data like solar exposure and communications windows. One language, one process, no server. The underlying problem (interactive map-based visualization with custom overlays on top of a computation backend) comes up constantly in geospatial work, and the approach generalizes well beyond aerospace. Dear ImGui (via CImGui.jl) handles all the UI: windows, sliders, buttons, menus, tables. It's an immediate-mode library, meaning every frame your code says "draw a button here, a slider there," and ImGui handles interaction. If your state changes, the UI reflects it next frame. No widget trees, no callbacks, no syncing. Mirage.jl handles rendering. I wrote it because the available Julia OpenGL wrappers were too low-level for rapid prototyping. The API feels like HTML5 Canvas2D: draw_image(), fill_rect(), draw_circle(). It manages shaders and vertex buffers internally so you don't have to. The immediate-mode approach has a real benefit beyond simplicity: there's no retained state to go stale. Your render function is a pure function of your application state, which is simply Julia variables. When we needed a new overlay, it was just a few draw calls in the right place, not a new component wired into a framework. The biggest accelerator was the REPL workflow. Start the GUI from the Julia REPL, use it, close the window, change some code, reopen it. All state persists in the session. This turns GUI development into the same fast iteration loop Julia developers already use for everything else. This stack is general-purpose. Any application that puts an interactive visual frontend on Julia computation could work this way: geospatial viewers, instrument control panels, simulation monitors, data exploration tools. For anyone who's built a QGIS plugin or wrestled with Electron to get a custom map tool out the door, this is a different way to think about the problem. In this talk I'll cover: - Why we moved away from a React/Julia split architecture - How Dear ImGui and OpenGL compose into an application framework in Julia - A live demo building an interactive geospatial visualization - Practical trade-offs versus web-based and existing desktop GIS approaches No graphics programming experience needed. false https://talks.osgeo.org/foss4g-2026/talk/8KDHFG/ Ran1 General session talk 2026-09-02T11:30:00+09:00 11:30 00:30 Somaliland’s electricity network lacks integration, relying on fragmented private grids with limited capacity and high losses. Modeling and simulations analyzed current and future network scenarios, identifying growth constraints and network inefficiencies. Future plans recommend structured upgrades, improved transmission, and better coordination among providers to meet increasing demand efficiently. foss4g-2026-4813-networking-modelling-a-case-for-somaliland-sub-transmission-and-distribution-investment-plan Caroline Akoth en The approach for the modelling and simulations of the existing and future networks of Hargeisa are presented as follows: 1. Model of the existing networks 2. Simulate of the main existing MV networks (3 main ESPs) 3. Design the future sub-transmission network (132/33 kV substations and 132 kV lines) in an optimal way considering the existing and future load centers, as well as future power plants. 4. Design the future distribution network in an optimal way considering the existing network of ESPs, voltage conversion, and the integration to the sub-transmission network. 5. Model the future distribution and sub-transmission networks (5 years and 10 years). 6. Simulate the future networks to evaluate network problems (overloads and under voltages) and review the design and model accordingly. In our demand forecasting process for Hargeisa, due to the specific data constraints encountered, as mentioned above, we opted for the Traditional Method, specifically using an Extrapolation approach. This method primarily relies on historical consumption and generation data to predict future demand, which was data available to the consultant. It calculates the 'average' growth rate over a specified period to project future demand. After CPCS completed data collection, as described above, we observed monthly trends over the two year time period of our dataset and identified no notable anomalies. The minimum energy demand was recorded in either February (SomPower) or March (Indho Power). Our analysis revealed a modest increase of 3% in energy usage for Indho Power, whereas SomPower experienced a 12.4% rise, and TEC saw a significant increase of 25.1%. Considering the varying customer bases of each Energy Service Provider (ESP), the total energy production growth in Hargeisa from 2021 to 2022 was approximately 10.8%. For Mansoor Power, which serves 1,570 customers, we estimated a conservative energy generation of around 920 MWh. Similarly, for Gaafane, with an assumed customer base of 5,000, the energy generation was projected to be around 2,900 MWh. false https://talks.osgeo.org/foss4g-2026/talk/8KBWCM/ Ran1 Lightning talk 2026-09-02T13:00:00+09:00 13:00 00:05 This study evaluates the performance of NVIDIA’s FoundationStereo for extracting high-quality 3D terrain information from high-resolution stereo satellite imagery, compared to conventional template-based matching methods, with a focus on DSM elevation accuracy and shape completeness. foss4g-2026-5578-performance-analysis-of-foundation-stereo-image-matching-for-3d-terrain-extraction-from-high-resolution-satellite-stereo-data sangwon Son en Recently, the launch and operation of high-resolution satellite data, including KOMPSAT-series satellites, microsatellites, and KOMPSAT-7, have been increasing. In particular, high-resolution satellites can acquire stereo imagery through overlapping observations, which is expected to provide significant potential for the acquisition of three-dimensional geospatial information. The extraction of 3D terrain information from high-resolution satellite imagery is performed through automatic matching between stereo image pairs, and various matching techniques have been developed for this purpose. With the recent advancement of artificial intelligence technologies, a variety of deep learning–based approaches have also been introduced in the field of stereo image matching. Among them, FoundationStereo, developed by NVIDIA, has been reported to demonstrate very strong performance. Therefore, this study applies the FoundationStereo method to high-resolution stereo satellite data, including CAS-500 (Korea Land Satellite) and KOMPSAT-series satellite data, in order to evaluate its performance. In the experiments, the quality of the extracted Digital Surface Model (DSM) obtained through stereo matching was compared and evaluated against the results produced by conventional template-based matching methods. In particular, the quality of the DSM was analyzed with a focus on elevation accuracy and shape completeness. false https://talks.osgeo.org/foss4g-2026/talk/3FYXYN/ Ran1 Lightning talk 2026-09-02T13:05:00+09:00 13:05 00:05 Mapillary hosts over 2 billion street-level images as open data. Their quality and capture conditions vary. This presentation introduces filtering methods to remove low-quality or irrelevant images, enabling more effective use of Mapillary imagery. foss4g-2026-5266-finding-the-street-level-images-you-need-in-mapillary Hironori Banno en Mapillary is an open data platform where users upload and share street-level imagery. It contains over 2 billion street-level images from around the world. These images are available as open data (CC BY-SA 4.0) and are widely used in various fields, such as OpenStreetMap mapping and urban environment assessment. Image quality on Mapillary can vary due to the diversity of contributors and capturing devices. The images vary in viewpoint, resolution, weather conditions, and time of capture, and some contain motion blur or are out of focus. Therefore, users need to not only retrieve images from target regions, but also filter out low-quality or irrelevant images using metadata and image analysis techniques, such as semantic segmentation and vision-language models (VLMs). In this presentation, I introduce methods for filtering images and share practical experiences from applying these methods. This session provides hints for using Mapillary imagery more effectively. false https://talks.osgeo.org/foss4g-2026/talk/BSKJ83/ Ran1 Lightning talk 2026-09-02T13:10:00+09:00 13:10 00:05 In earth volume calculation using the spot level method, computational speed was improved by adopting image-based rasterization for the inside/outside determination of the calculation area. foss4g-2026-5198-speeding-up-earthwork-volume-calculation-using-the-point-elevation-method Kashiwagi-EB en The spot level method, a grid-based earth volume calculation technique, has a lower computational cost compared to methods such as TIN-based division and the prismoidal method, and has been adopted in our in-house CesiumJS-based web application for earthwork progress management. However, even when using the spot level method, which is known for its relatively low computational cost, computation time can increase significantly when the calculation target area is vast or when large-scale point cloud data is involved, posing operational challenges. In this presentation, we introduce a technique for reducing computational load while maintaining accuracy, targeting the inside/outside determination of grid points, which has been identified as a bottleneck in spot level method calculations. Specifically, we present results and discussion on the impact on computation time and calculated cut/fill volumes in the spot level method when the ray casting-based inside/outside determination is replaced with a rasterization-based approach using a vertically projected image. In addition, since further speed improvements were achieved by parallelizing the computation on a per-grid basis, we also report on the implementation of the aforementioned rasterization-based determination in our in-house application, as well as the performance gains achieved through parallelization. false https://talks.osgeo.org/foss4g-2026/talk/9MCN3A/ Ran1 Lightning talk 2026-09-02T13:15:00+09:00 13:15 00:05 Volunteers from the FOSS4G 2026 Hiroshima LOC are building a cross-platform event guide app using React Native and MapLibre. The app offers map-based navigation from Hiroshima Station to the venue, interactive indoor floor maps, and session information linked to each floor. foss4g-2026-5045-a-map-first-conference-guide-app-with-maplibre-for-foss4g-hiroshima-2026 Haruki InoueRyosuke Aoki en Members of the FOSS4G 2026 Hiroshima Local Organizing Committee have been developing a mobile application designed to help attendees navigate the conference with ease. Built with React Native and MapLibre, the app runs on both iOS and Android and places an interactive map at the core of the user experience. The app provides three main features. First, it guides attendees from Hiroshima Station to the International Conference Center Hiroshima, offering a clear, map-based route so that newcomers to the city can reach the venue with confidence. Second, it displays indoor floor maps of the conference center and workshop spaces, allowing users to explore the building layout before and during the event. Third, tapping on a floor reveals the sessions and workshops scheduled in that area, making it easy to discover what is happening nearby and plan your day. By combining outdoor navigation and indoor mapping in a single interface, the app aims to reduce the friction that attendees often face when moving between sessions at an unfamiliar venue. All map data and rendering are powered by open-source geospatial tools, staying true to the spirit of FOSS4G. In this lightning talk, I will give a brief demo of the app, share the technical choices behind it, and invite every attendee to install and use it during the conference. false https://talks.osgeo.org/foss4g-2026/talk/A7PKNC/ Ran1 Lightning talk 2026-09-02T13:20:00+09:00 13:20 00:05 An interactive web application that visualizes Tokyo's last train network fading away over time in a firework-like radial display. It combines public transit open data (ODPT) from 17 rail operators to show when and where you can no longer catch the last train home. foss4g-2026-5169-tokyo-last-train-map-visualizing-when-you-can-t-get-home-with-open-data-from-17-rail-operators Hajime Kato en Tokyo's rail network is among the most dense and complex in the world, yet it shuts down every night. The "last train" is a daily constraint for anyone living in Tokyo, but figuring out the exact moment you can no longer get home — considering transfers across multiple operators and lines — is far from straightforward. The Tokyo Last Train Map visualizes this phenomenon through an interactive web application where the last train network gradually disappears over time, rendered in a firework-like radial display. The project originated in 2018 as a static infographic that went viral on social media, and was rebuilt in 2025 as a fully interactive version. The data source is GTFS-format open data provided by the Public Transportation Open Data Center (ODPT). It integrates timetable data from 17 rail operators in the Tokyo metropolitan area, including cross-operator transfer routing. The frontend is built entirely with SVG and JavaScript — no external mapping libraries — as a custom interactive visualization. The project received a Judges' Special Award at the Public Transportation Open Data Challenge organized by ODPT. This lightning talk will introduce the uniquely Japanese urban transit culture of the "last train" while demonstrating how combining open data from multiple sources can produce both practical and visually compelling results. false https://talks.osgeo.org/foss4g-2026/talk/AMBJNZ/ Ran1 Lightning talk 2026-09-02T13:30:00+09:00 13:30 00:05 An open-source geospatial system in Slovenia provides a unified accessibility database and public web viewer for inclusive mobility. It helps municipalities and other decision-makers identify barriers, prioritize interventions, and plan their gradual removal for vulnerable groups. foss4g-2026-5248-from-spatial-database-to-public-accessibility-viewer-open-source-geospatial-support-for-inclusive-mobility-planning-in-slovenia Tomaž Žagar en This lightning talk presents an open-source geospatial workflow for improving inclusive mobility through an accessibility database and public web viewer in Slovenia. The project’s main goal is to improve quality of life in mobility and accessibility for people with disabilities and older adults, while building a unified data framework for vulnerable groups in urban space. The system covers accessibility conditions for several vulnerable groups: mobility-impaired people, blind and partially sighted people, deaf and hard of hearing people, older adults, and, more recently, people with intellectual disabilities. The public viewer provides open access to this information, but its main role is to support municipalities, public institutions, infrastructure managers, and other decision-makers in identifying barriers, prioritizing interventions, and preparing plans for their removal. The talk will focus on the database and the viewer rather than on general project promotion. It will show how an open-source stack supports the full chain from data capture and maintenance to public visualization and decision support. QGIS and QField are used in data workflows, the database is implemented in PostgreSQL/PostGIS, and the public system is built as a React/Laravel web application with web mapping components such as Leaflet, WMS support, and vector-tile-based optimization for large spatial datasets. false https://talks.osgeo.org/foss4g-2026/talk/DSCCXD/ Ran1 Lightning talk 2026-09-02T13:35:00+09:00 13:35 00:05 Introducing the Climate Action Navigator, a FOSS4G website containing high-resolution spatial indicators of actionable insights to support climate action. foss4g-2026-5081-introducing-the-climate-action-navigator Danielle en Introducing the Climate Action Navigator (https://climate-action.heigit.org) from HeiGIT! We are using FOSS4G to create a website with high-resolution spatial indicators, providing actionable insights to communities, organisations, and decision makers. Join us to find out what is on the Climate Action Navigator, how to partner with us, and what we are working on next! The Climate Action Navigator currently includes indicators relating to walkability, bikeability, land consumption, emissions from land use change, and heating emissions. false https://talks.osgeo.org/foss4g-2026/talk/8JZF9W/ Ran1 Lightning talk 2026-09-02T13:40:00+09:00 13:40 00:05 We present a shift from tool-centric to intent-centric wildfire monitoring. By integrating LLMs and VLMs, we eliminate the technical barrier of manual tool selection. This enables any user to utilize satellite data through simple dialogue, achieving accessibility for everyone and bridging technology and humanity. foss4g-2026-5485-intent-centric-wildfire-monitoring-llm-and-vlm-orchestration-for-everyone Dongchul Kim en The Vision: Enabling Accessibility for Everyone - "ForestView AI" was designed to make satellite intelligence accessible to non-experts. However, our initial platform revealed a persistent "Expertise Gap": users were still required to make complex technical decisions regarding which analysis tools to invoke. This barrier prevented non-specialists from effectively utilizing geospatial information. The Evolution: Intent over Complexity - To close this gap, we evolved our system into an "Intent-centric" platform by orchestrating LLM and VLM engines. This is not merely a UI update; it represents a fundamental shift in how humans interact with geospatial data. We ensure the technology adapts to natural human language rather than requiring users to learn technical jargon. Key Innovations: - LLM-Driven Orchestration: The LLM interprets natural language intent and automatically invokes the optimal analysis pipeline from a suite of specialized geospatial tools. - VLM-Driven Visual Interpretation: The VLM performs specialized visual analysis on the results of each diagnostic stage. By examining various analytical outputs, it identifies critical changes and translates visual patterns into structured textual insights. - AI-Synthesized Reports: The LLM synthesizes the VLM’s visual interpretations with quantitative data. Instead of merely listing raw numbers, it generates narrative reports that provide actionable insights and comprehensive diagnostics based on the integrated data. false https://talks.osgeo.org/foss4g-2026/talk/EF3DY8/ Ran1 Lightning talk 2026-09-02T13:45:00+09:00 13:45 00:05 verture Maps Places offers 53 million POIs with a clean taxonomy — but what can you actually do with them? In 50 lines of Python and DuckDB, I turn raw Overture places into a visual neighborhood typology. Before/after, pipeline, and a ready-to-run notebook. foss4g-2026-5657-50-lines-of-python-neighborhood-dna-from-overture-maps-places /media/foss4g-2026/submissions/ERGRGW/light_talk_zAq20sb.png Marija Ercegovac en This is a one-idea, four-minute talk. The idea: every neighborhood has a hidden fingerprint in its POI mix. Overture Maps Places — the new open dataset combining Meta, Microsoft, Foursquare, and OSM sources — gives us 53 million points with a standardized category taxonomy. That makes neighborhood profiling dramatically simpler than raw OSM tagging. I will show one before/after image: thousands of raw Overture points versus the same city colored by discovered neighborhood types. Then one workflow slide: DuckDB spatial query → H3 hexagons → POI feature vectors → UMAP + HDBSCAN → typology map. Then one takeaway: a public Jupyter notebook. Replace the city name, run, done. Stack: Python, DuckDB, GeoPandas, hdbscan, umap-learn, H3. false https://talks.osgeo.org/foss4g-2026/talk/ERGRGW/ Ran1 Lightning talk 2026-09-02T13:50:00+09:00 13:50 00:05 Cloud Native Geospatial formats like FlatGeobuf and GeoParquet allow applications to access geospatial data directly from an object store with no API server maintenance. This session will explain how the Geoconnex project has leveraged such formats to improve the user experience of analyzing hydrological information across the United States. foss4g-2026-5233-improving-water-data-access-with-cloud-native-geospatial-formats Colton Loftus en Many organizations want to share their data in a web accessible way, but do not have sufficient time to maintain APIs. Additionally, generating subsets of data for web access can easily become out of sync. Cloud native geospatial formats like FlatGeobuf and GeoParquet help to solve this by allowing for data access via object storage http range requests and skipping the need for database or server maintenance. This talk will reference how the Geoconnex project, funded by the US Geological Survey, has used FlatGeobuf for storing data pertaining to all hydrologic catchments and mainstem rivers in the entire United States. This has allowed the project to use the same exact file for both internal microservices and frontend applications. Additionally, we will demonstrate how Geoconnex has used GeoParquet to allow for efficient geospatial queries across the entire United States, directly from the browser. These formats have helped to reduce the workload of maintaining data and improve the user experience for end users. false https://talks.osgeo.org/foss4g-2026/talk/GFYMJ7/ Ran1 Lightning talk 2026-09-02T14:00:00+09:00 14:00 00:05 This lightning talk shares how the SHAPE-CAR project in Fiji uses open-source GIS tools to support community-led climate resilience planning, integrating participatory mapping, environmental monitoring, and spatial analysis to inform adaptation decisions in vulnerable island communities. foss4g-2026-4998-integrating-indigenous-knowledge-and-open-source-gis-for-climate-resilience-in-fiji Maloni Vakacavu Siga en Small island communities face increasing climate risks including flooding, coastal erosion, and food insecurity. However, adaptation planning is often limited by a lack of accessible spatial data and tools at the community level. The SHAPE-CAR (Sustainable Health, Agriculture, Protection and Climate Action for Resilience) project in Fiji explores how open-source geospatial tools can support locally driven climate resilience planning. This lightning talk presents how GIS has been integrated into community engagement and planning processes across several Fijian villages. Using tools such as QGIS, open satellite imagery, and participatory mapping approaches, the project supports hazard mapping, land suitability assessments for climate-smart agriculture, and environmental monitoring. Spatial analysis is combined with local knowledge to identify priority adaptation actions, including agroforestry planning, flood-risk awareness, and resource management. The experience highlights how open-source GIS can bridge scientific data and community knowledge, enabling practical decision-making in resource-constrained contexts. The SHAPE-CAR approach demonstrates a replicable model for integrating geospatial tools into community-based climate adaptation initiatives across Small Island Developing States. false https://talks.osgeo.org/foss4g-2026/talk/JDCZGQ/ Ran1 Lightning talk 2026-09-02T14:05:00+09:00 14:05 00:05 Iwfgara is an open-source web-based 3D GIS platform for visualizing numerical weather prediction data and enabling interactive exploration of atmospheric phenomena. foss4g-2026-5170-iwfgara-visualizing-numerical-weather-prediction-data-in-an-open-source-web-based-3d-gis /media/foss4g-2026/submissions/LWHWST/iwfgara_JATCQXj.png Noh Jaehyuk en Iwfgara is a web-based platform that integrates three-dimensional geospatial information with meteorological data to visualize atmospheric phenomena. The system collects numerical weather prediction (NWP) datasets, including the Global Forecast System (GFS) from the National Oceanic and Atmospheric Administration (NOAA) and forecasting data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and processes them in near real time for visualization. In this work, grid-based NWP data are transformed into a structure suitable for integration with a 3D GIS environment. Using an open-source 3D GIS engine, the platform enables interactive visualization of large-scale gridded atmospheric variables such as wind speed, temperature, and humidity directly in a web browser. The system also supports overlaying meteorological satellite imagery and provides vertical analysis tools for exploring atmospheric structures across different altitude levels. This work demonstrates how open-source geospatial technologies can enable efficient web-based visualization and exploration of large-scale meteorological datasets in an interactive 3D environment. false https://talks.osgeo.org/foss4g-2026/talk/LWHWST/ Ran1 Lightning talk 2026-09-02T14:10:00+09:00 14:10 00:05 A reproducible open-source workflow for analyzing urban growth in Indonesia, combining satellite data, machine learning, and accessible tools for practical, real-world geospatial analysis. foss4g-2026-5599-a-reproducible-open-source-workflow-for-urban-issues-in-indonesia-case-study-urban-sprawl /media/foss4g-2026/submissions/QPQKQJ/Digital_Earth_8qhrOMG.png Victor Christian en This talk presents a practical approach to analyzing urban sprawl using open-source geospatial tools in a reproducible workflow. The focus is not on introducing new algorithms, but on demonstrating how existing tools can be effectively combined to address real-world challenges. The session will walk through a simplified workflow built on Open Data Cube and Python in a Jupyter Notebook environment. It will cover key steps including satellite data preparation, classification using Random Forest, and the use of spatial metrics such as Shannon Entropy and Urban Expansion Intensity Index (UEII) to interpret urban growth patterns. Using a case study from Indonesia, the talk highlights how this workflow can be applied in regions where technical and computational resources are limited. Special attention is given to reproducibility, showing how the process can be adapted and reused in different contexts. In addition, the talk will briefly discuss practical challenges encountered during implementation, including data handling, model limitations, and workflow simplification, along with strategies used to address them. This makes the session relevant not only for researchers, but also for practitioners and students interested in applied geospatial analysis using open-source tools. false https://talks.osgeo.org/foss4g-2026/talk/QPQKQJ/ Ran1 Lightning talk 2026-09-02T14:15:00+09:00 14:15 00:05 Presenting a webapp to track your location on a photo of a physical map foss4g-2026-4979-you-are-here-but-what-if-i-m-not Leo Ghignone en Those big signs with a map and a "You are here" dot can be quite useful when navigating a park, city, or new location in general; they could be even more useful if you could carry them around with you and see your location moving on the map! This talk will be a quick presentation of a new webapp designed to do exactly that: after adding 3 calibration points the gps location can be transformed to image coordinates and shown directly on the map. More points can be added to manage maps that are not drawn to scale. The app is implemented in Svelte and designed as a PWA (Progressive Web App), meaning it can be used from a browser or installed to a smartphone, and it can work completely offline as well. This is an eaely look into a personal project that will hopefully grow larger in future. false https://talks.osgeo.org/foss4g-2026/talk/RUSQQV/ Ran1 Lightning talk 2026-09-02T14:20:00+09:00 14:20 00:05 This study proposes an automated slope estimation method using cross-sectional profiles derived from large-scale point cloud data to reduce operator dependency and improve reproducibility, and demonstrates its practical applicability for the safety inspection and maintenance of retaining walls. foss4g-2026-5557-automated-slope-estimation-of-retaining-walls-using-drone-photogrammetry-point-clouds /media/foss4g-2026/submissions/TMHDKA/session_image_QT9v2HP.PNG hyeonjeong Jo en The deterioration of retaining walls and slopes leads to a reduction in structural stability, causing various negligent accident such as collapse and rockfall. Thus, the need for continuous management and precise condition assessment of such structures has been increasing. Recently, drone-based photogrammetry has enabled efficient data acquisition in sites that are difficult or limited to access, or in areas requiring more manpower than necessary, and has been actively applied, particularly in environments such as retaining walls and slopes. However, the large-scale point clouds generated from drone photogrammetry are difficult to handle. In particular, slope, which represents the structural characteristics of retaining walls and slopes, can still incur errors in analysis results depending on the operator’s experience and subjective judgment, and accordingly, objective calculation standards are required. Therefore, this study proposes an automated procedure for slope estimation by generating cross-sectional profiles based on arbitrary points within large-scale point cloud data, and evaluates the applicability of the proposed method by applying it to an actual reinforced retaining wall. The experimental results indicate that the proposed automated slope estimation method minimizes operator dependency and provides reproducible slope estimation results even in complex field conditions, thereby demonstrating its potential to contribute practically to the field of safety inspection and maintenance of retaining walls. false https://talks.osgeo.org/foss4g-2026/talk/TMHDKA/ Ran1 General session talk 2026-09-02T14:30:00+09:00 14:30 00:30 An in-depth look at GeoServer’s support for Mapbox Vector Tiles, covering recent enhancements, flexible styling mechanisms, and performance optimization techniques such as feature consolidation. The session demonstrates generating high-quality, multi-projection base maps using OpenMapTiles and Planetiler, offering practical guidance for scalable, production-ready vector tile workflows. foss4g-2026-5576-vector-tiles-and-geoserver-dynamic-vector-tiles-server-xyz-services-and-base-maps Andrea Aime en Mapbox Vector Tiles have become a widely adopted format for delivering geospatial data, enabling dynamic rendering and rich interactivity in modern web mapping applications. Although not an official OGC standard, their open specification has made them a cornerstone of contemporary web cartography. This presentation examines the evolving capabilities of GeoServer in serving Mapbox Vector Tiles, with a focus on recent enhancements and practical best practices. It will demonstrate how GeoServer leverages flexible styling mechanisms to precisely control tile content, enabling efficient and tailored data delivery. Key configuration options—including label point generation, attribute selection, and feature consolidation—will be discussed as essential tools for optimizing tile output. The session will also provide actionable guidance for streamlining vector tile generation, supporting the development of scalable and maintainable workflows. Finally, the presentation will explore the use of vector tiles as a foundation for generating high-quality base maps across multiple coordinate reference systems. Using OpenMapTiles styles in combination with Planetiler, it will showcase how to produce visually consistent, multi-projection base maps suitable for a wide range of applications. This talk is intended for practitioners seeking to build advanced web mapping solutions or generate custom base maps, offering practical insights into maximizing GeoServer’s vector tile capabilities. false https://talks.osgeo.org/foss4g-2026/talk/GB9MYE/ Ran1 General session talk 2026-09-02T15:30:00+09:00 15:30 00:30 We use LLM to extract and structure geospatial data buried in 100K–1M+ PDF and Office files held by Japan's MLIT, enabling visualization, spatial analysis, and evidence-based policymaking — demonstrated through real-world use cases, no coding required. foss4g-2026-5094-awakening-dormant-geospatial-data-structuring-large-scale-government-documents-with-llm Kazuma Tsuchiya en By combining LLM-based structuring with spatial joins, we achieve robust data integration that goes beyond simple text matching. ### Key features: - Batch structuring and high-speed parallel processing of PDF, Excel, Word, and PowerPoint files using LLM - Data cleansing, geocoding, and spatial joins to reconstruct documents as geospatial data - Spatial analysis and visualization leveraging the rich geographic density unique to MLIT datasets - End-to-end pipeline from data extraction through anonymization to open data publication ### Who Should Attend: - Government and municipal officials interested in digital transformation and data infrastructure - Researchers and think tank professionals involved in EBPM - Data engineers, GIS developers, and no-code/low-code developers - Startups and corporate representative working on projects that utilize open or public data false https://talks.osgeo.org/foss4g-2026/talk/ZNYKFH/ Ran1 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 In this presentation, we will try to understand the relevance of geospatial metadata in the modern technological landscape, and we will reflect on their usefulness. foss4g-2026-5084-metadata-in-the-time-of-robots Antonio CercielloJoana Simoes en Having good (GIS) metadata is crucial for your data and services. Especially if you care about the FAIR principles. And even though no one enjoys creating it. First, what is metadata, and what is it for? What has changed with the advent of the latest technological trends? Are they still relevant as they once were? Do the latest emerging standards make them easier to create and use? Are we creating them in the right way? Are we putting them in the right place? And, the inevitable question, can LLM tools help us with the tedious task of creating metadata, consuming a reasonable amount of CO2? In this presentation, we will try to reflect on this topic together, trying to understand which is the right direction. false https://talks.osgeo.org/foss4g-2026/talk/TCFZAE/ Ran1 General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 CesiumJS's built-in APIs hit CPU bottlenecks when rendering massive dynamic objects in real time. This talk presents how we used Vertex Texture Fetch (VTF) to store position data in GPU textures, enabling real-time updates without geometry reconstruction, applied to a satellite trajectory monitoring system. foss4g-2026-4982-rendering-massive-moving-points-at-60fps-on-cesiumjs-real-time-dynamic-vector-rendering-with-gpu-textures Minjeong, KIM en We needed to render thousands of satellites moving in real time on CesiumJS, each leaving a trail of trajectory points. The built-in API rebuilds vertex buffers on the CPU every time positions change, making it fundamentally unsuitable for large numbers of dynamic objects. Vertex Texture Fetch (VTF) stores position data in GPU textures instead of vertex attributes, and samples them in the vertex shader to retrieve positions. Since the geometry only holds texture UV coordinates, it can be created once and reused. When objects move, only the texture pixels are updated. Thanks to this structure, the update cost remains nearly constant regardless of how many objects are moving simultaneously. We are currently using this technique in a space surveillance system, rendering satellites with continuously changing orbits alongside massive numbers of point and polyline trails in real time. The talk will cover how the VTF pipeline works, how it differs from the traditional vertex attribute approach, and how we achieved 64-bit precision in a WebGL 32-bit float environment using High/Low splitting, — achieving over 150% frame rate improvement — with a demo showing the difference. false https://talks.osgeo.org/foss4g-2026/talk/9KACSL/ Ran1 General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 PLATEAU is one of the world's most detailed 3D city model datasets — and one of the hardest to work with. This talk walks through a solar potential analysis workflow on PLATEAU data: CityGML challenges, the transformation pipeline, and turning a one-off analysis into a reusable spatial workflow. foss4g-2026-5183-solar-potential-analysis-on-japan-s-plateau-3d-city-model-a-visual-etl-workflow /media/foss4g-2026/submissions/8MAGEX/flow-solar_XM5Bjg9.png Kyle WaiteBill Cook en Urban solar potential analysis sounds straightforward: take 3D building geometry, calculate roof surface orientation and area, factor in irradiance data, output a map. In practice, working with real-world CityGML data — especially Japan's PLATEAU dataset — surfaces every hard problem in spatial ETL at once. This talk is about building that workflow honestly: where pipelines break, how you design transformations that survive contact with real data, and what it looks like when it finally works. The Data Challenges - Parsing and flattening nested CityGML building attributes across large LOD2 datasets - Coordinate system handling — JGD2011/EPSG:6668 and the precision issues that follow - Extracting and classifying roof surfaces for irradiance calculation - Joining geometry with solar irradiance reference data at scale - Managing intermediate and output volume for a whole city district The Workflow We'll walk through the pipeline stage by stage — not as a clean success story, but as a real engineering process. Where did we need a transformation type that didn't exist yet? Where did intermediate data look completely wrong until we found a subtle coordinate issue? Each stage will be visible as a node graph, with data inspectable at every step — geometries, attributes, intermediate outputs — so the audience can follow the logic without reading code. The Output — and What It Represents The final stage produces solar potential scores per building surface, visualized directly within the workflow — geometries coloured by score, attributes inspectable in place. But the more valuable output is the workflow itself. Every transformation step is documented, configurable, and reusable. Swap the city district, update the irradiance reference data, re-run. What started as a one-off analysis becomes a repeatable spatial data pattern worth sharing with the community. What You'll Take Away A practical map of the pain points in PLATEAU/CityGML-based analysis workflows, and approaches that work A mental model for designing reproducible, visual ETL pipelines for complex geospatial data A look at what browser-native, collaborative workflow tooling can do for this class of problem Who Should Attend Geospatial developers, GIS analysts, and data engineers working with 3D city models, CityGML, or large-scale urban datasets — and anyone interested in making spatial data pipelines more reproducible, inspectable, and shareable. false https://talks.osgeo.org/foss4g-2026/talk/8MAGEX/ Ran1 General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 In this talk, I'll try to explain the metadata problem in geospatial data and why things like STAC are useful but cannot always solve your problem. foss4g-2026-5044-the-metadata-problem Batuhan Kavlak en The famous work Metaphysics belongs to Aristoteles. For sure, in philosophy, there are always discussions. According to common acknowledgement, the book's title was given by Andronicus of Rhodes as "ta meta ta physika." In ancient Greek, this literally translates to "the [books] after the physics [books]." It wasn't a profound philosophical title; it was essentially a librarian's organizational sticky note. "Meta" simply meant "after." For sure, some would disagree, but this snippet of information tells us something. Metaphysics is mostly perceived as knowledge beyond physics, has been mystified and even neglected in favor of positive science. But the term "meta" is mostly used to mean something of something, such as metadata, which means data about data. Therefore, it can be about physics of physics as well. The question is then, what is metadata? Data about data or data beyond data? In this talk, I'll try to explain the metadata problem in geospatial data and why things like STAC are useful but cannot always solve your problem. We'll see the data about data and beyond by understanding the origins of geospatial data and the tools that explain it better. By the way, many people think Aristoteles didn't write anything in his life, but that's not what this talk is about. I'm also not an expert in either philosophy or geospatial. I'd like to conduct random readings and will present my findings. false https://talks.osgeo.org/foss4g-2026/talk/A3UL3S/ Ran2 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 Visualizing national climate projection data in the browser requires going beyond standard raster layers. We present maplibre-gl-shader-layer, an open source TypeScript library enabling custom WebGL shaders on MapLibre GL JS — demonstrated through Météo France climate datasets rendered with high-precision multi-channel tile encoding and configurable colormaps. foss4g-2026-5636-rendering-national-climate-data-in-the-browser-webgl-custom-shaders-with-maplibre-gl-js Florent Gravin en How do you visualize decades of high-resolution national climate projection data — wind speed, solar radiation, temperature, degree days — directly in a web browser, with smooth rendering and precise data picking, all built on open source tools? At Camptocamp, we tackled this challenge during the Météo France hackathon in Toulouse, where teams were given access to beta climate projection datasets from Météo France and the DINUM. Our goal: build a web application helping the renewable energy industry assess regional potential under different climate warming scenarios, aligned with the French government's TRACC adaptation framework (+2°C, +2.7°C, +4°C milestones). The data pipeline — built with Python, GDAL, Xarray, and RioXarray — transforms NetCDF climate model outputs (CNRM-ALADIN64E1, ~12 km resolution, 2014–2100) into monthly raster tilesets. The challenge then becomes how to render these rasters with maximum expressiveness on a map, going well beyond what standard raster layer support in MapLibre GL JS offers out of the box. This is where our open source library maplibre-gl-shader-layer comes in. Born from real production needs in meteorological data visualization, this TypeScript/WebGL library provides the building blocks to create fully custom tiled layers for MapLibre GL JS, powered by Three.js under the hood. Developers can write their own GLSL fragment shaders and hook into per-tile uniform updates — giving full control over color mapping, encoding, blending, and animation. The library's flagship component, MultiChannelSeriesTiledLayer, is designed specifically for scientific data: it decodes multi-channel PNG or WebP tiles where RGB channels encode up to 24-bit precision float values (similar to Mapbox Terrain-RGB), supports time/depth/scenario series interpolation, and applies configurable colormaps from a built-in library (viridis, inferno, turbo, and custom descriptions). Nodata handling via the alpha channel and support for PMTiles archives round out the feature set for production use. We will walk through the full open source stack — from raw NetCDF to interactive browser map — and show how maplibre-gl-shader-layer makes it straightforward to build expressive, performant meteorological visualizations without sacrificing flexibility. Demos will include climate indicator overlays, a warming scenario slider, and seasonal navigation — all rendered in WebGL. The library is MIT-licensed, available on npm, and actively maintained by the Camptocamp geoblocks team. Repository: https://github.com/geoblocks/maplibre-gl-shader-layer false https://talks.osgeo.org/foss4g-2026/talk/K8HNDT/ Ran2 General session talk 2026-09-02T11:30:00+09:00 11:30 00:30 We built a conversation-first data analysis tool for Japan's MLIT LINKS project that lets non-engineers explore location-rich datasets using natural language. The stack runs entirely in-browser DuckDB-WASM for SQL execution, Claude on AWS Bedrock, and MapLibre GL JS and Vega-Lite for declarative visualization. foss4g-2026-5188-combining-duckdb-maplibre-gl-js-and-ai-for-browser-native-map-visualization Piyush ChauhanHiroki Inoue en Japan's MLIT LINKS project manages large volumes of data, much of it geospatial, but the people who need to analyze it aren't engineers. Writing SQL, building charts, or plotting data on maps requires technical skills that create a bottleneck. We built a conversation-first tool that bridges this gap. Users describe what they want in plain language, and the system handles the rest — generating SQL queries, rendering interactive charts, and plotting results on maps, all within the browser. The architecture is fully client-side. DuckDB-WASM executes AI-generated SQL directly in the browser with no backend required, using Parquet for efficient columnar storage. For visualization, we chose MapLibre GL JS and Vega-Lite because their declarative specs are natural targets for AI generation — the model outputs a spec, and the library renders it. Inference runs through AWS Bedrock with Claude, restricted to Tokyo and Osaka regions for compliance. The UI splits into a chat panel on the left and a live preview panel on the right showing tables, charts, and maps. Each conversation can reference multiple tables, and each table supports an optional chart and map view. Our biggest challenge was AI response quality. Tool design for DuckDB queries, chart specs, and map specs required careful prompt engineering — but a single monolithic system prompt proved brittle and hard to maintain. We solved this with an AI skill system: the system prompt is broken into many small, focused markdown files, and the model retrieves only what it needs for a given task via a get_skill tool. This reduces context size while significantly improving response quality and stability. Improving the AI's behavior now means creating or editing a skill markdown file in the codebase — no prompt rewrites needed. To close the feedback loop, we built an AI regression testing pipeline. It generates detailed markdown and JSON reports scoring response quality, enabling automated tuning. Skill refinement is now largely handled by AI itself through GitHub Issues paired with Claude Code Actions. false https://talks.osgeo.org/foss4g-2026/talk/LFWXY7/ Ran2 General session talk 2026-09-02T13:00:00+09:00 13:00 00:30 I needed to analyze pedestrian flow patterns and draw them on map in urban spaces, so I built a simple pipeline to extract trajectories from smartphone videos which shot from a low line of sight. foss4g-2026-5000-pedestrian-trajectory-mapping-with-maplibre-and-opencv-from-smartphone-videos Toki Hirose en ## Overall Pipeline 1. Capturing videos of pedestrians with smartphone 2. Pedestrian Detection with openCV DNN and makeing trajectories with CentroidTracker 3. Homography transformation from video coordinate to wgs84 4. Draw trajectory on interactive map ## In this session I will talk about the barriers encountered during the experiment. 1. From holding the smartphone with my hand to securing it with a tripod. 2. How effective is a low line of sight in videos. 3. Comparison between centroid and footprint trackers. 4. How and how many trajectory points I sampled. ## Technologies Used This project utilizes only open source technologies: - [**OpenCV DNN**](https://opencv.org/) - Computer vision and deep learning - [**CentroidTracker**](https://pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/) - Multi-object tracking - [**MapLibre GL**](https://maplibre.org/) - Interactive web mapping - [**YOLOx**](https://github.com/Megvii-BaseDetection/YOLOX) - Object detection - [**Python**](https://www.python.org/), [**TypeScript**](https://www.typescriptlang.org/), [**React**](https://react.dev/) - Development frameworks ## License This project acknowledges the following open source licenses: - [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) - OpenCV, YOLOx, TypeScript - [BSD 3-Clause](https://opensource.org/licenses/BSD-3-Clause) - MapLibre GL - [MIT](https://opensource.org/licenses/MIT) - React - [Python Software Foundation License](https://docs.python.org/3/license.html) - Python ## I expect that this project contributes to individual protest analysis by analyzing pedestrian reactions. false https://talks.osgeo.org/foss4g-2026/talk/QLQTZP/ Ran2 General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 Decision-making depends on trust, but how often can you trace a reported figure back to its source? We're solving that with an open-source platform to repeatedly analyse any dataset against any geometry cloud-natively, with an auditable record of every step and full provenance for each value. foss4g-2026-5118-data-dashboards-with-provenance-using-cloud-native-geospatial-processing Alex LeithWilliam Jones en When a dashboard reports that there are 37,000 hectares of mangroves in a given region, can you trace that number back to its source? Which dataset? Which version? What software was used? Without that chain of evidence, confidence in the figures and the decisions made from them is limited. Provenance for the values used in our dashboard is fundamental to our tool. This talk presents an open-source platform developed by Auspatious for the UNSW Centre for Sustainable Development Reform, built around three tightly integrated components. 1. Cloud Infrastructure orchestrates parallel processing at scale using Argo Workflows on Kubernetes. Every pipeline run is version-controlled and reproducible, processing thousands of geometries and intersecting billions of pixels over decades of data. 2. The Python Toolkit is the processing engine. Built on the OpenDataCube, Spatio-Temporal Asset Catalog, STAC-GeoParquet, Cloud Optimized GeoTIFFs, Xarray, Dask, and Obstore, it creates and stores metadata, converts data and produces versioned outputs with full provenance at every step. Any dataset and any geometry can be combined, and initial applications include Global Mangrove Watch extents analysed against global Exclusive Economic Zones, but the toolkit is domain-agnostic. 3. Our App ties it together. A web interface for managing datasets (such as GMW mangrove extent or Google-Microsoft open buildings), geometries (geographic boundary regions of interest), products (processed analysis outputs), and pipeline runs. It tracks provenance end-to-end, exposes an API used by the toolkit and workflows, and presents results through interactive dashboards and reports. Together these components deliver reproducibility and a provenance chain at a scale that handles global data processing and using open-source tools. Through cloud-native architecture, we're delivering a dashboard that presents simple information but retains the link through to the source. And in this talk, we'll share what we built, our lessons from running these pipelines in production, and how you can adapt the system for your own environmental monitoring or spatial analysis domain. false https://talks.osgeo.org/foss4g-2026/talk/R999MX/ Ran2 General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 Open-source communities can't afford to ignore the growing use of AI agents to write code. This talk shares lessons from building MapLibre Agent Skills: an open, eval-tested knowledge base that corrects AI hallucination in MapLibre implementations, with lessons for other projects looking to do the same. foss4g-2026-5498-teaching-ai-to-contribute-to-open-source-lessons-from-maplibre-agent-skills /media/foss4g-2026/submissions/RT8SMH/Screenshot_2026-03-21_at_4.41.00PM_dF8Fbuh.png Stephanie May en You've heard about the AI code contribution problem: maintainers flooded with plausible-but-wrong pull requests and issues generated by AI assistants that don't understand the codebase. But there's a second problem that gets less attention: AI doesn't just generate bad contributions — it generates bad advice. Developers building with open-source libraries are getting confidently wrong answers, and the resulting confusion circles back as bug reports, forum questions, and more maintenance load. Agent skills — curated knowledge files that any AI coding assistant can load into context — offer open-source projects a way to address both problems at once. They improve the code AI writes for your users, which reduces the confused issues that reach maintainers. They can also raise the floor on AI-assisted contributions by giving models accurate, version-specific knowledge about your project's APIs. Proprietary platforms ship agent skills as a commercial product alongside their paid services. Open-source projects have to build them differently: in the open, with community authorship, and so far sustained with a non-existent budget. Yet the value is clear: they give LLMs the knowledge to get it right the first time: fewer wrong answers, fewer confused issues, fewer wasted review cycles. [MapLibre Agent Skills](https://github.com/maplibre/maplibre-agent-skills) is one such effort. Hosted under the MapLibre GitHub organization, it's a growing collection of skills in the open agent skills format supported by every major AI coding assistant. Each skill targets a topic where AI regularly fails MapLibre developers — confusing MapLibre APIs with Mapbox's, or hallucinating method signatures that changed between major versions. We identify these failure points through systematic mining of GitHub issues, Stack Overflow questions, and community Slack conversations — the places developers land after AI gives them a wrong answer. But writing skills is only half the problem. How do you know they work, and how do you know they create solutions that can keep working? We built an evaluation pipeline that tests every skill for grounding, completeness, code correctness, and reliability — but the harder questions are the ones we can't answer with automated tests. Will developers keep asking questions on Stack Overflow and community forums, or will they give up where they can't blindly trust coding assistants? Will the companies who use and integrate these tools fund the maintenance work and ecosystem development that keeps documentation current? This talk shares what we've learned building MapLibre Agent Skills and names the open questions we're still working through. If you build with MapLibre, you can install these skills today. If you have expertise in any corner of the ecosystem — framework integration, tile pipelines, cartography, migration — we're looking for skill authors. And if you're wrestling with these same questions about AI in the open source world, bring them. This conversation is bigger than any one project, and transparency, collaboration, and cooperation are how we'll figure it out. false https://talks.osgeo.org/foss4g-2026/talk/RT8SMH/ Ran2 General session talk 2026-09-02T14:30:00+09:00 14:30 00:30 GeoGirafe is a framework-free WebGIS built on standard WebComponents. This talk presents its feature set, plugin-based architecture, community governance model, and the funding structure that keeps the project independent and sustainable. foss4g-2026-5208-geogirafe-a-community-driven-webgis-built-plugin-by-plugin /media/foss4g-2026/submissions/BZ3B7D/Capture_d%C3%A9cran_2026-03-15_%C3%A0_12.30.11_xiEsGK4.png Stéphane Malta e Sousa en GeoGirafe is a WebGIS application designed from the ground up to be owned and shaped by its user community. Rather than relying on heavyweight frameworks or vendor ecosystems, GeoGirafe embraces a lean, modular architecture keeping the barrier to contribution low and long-term maintainability high. ## Features Version 1.0, released in late 2025, ships with a comprehensive feature set: * WMS, WMTS, WFS, Vector Tiles and COG support * Advanced drawing and measurement tools * Full-text search across features, thematics and layers * Cross-section view with LiDAR profile * Sharing tools (iframes and permalinks) * 3D views, drawings and shadow simulation * Layer WFS filtering GeoGirafe works as a standalone application or can be extended with an optional backend, unlocking: * OpenID Connect authentication * Themes, layers and column-level permissions * Secured printing service Three major features are underway for 2026: * Editing via OGC API Features (backed by QGIS Server or pygeoapi) * Advanced filtering (multi-criterion and spatial predicates) * Panoramic road-level photo viewer ## Key Objectives * **Community Governance**: Roadmap and technical decisions are driven by the community, not by any single organization. This collective ownership model keeps the project aligned with real-world needs. * **Sustainability**: Every architectural choice is made with the long term in mind. By avoiding monolithic framework dependencies, GeoGirafe stays adaptable as the web evolves — without accumulating technical debt or locking contributors into a specific toolchain. * **Modularity & Extensibility**: A plugin-based architecture enables teams to develop custom features independently, without forking or destabilizing the core. This makes contributions straightforward and custom developments genuinely sustainable. * **Open Collaboration**: Documentation is community-maintained, and first-level support runs through a Discord server. Three companies have joined the ecosystem and offer professional-grade support for organizations that need it. false https://talks.osgeo.org/foss4g-2026/talk/BZ3B7D/ Ran2 General session talk 2026-09-02T15:30:00+09:00 15:30 00:30 This presentation introduces an efficient algorithm using "Extended Spatial ID" and "V-Bit" encoding to solve computational explosions in 3D overlap detection. By mapping 3D geometries to 1D keys, our method enables fast, scalable spatial set operations. This approach will play a key role in drone 3D airspace management. foss4g-2026-5476-efficient-search-and-set-operations-for-spatial-ids Suzu ShimokawaTomoro Saito加納祐吾 en "Spatial ID" is a geospatial standard that recursively divides the Earth into 3D voxels. By reducing 3D overlap detection to simple set operations, it has strong potential for use in 3D drone airspace management. To safely manage dense drone traffic and identify available airspace, executing fast spatial set operations (e.g., union, intersection, and difference) across a large number of 3D regions is critical. However, practical deployment faces significant computational challenges. First, representing irregular 3D shapes leads to substantial data bloating. Second, performing set operations across datasets with different resolutions (zoom levels) results in exponential growth in computational complexity. In this presentation, we propose a novel algorithm to overcome these bottlenecks. First, we introduce the “Extended Spatial ID.” Unlike standard Spatial IDs, which use a uniform zoom level across all dimensions, our approach assigns independent zoom levels to the X, Y, and Z axes. This significantly compresses the data structure. Based on this design, we developed efficient one-to-one spatial operations. For example, when computing the difference between two regions, our algorithm minimizes the number of resulting voxels. This effectively resolves both data expansion and computational overhead in one-to-one operations. Second, we introduce "V-Bit" encoding to address the O(N) linear scan bottleneck when detecting overlapping regions in vast datasets. This method encodes the binary tree structure of Spatial IDs into a compact 2-bit array, enabling direct mapping of complex 3D geometries to a 1D ordered index. This encoding facilitates the efficient retrieval of relevant spatial regions using simple prefix range queries in a standard key–Value Store (KVS). By combining efficient one-to-one spatial operations with fast 1D KVS-based search, our method achieves highly scalable 3D spatial computation for future drone infrastructure. false https://talks.osgeo.org/foss4g-2026/talk/CJGGW9/ Ran2 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 This session is aimed at developers, project leads, and organisational decision-makers who are navigating the question of how to sustain FOSS4G. We aim to leave attendees with a clearer framework for thinking about sustainability, and a more honest picture of what each path demands in practice. foss4g-2026-5505-sustaining-open-source-real-models-real-lessons Jeroen TichelerAna Belgun en Open source geospatial software powers critical infrastructure, research, and decision-making around the world — yet the question of how to sustain it remains one of the most pressing and underexplored challenges in our community. This presentation offers an experience-driven look at the business models behind open source sustainability, drawing on the lived experience of organisations that have built their work around open source libraries. We examine three broad sustainability strategies — consulting-led, product-led, and hybrid approaches — exploring the trade-offs, tensions, and unexpected lessons each model brings. What does it actually take to keep a widely-used open source project healthy over the long term? Where do community interests and commercial realities align, and where do they diverge? The conversation is made more timely by the European Commission's Cyber Resilience Act, which introduces the concept of open source stewardship as a formal responsibility. For organisations that develop, maintain, or depend on open source software, this represents both a new compliance consideration and a genuine opportunity to articulate and formalise the value of what we do. This session is aimed at developers, project leads, and organisational decision-makers who are navigating these questions — whether just starting out or well into the journey. We aim to leave attendees with a clearer framework for thinking about sustainability, and a more honest picture of what each path demands in practice. false https://talks.osgeo.org/foss4g-2026/talk/93MA3B/ Ran2 General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 We present a pipeline transforming 190,000+ Japanese geological borehole records into interactive CesiumJS 3D Tiles. From open-sourcing a custom XML parser (boring-parser), through Implicit Tiling with glTF metadata extensions, to a browser-based WebGIS viewer — we share the journey of making subsurface data accessible to everyone. foss4g-2026-5617-from-boring-xml-to-3d-tiles-building-a-geological-borehole-viewer-with-cesiumjs Haruto SASAKI en ~Background~ Japan's National Ground Information Database holds over 190,000 borehole columnar logs in BED XML format spanning six schema versions (1.10–4.00). This geological data is critical for disaster prevention, construction, and urban planning, yet has traditionally been accessible only as 2D columnar charts (PDF/images) — making it difficult to grasp spatial relationships between boreholes, continuity of geological layers, or to get an overview of 190K records at once. Our initial proof-of-concept used PostGIS and Ruby on Rails to dynamically serve borehole data via bounding-box queries. At the scale of 190K records, the per-camera-move database queries proved too slow for practical use, and the always-on server cost was prohibitive. This motivated a pivot to pre-tiled static file delivery using 3D Tiles. ~Building on Open Standards, Step by Step~ Step 1: Custom XML Parser (boring-parser, open-source) — BED XML has six incompatible schema versions, DTD-based definitions with no type information, and mixed geodetic datums (Tokyo, JGD2000, JGD2011). No existing tool could handle all versions, so we built boring-parser and published it on crates.io as open-source. It unifies parsing across all versions with automatic datum-to-WGS84 conversion. Step 2: 3D Tile Generator based on OGC 3D Tiles 1.1 — Using boring-parser as the foundation, we built a tile generator compliant with the OGC 3D Tiles 1.1 specification using Implicit Tiling (quadtree, region). We designed a three-tier LOD strategy for 190K boreholes: spatially clustered count markers at wide zoom (Levels 4–6), individual circle markers at mid zoom (Levels 7–9), and detailed cylindrical meshes with per-layer rock-type coloring at close zoom (Level 10). Each GLB embeds geological metadata via glTF extensions EXT_structural_metadata and EXT_mesh_features, enabling interactive property queries in the browser. Step 3: WebGIS Viewer (CesiumJS + Resium) — The frontend renders the generated 3D Tiles using CesiumJS and Resium, providing 3D borehole visualization with rock-type coloring, multi-borehole cross-section generation with DXF export for CAD workflows, and terrain rendering via Japan's GSI elevation tiles. ~Key Technical Challenges~ - Unifying six XML schema versions into a common data model - Multi-step geodetic datum conversion (Tokyo → JGD2000 → JGD2011 → WGS84 → ECEF) - Designing Implicit Tiling levels and clustering strategies for smooth browsing of 190K records - Embedding rich geological metadata into glTF via EXT_structural_metadata PropertyTable schema design ~Takeaway~ By publishing the foundational parser (boring-parser) as open-source and building the visualization pipeline on open standards (3D Tiles, glTF), we show how domain-specific geospatial data can be made accessible through the browser. Improving accessibility to geological data supports better decision-making in disaster prevention and urban planning. We hope this "build the parser, open-source it, then visualize with open standards" approach inspires others facing similar challenges. false https://talks.osgeo.org/foss4g-2026/talk/CUPKJ8/ Ran2 General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 Overview of how the ESA Planetary Science Archive (PSA) uses open-source technologies such as OpenLayers, GeoServer, Three.js, and PostGIS to manage, visualize, and distribute planetary data, particularly for Mars, enabling interactive exploration, 3D visualization, and efficient access for the scientific community. foss4g-2026-5005-use-of-open-source-software-in-the-esa-planetary-science-archive Fran Raga en The European Space Agency (ESA) uses a range of open-source technologies to manage, visualize, and distribute planetary data, with a particular focus on Mars. These tools support both internal operations and provide the global scientific community with access to high-quality planetary datasets through the Planetary Science Archive (PSA). Several key open-source technologies are used in the PSA infrastructure. **OpenLayers** is used to create interactive web-based maps that allow scientists to explore planetary geospatial data through intuitive interfaces. **GeoServer** serves spatial datasets through standard protocols such as WMS, enabling visualization of observation footprints over different planetary base maps. **Three.js** provides 3D visualization capabilities, allowing users to interactively explore irregular bodies such as comet 67P/Churyumov–Gerasimenko from the Rosetta mission. At the data layer, **PostgreSQL** combined with **PostGIS** manages complex geospatial datasets and enables advanced spatial queries, supporting the analysis and integration of large volumes of planetary data. ESA also collaborates on open-source projects that facilitate scientific data access and processing. One example is **Astroquery**, a Python library that enables programmatic access to astronomical databases. ESA contributes to this project to ensure planetary mission data can be easily accessed and integrated into scientific workflows. Another example is **Antimeridian**, an open-source tool designed to correctly process geometries that cross the 180° longitude line. This capability is particularly relevant for planetary mapping, where coordinate systems may extend beyond traditional Earth-based longitude conventions. These technologies are integrated into a new user interface for the PSA that provides several capabilities for researchers. Scientists can explore interactive maps of Mars, Phobos, and other planetary bodies, apply filters, and overlay multiple geospatial layers such as geological, topographical, and spectral data. The interface also supports multiple projections, including polar and equirectangular views, and allows users to query and extract information for specific regions of interest. In addition, 3D visualization tools enable detailed inspection of planetary surfaces and irregular bodies. Users can perform real-time queries to retrieve mission data and download selected datasets for further scientific analysis. The underlying GIS architecture combines GeoServer for distributing planetary base maps, a frontend based on OpenLayers and Three.js for 2D and 3D visualization, and PostgreSQL/PostGIS for geospatial data storage and querying. Additional integration with tools such as Astroquery and Antimeridian improves data accessibility and resolves technical challenges related to planetary coordinate systems. By building its planetary data infrastructure on open-source technologies, ESA provides powerful tools for exploring and accessing planetary datasets while supporting collaboration within the scientific community. These technologies enable researchers to combine data from multiple missions and instruments within a single interface, facilitating more comprehensive planetary studies. This work demonstrates how open-source GIS tools can effectively support planetary science and the exploration of the Solar System. false https://talks.osgeo.org/foss4g-2026/talk/9BXVRG/ Ran2 General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 We present i.hyper, a comprehensive hyperspectral processing addon for GRASS. It includes modules for import, metadata handling, atmospheric correction, preprocessing, spectral resampling, indices, albedo, visualization, exploration, export, and more, covering the workflow from raw satellite products to analysis-ready data. foss4g-2026-5190-i-hyper-processing-hyperspectral-imagery-in-grass /media/foss4g-2026/submissions/DRUBLN/import_example_GExy2U0.jpg Alen MangafićTomaž ŽagarYann Chemin en Hyperspectral remote sensing provides rich spectral information for applications such as soil monitoring, geochemistry, vegetation analysis, environmental assessment, and coastal studies. With operational missions such as EnMAP, PRISMA, and Tanager, and upcoming missions such as FLEX and CHIME, the amount and diversity of hyperspectral data are rapidly increasing. However, open-source spatial workflows still lack unified tools for handling these products, which differ in structure, spectral sampling, radiometric units, and metadata organization. We present i.hyper, a multimodular addon for GRASS that supports reproducible hyperspectral workflows within a single spatial environment. Its core is a 3D raster data cube in which the spectral axis forms the third dimension, combined with a redesigned structured metadata model that stores spectral band attributes, acquisition geometry, processing history, and extensible user-defined fields. This improves traceability and makes the system easier to extend. The addon includes i.hyper.import for unified import of hyperspectral products, i.hyper.metadata for metadata inspection and editing, i.hyper.atcorr for atmospheric correction (based on the 6SV2.1 radiative transfer model), i.hyper.preproc for spectral preprocessing and dimensionality reduction, i.hyper.specresamp for spectral resampling, i.hyper.indices for wavelength-based spectral indices, i.hyper.albedo for broadband albedo estimation, i.hyper.composite for false-color composites, i.hyper.explore for interactive spectral exploration, and i.hyper.export for export to external formats. Together, these modules cover the workflow from raw satellite products to analysis-ready data while preserving spatial and metadata consistency throughout processing. i.hyper is available in the official GRASS Addons repository. Combined with GRASS from conda-forge and the broader Python ecosystem, it provides a particularly powerful environment for hyperspectral workflows. false https://talks.osgeo.org/foss4g-2026/talk/DRUBLN/ Conference Management Room1 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 Open drone technology and collaborative platforms improve geospatial data collection, disaster response, and sustainable development. Tools like Drone Tasking Manager, OpenDroneMap, and OpenStreetMap enable coordinated drone missions, efficient image processing, and open data sharing, supporting mapping, environmental monitoring, agriculture, and urban planning. foss4g-2026-5108-open-drone-mapping-for-emmpowerment /media/foss4g-2026/submissions/QBY8FJ/WhatsApp_Image_2026-03-11_at_1.19.05_AM_6Y3PTqO.jpeg Sia Moadeh KamandaGibril Ahmed Lansana en Abstract The use of open drone technology and collaborative mapping platforms has significantly improved the collection and use of geospatial data in areas such as disaster response, environmental monitoring, agriculture, and urban planning. Open drones provide a fast and cost-effective way to capture high-resolution aerial imagery compared to traditional land surveying methods. They are particularly useful for mapping remote or difficult-to-access locations, allowing communities, researchers, and organizations to obtain accurate spatial information that supports planning and development. A key platform that supports coordinated drone mapping is the Drone Tasking Manager. This platform organizes drone missions by dividing large mapping areas into smaller sections that can be assigned to different drone pilots. This structured approach helps teams work more efficiently while avoiding duplication of flight coverage. It also allows project managers to track progress and review completed tasks to ensure that collected imagery meets the required standards. As a result, mapping projects become more organized, faster, and more reliable, especially when several drone pilots are involved. As one of the drone pilots participating in these mapping activities, I have personally experienced the benefits of coordinated platforms like the Drone Tasking Manager. During field operations, drone pilots capture aerial imagery that contributes to the production of accurate maps and geospatial datasets. This experience not only strengthens drone operation and aerial surveying skills but also highlights the importance of teamwork in large-scale mapping projects. Open drone technology is commonly integrated with open-source software such as OpenDroneMap, which processes drone images into useful geospatial products like orthophotos and digital surface models. These outputs can then be shared through platforms such as OpenStreetMap, making the data accessible to governments, humanitarian organizations, and communities. Overall, the integration of open drones with collaborative mapping platforms improves the efficiency of data collection, promotes open data sharing, and supports disaster management, environmental monitoring, and sustainable development. false https://talks.osgeo.org/foss4g-2026/talk/QBY8FJ/ Conference Management Room1 General session talk 2026-09-02T11:30:00+09:00 11:30 00:30 How far can a fully open geospatial stack take a natural-language request like “Show me cafes in Hiroshima City”? This talk presents TRIDENT, text2geoql-dataset, and a self-hosted planet-scale OpenStreetMap stack for building a grounded, interactive AI map agent. foss4g-2026-5481-building-an-ai-map-agent-with-self-hosted-planet-scale-osm-and-tiny-llms Yui Matsumura en This talk presents a practical end-to-end pipeline for building an AI map agent with a fully open geospatial stack. Starting from a natural-language request such as “Show me cafes in Hiroshima City,” I demonstrate how TRIDENT, text2geoql-dataset, and core OpenStreetMap services including Overpass API, Nominatim, and Taginfo can be combined to generate grounded, interactive maps without depending on public shared infrastructure for repeated LLM-driven queries. The talk has three parts. First, I introduce my self-hosted “osm-planet-in-da-house” environment, where planet-scale OSM services run at home and provide rate-limit-free endpoints for experimentation. Second, I explain how text2geoql-dataset synthesizes and validates natural-language-to-Overpass-QL training pairs by resolving place names, checking tag validity, and confirming that queries return real results. Third, I discuss the path toward running the deepest layer of the agent with a tiny local model on Raspberry Pi-class hardware for offline or low-connectivity settings. Rather than promising magic, I will focus on concrete architecture, lessons learned, practical constraints, and what is realistically possible today with FOSS. false https://talks.osgeo.org/foss4g-2026/talk/78TXXS/ Conference Management Room1 General session talk 2026-09-02T13:00:00+09:00 13:00 00:30 This presentation introduces GeoServer’s authentication and authorization subsystems, covering supported protocols, identity providers, and integration strategies. It explores combining mechanisms into a unified framework, custom plugins, and proxy-based solutions. It concludes with GeoFence, highlighting advanced rule-based access control, fine-grained data security, and flexible integration options. foss4g-2026-5570-mastering-security-with-geoserver-geofence-and-openid Andrea Aime en The presentation will provide a comprehensive introduction to GeoServer’s authentication and authorization subsystems. The authentication section will cover the supported protocols (e.g., Basic/Digest authentication) and identity providers (such as local configuration files, databases, LDAP servers, and OAuth2/OpenID Connect), including scenarios where the same source may fulfill both roles. It will explain how to combine multiple authentication mechanisms into a unified and coherent security framework, and will present examples of custom authentication plugins for GeoServer, enabling integration with bespoke security architectures. The presentation will then address authorization, describing GeoServer’s pluggable authorization model and comparing it with external proxy-based solutions. The default service and data security system will also be examined, highlighting its strengths and limitations. Finally, we will explore the advanced authorization provider, GeoFence. The various levels of integration with GeoServer will be presented, ranging from simple, seamless direct integration to more sophisticated external deployments. We will conclude by showcasing GeoFence’s powerful authorization capabilities, including: * User- and role-based access control * OGC service, workspace, layer, and layer group restrictions * CQL read and write filters * Attribute-level security * Spatial filtering of raster and vector data based on areas of interest false https://talks.osgeo.org/foss4g-2026/talk/98G8PE/ Conference Management Room1 General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 This talk explores how cloud-native principles- combined with STAC, COGs, and TiTiler - enable dynamic, scalable, expression-driven raster workflows without precomputing products. foss4g-2026-4891-cloud-native-raster-workflows-with-titiler Dimple Jain en For years, raster workflows in GIS and Earth Observation followed a familiar pattern: preprocess everything. Generate pyramids, compute indices, create static XYZ tiles, store derived rasters, and repeat for every visualization or analysis need. At small scale, this works. At cloud scale, it breaks. As datasets grow - multi-temporal scenes, multi-sensor collections, higher resolutions - preprocessing pipelines become expensive, slow, and storage-heavy. Experimentation slows down. Storage multiplies unnecessarily. In this session, we will: - Understand what “cloud-native” really means - Explore how TiTiler enables dynamic rendering - Discuss scaling considerations and performance trade-offs - Share practical lessons from building dynamic raster APIs This talk is aimed at engineers, GIS developers, and analysts modernizing geospatial infrastructure - and anyone who wants to rethink how raster data should be served false https://talks.osgeo.org/foss4g-2026/talk/ELRLSW/ Conference Management Room1 General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 GeoParquet is a file format based on Apache Parquet for efficient storage of large geospatial datasets. Well-sorted Parquet achieves better compression and better query performance. However, it is not enough for streaming features with acceptable performance. To address this, I added hierarchical dimension for sorting and achieved better results. foss4g-2026-5066-a-proposal-for-hierarchically-organized-geoparquet Kanahiro Iguchi en ## Abstract GeoParquet is a file format based on Apache Parquet for efficient storage of large geospatial datasets. To make better use of Parquet, spatial sorting is key. Well-sorted Parquet achieves better compression and better query performance. However, it is not enough for streaming features with acceptable performance. To address this, I added hierarchical dimension for sorting and achieved better results. In this session, I propose spatially and hierarchically organized GeoParquet layout for streaming. ## Outline 1. GeoParquet 2. Spatial sorting 3. Predicate Pushdown 4. Feature streaming is not easy 5. Hierarchical sorted Parquet ## Reference - https://github.com/Kanahiro/yosegi false https://talks.osgeo.org/foss4g-2026/talk/F8BZWW/ Conference Management Room1 General session talk 2026-09-02T14:30:00+09:00 14:30 00:30 An experimental CesiumJS project that reconnects scenes from Barefoot Gen to real locations in Hiroshima, using historical maps and open geospatial data to explore spatial storytelling, cultural memory, and how narrative can be read through geographic space. foss4g-2026-5298-mapping-the-distance-to-history-reading-barefoot-gen-on-a-map-with-open-geospatial-tools /media/foss4g-2026/submissions/AL9L3W/%E3%82%B9%E3%83%A9%E3%82%A4%E3%83%891_WdTqqoi.PNG Gen Kukita en This talk introduces an experimental project that reconstructs the spatial world of Keiji Nakazawa’s manga Barefoot Gen using open geospatial technologies. Although Barefoot Gen is widely known as a narrative of the atomic bombing of Hiroshima, readers usually experience the story only through sequential manga panels. As a result, the geographic structure of the city—distance from the hypocenter, directions of movement, terrain, and the scale of destruction—often remains abstract. To address this, the project models each narrative scene as structured geospatial data (scene ID, order, place type, viewpoint, coordinates, time phase, and source reference). These scene points are visualized in a web-based 3D viewer built with CesiumJS, combined with historical maps and open geospatial datasets. Users can navigate Hiroshima spatially, explore key scenes from the narrative, and examine how the story relates to the real geography of the city. Methodologically, the project borrows a geolocation logic similar to OSINT-style analysis—linking open sources to geographic coordinates—but applies it to cultural memory rather than investigative verification. The goal is not to reproduce the past exactly, but to make the “distance to history” visible and explorable through geographic space. The presentation will demonstrate a prototype viewer currently under development, focusing on an initial set of narrative scenes reconstructed as geospatial data. The talk will also discuss how open geospatial technologies can support new forms of spatial storytelling and contribute to emerging practices in Geospatial Humanities. false https://talks.osgeo.org/foss4g-2026/talk/AL9L3W/ Conference Management Room1 General session talk 2026-09-02T15:30:00+09:00 15:30 00:30 This talk presents the implementation of Object-Selective Post Effects (Outline/Bloom) in a GIS-oriented rendering system. It explains how selective effects were achieved through object identification, mask generation, and render pass composition, while addressing GIS-specific constraints such as rendering order and mesh structures. foss4g-2026-5129-developing-rendering-effect-extensions-as-a-gis-beginner So Sakabe en This session introduces the implementation of **Object-Selective Post Effects that apply Outline and Bloom to dynamically generated and managed objects** in a 3D map rendering system, from a graphics engineer’s perspective. While implementing selective effects, several commonly used techniques in conventional rendering pipelines could not be directly applied. For example: - **Override materials** apply to the entire scene and therefore cannot selectively render specific objects. In addition, they break when meshes rely on custom vertex attributes. - Unlike screen-space post effects, **Object-Selective Post Effects depend on the rendering order and occlusion relationships within the 3D scene**, which introduces design constraints on where effect passes can be inserted and how rendering layers should be structured. The session walks through the development process, starting with understanding the existing rendering pipeline, identifying potential extension points, and designing an implementation strategy. The talk also discusses how the final solution balances **visual quality, compatibility with the existing rendering architecture, and runtime performance**. Although the system is built in a GIS context, the main focus of this session is on practical insights into implementing selective rendering effects and making design decisions in constrained graphics environments. false https://talks.osgeo.org/foss4g-2026/talk/HKTWAC/ Conference Management Room1 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 Hydrological ML requires costly upstream catchment aggregation. We present an efficient flow-accumulation-based method bypassing per-pixel delineation, achieving orders-of-magnitude speedups. Implemented in GRASS and Python, this open-source approach enables scalable, high-resolution modeling, demonstrated by a countrywide 90 m Random Forest nitrogen prediction. foss4g-2026-5235-efficient-pixel-scale-upstream-covariate-computation-for-environmental-machine-learning Kajetan Chrapkiewicz en Environmental data cubes play an increasingly important role in geospatial machine learning pipelines by collating harmonised layers derived from heterogeneous sources into analysis-ready form. Open-source geospatial infrastructures are central to enabling this harmonisation, interoperability, and reproducibility across domains. Yet domains such as freshwater hydrology require additional, costly data processing to convert gridded environmental layers into river-network-aware predictor variables that summarise environmental conditions as zonal statistics — such as average clay content or standard deviation of air temperature — across the entire upstream catchment draining to a given stream pixel. In principle, this upstream aggregation can be achieved by delineating a separate upstream catchment for every stream pixel and computing zonal statistics within it. However, this approach becomes a major computational bottleneck already at the scale of a small European country (tens of thousands of km²), even when parallelised across high-performance computing clusters. Here we show an efficient and accurate method for calculating ML-ready, river-network-aware predictor variables based on a multi-flow-direction flow accumulation algorithm that requires no per-pixel catchment delineation. We validate the method against conventional delineation-based zonal statistics, demonstrating close numerical agreement while achieving orders-of-magnitude speedup. We implement the method both as a new feature of the GRASS software suite and as an open-source Python package providing a generic interface to arbitrary flow accumulation backends, such as pysheds, making it directly pluggable into existing ML pipelines. The complete workflow runs reproducibly in a Jupyter notebook, lowering the barrier for users less familiar with geospatial scripting. We demonstrate the approach on an end-to-end machine learning workflow: high-resolution (90 m) Random Forest predictions of total nitrogen across the full stream network of a European country, trained on multiple environmental predictors computed with the proposed flow-accumulation-based method. At larger scales, this open and scalable approach supports evidence-based nutrient management and policy decisions, illustrating how FOSS4G tools can accelerate environmental modelling, reproducible research, and applied geospatial machine learning. false https://talks.osgeo.org/foss4g-2026/talk/JVFQTA/ Conference Management Room1 General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 CPCS used GIS and six transport surveys to collect and analyse data on Lagos’ public transit, focusing on eight proposed Quality Bus Corridors. Insights informed sustainable restructuring models, with data stored in a GIS database, published in GTFS format, and shared via an interactive visualization portal. foss4g-2026-4811-applied-use-of-gtfs-data-in-transportation-planning Caroline Akoth en In this project, CPCS utilized GIS technology to conduct comprehensive data collection exercises aimed at understanding the public transit industry in Lagos. The project gathered spatial data through six transport surveys and developed tools for automated data analysis. Data collection methods included surveys and manual traffic counts, such as: Bus route identification survey, Manual classified traffic counts, Bus lines identification survey, Boarding and alighting survey, Stakeholder surveys (drivers, bus owners, ancillary service providers), Onboard OD passenger survey, Passenger quality/mystery survey, Intermodal hub identification survey. The collected data aimed to provide insights into existing bus operators and their routes along eight proposed Quality Bus Corridors (QBCs) within Lagos State’s mainland. These corridors, ranging from 1.6 km to 14.4 km in length, serve as feeder routes connecting communities to intermodal hubs and other transportation modes like BRT, LRT, First-and-Last-Mile buses, and paratransit services. Currently, these corridors host paratransit bus services using danfo and Koropes Operations Management Through extensive data analysis, the project developed sustainable models for transitioning and formalizing the Lagos bus industry. Spatial analysis provided corridor-specific results and implementation strategies, leading to a plan for restructuring the bus industry and integrating existing operators into the future QBCs. All collected data was stored in a GIS database and updated to public transit repositories in GTFS format. Additionally, a data visualization portal was created to allow users to interact with transit data for various locations in Lagos. false https://talks.osgeo.org/foss4g-2026/talk/AZE79Z/ Conference Management Room1 General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 A modern vector map of Japan built from GSI open data and OpenStreetMap, focused on cartographic design and customization. Built on vector tiles, it integrates with MapLibre and open-source tools, balancing Japanese mapping conventions with modern usability. foss4g-2026-5482-turning-japan-s-open-data-into-a-modern-web-map /media/foss4g-2026/submissions/B3BHX3/OpenChizu-MapTiler_3FnRxtR.png Nicolas Bozon en This presentation introduces a new vector map of Japan built from Japanese governmental open data, OpenStreetMap, and the MapTiler platform. The project translates the highly detailed vector tile schema of the Geospatial Information Authority of Japan (GSI) into a modern web map style while preserving the semantics of Japanese cartography. This includes translating local feature attributes into clear, usable map layers, rendering Japanese text using modern fonts, and converting traditional map symbols into scalable vector icons. The result is a coherent and modern map style that maintains the richness and precision of the original data while significantly improving readability and usability. A strong emphasis was placed on flexibility and customization: the map supports multiple visual styles, including a carefully designed dark mode, and allows developers to adapt colors, symbols, and visual hierarchy to different applications and use cases. Built on vector tiles, the map enables dynamic styling and smooth rendering across all zoom levels. Through the MapTiler platform, it can be easily customized and integrated into applications using MapLibre, the MapTiler SDK, or other open-source mapping libraries such as Leaflet and OpenLayers. Support for TileJSON, OGC Tiles, and WMTS ensures full interoperability within the broader open geospatial ecosystem. The resulting map balances local cartographic conventions with modern usability expectations, providing a highly readable and adaptable basemap for web and mobile applications. OpenChizu can also be used completely offline and deployed into secured environements and sovereign map infrastructures. This talk demonstrates how open data, open standards, and open-source technologies can be leveraged to design modern, user-friendly maps at national and global scales. false https://talks.osgeo.org/foss4g-2026/talk/B3BHX3/ Conference Management Room1 General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 Ever wondered how geospatial data travels from cloud storage to your browser's GPU? This talk walks through the full pipeline: accessing cloud-optimized formats like PMTiles, decoding vector tile formats like MVT and MLT, and rendering on the GPU. We also peek at future directions like WebGPU and WebAssembly. foss4g-2026-5580-from-cloud-to-gpu-inside-the-modern-vector-tile-rendering-pipeline Markus Tremmel en Ever wondered how modern web maps actually work under the hood—from cloud-native storage all the way to pixels on your screen? This talk takes you on a deep dive through the full vector tile rendering pipeline. We’ll start at the source, exploring how geospatial data is stored and accessed efficiently using cloud-optimized formats like PMTiles. From there, we’ll unpack how data is encoded into vector tile formats such as Mapbox Vector Tiles (MVT) and emerging approaches like MapLibre Tile (MLT)—what they contain, how they are structured, and the design decisions behind them. Next, we move into the browser to examine how tiles are decoded and rendered on the GPU using map renderers like MapLibre GL JS, and how they transform raw tile data into interactive maps via WebGL. Finally, the talk closes with an outlook on the evolving hardware landscape and how technologies like WebGPU and WebAssembly SIMD are opening new possibilities for high-performance browser-based map rendering. false https://talks.osgeo.org/foss4g-2026/talk/JVYYMX/ Conference Management Room2 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 This talk presents MapConductor, a neutral open-source layer designed to improve interoperability among mobile map SDKs. Rather than replacing existing providers, it bridges ecosystems, supports shared conceptual models, and promotes flexible integration across diverse geospatial platforms. foss4g-2026-4943-mapconductor-an-interoperability-layer-for-mobile-map-sdks Masashi Katsumata en Mobile map development relies on powerful SDKs such as Google Maps, Mapbox, HERE, ArcGIS, and MapLibre. Each of these SDKs provides unique strengths, architectural philosophies, and cloud integrations. However, differences in API design, camera systems, rendering models, and platform implementations make cross-SDK knowledge transfer difficult. Developers often need to rethink concepts when switching providers or supporting multiple SDKs. This talk introduces MapConductor, an open-source interoperability layer that provides a unified declarative API across multiple mobile map SDKs. Rather than replacing existing SDKs, MapConductor is designed to work alongside them — preserving their strengths while providing a shared conceptual model for markers, overlays, camera control, and geospatial operations. Inspired by Kubernetes’ orchestration model, MapConductor aims to enable flexibility and knowledge portability across map SDK ecosystems. We will present architectural design decisions, technical challenges, performance considerations, and lessons learned from building a cross-SDK abstraction in real-world mobile applications. false https://talks.osgeo.org/foss4g-2026/talk/K7KV9Y/ Conference Management Room2 General session talk 2026-09-02T11:30:00+09:00 11:30 00:30 To address data integration and interoperability, we conducted a PoC using OSS “Ouranos GEX” in our high-speed spatiotemporal data management technology. We present PoC results and discuss the development of the Java-based OSS, with a focus on technical challenges and lessons learned. foss4g-2026-5317-applying-oss-ouranos-gex-lessons-from-poc-and-the-path-to-java-based-development Nan Si en To build a next-generation digital society where people, robots, and autonomous systems can work together seamlessly, a strong and unified social infrastructure is essential. One major challenge is the fragmentation of spatio-temporal data caused by diverse proprietary standards. This fragmentation makes it difficult for different systems to connect and share data in real-time, resulting in inefficiencies and coordination issues. These obstacles hinder the growth of smart cities and digital twins. Resolving these issues requires cooperative efforts across various fields, as individual actions alone cannot overcome the broader problems caused by disconnected standards and data silos. To address these challenges, Japan’s Ministry of Economy, Trade and Industry (METI) and the Information-technology Promotion Agency (IPA) introduced the "Spatial ID Guidelines," a standardized framework for classifying geospatial data as unique 4D voxels. Our experimental group incorporated an official OSS, "Ouranos GEX," into our high-speed data management technology to develop a spatio-temporal data integration system. Using this platform, we conducted a Proof-of-Concept (PoC) to validate real-time data consolidation and management. This experiment demonstrated that converting dynamic data into Spatial IDs enables different organizations to share data easily, even when using different data formats. By adopting standardized 4D voxels, we achieved unified management and rapid updating of heterogeneous spatio-temporal information in complex environments. The PoC confirmed that this standardized indexing helps break down data silos and enables real-time data integration. While existing Spatial ID tools are mostly available in Python and JavaScript, large-scale commercial use demands faster processing, better memory safety, and higher stability. To bridge the gap between prototypes and industrial production, we developed the Java-based OSS for “Ouranos GEX.” In this project, we focused on porting and optimizing the core logic, such as spatial ID calculations and coordinate transformations, to provide a stable foundation for a wide range of practical, real-world applications. This report introduces our PoC results, technical challenges, and practical lessons learned. For example, migrating the coordinate transformation logic to Java required addressing discrepancies in how different programming languages handle map projections to ensure high precision. Our goal is to promote the adoption of standardized 4D spatial data and contribute to building interoperable digital twin solutions globally. false https://talks.osgeo.org/foss4g-2026/talk/BJFRLD/ Conference Management Room2 General session talk 2026-09-02T13:00:00+09:00 13:00 00:30 To transform discrete geospatial data into an insightful heatmap, this proposal focuses on the selection of geospatial indexing methods with traversal functionality, and the propagation of data like waves through space. This approach is implemented using open source libraries and applied to two real-world phenomena in Japan. foss4g-2026-5079-wave-like-propagation-heatmap-from-discrete-data-using-geospatial-indexing-methods /media/foss4g-2026/submissions/NYQCFN/submit_image_KLLcYvw.png Takashi Nojima en # Overview Transforming discrete geospatial data into an insightful heatmap is a challenge in geospatial visualization. To address this, my approach relies on geospatial indexing methods that support traversal functionality, enabling values to propagate across neighboring indexed cells like a wave across space. Designing a propagation from discrete data comes down to two key questions. 1. Is the propagation value distance-based or area-based? 2. Is the propagation front directional or concentric? These frameworks are applied to two real-world phenomena in Japan, with WebGIS applications built to visualize the results. 1. Cherry blossom bloom propagation using H3 2. Railroad accessibility propagation using A5 The backend is built on PySpark and Apache Sedona for nationwide parallel distributed processing, with the frontend WebGIS built on React and Deck.gl. The backend outputs JSON files optimized for rendering performance from the frontend. The entire stack, including H3 and A5 as spatial index libraries, is implemented with open source libraries. # Cherry Blossom Bloom Propagation Cherry blossom blooming propagates as a directional wave, advancing from blooming to non-blooming areas. This phenomenon is modeled by H3 with distance-based indexing and isotropic cell structure. The dataset is open data from the Japan Meteorological Agency, covering bloom start and full bloom dates at stations nationwide. Each observation location is mapped to an H3 cell and sorted by date. Propagation is computed between adjacent observation cells in the date order. For each cell, surrounding H3 cells within grid distance are enumerated using gridDisk. A dot product calculation determines whether a candidate cell lies in the forward direction of the bloom front, and qualifying cells are assigned linearly interpolated bloom dates. Missing values are filled using the well-known characteristic that cherry blossoms take roughly seven days from bloom start to full bloom, and another seven days to bloom end. # Railroad Accessibility Propagation Railroad accessibility represents passenger population density around stations, and propagates concentrically. This phenomenon is modeled by A5 with area-based indexing and equal-area cell structure. The dataset is open data from the Ministry of Land, Infrastructure, Transport and Tourism, covering annual passenger volume per station. Each station is mapped to an A5 cell, and passenger volume propagates outward using gridDisk up to k=3 rings. The A5 cell resolution and k=3 are both derived from a usable distance of 3,500 meters, where each ring covers roughly 500 meters. Passenger volume decays geometrically by 0.7 per ring, with contributions from all stations aggregated per cell. # Conclusion This proposal demonstrated that translating discrete phenomena into geometric properties simultaneously determines the choice of spatial index and the design of the propagation algorithm. Cherry blossom bloom propagation, directional and distance-based, leads to H3. Railroad accessibility propagation, concentric and area-based, leads to A5. These two questions, whether propagation is distance-based or area-based, and whether it is directional or concentric, serve as a practical framework for designing propagation heatmaps from discrete data using open source libraries. false https://talks.osgeo.org/foss4g-2026/talk/NYQCFN/ Conference Management Room2 General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 "Cognitive Motion" leverages Motion UI and perceptive psychology to simplify complex Smart Agriculture data. By using multiscale continuity, temporal animations, and real-time visual feedback, it reduces cognitive load and prevents spatial disorientation. Purposeful design aligns data visualization with human instinct, fostering trust and enabling more intuitive, precise decision-making. foss4g-2026-5296-cognitive-motion-design-redefining-interaction-in-map-applications-use-case-for-smart-agriculture Jirayut Naksin en In the era of Smart Agriculture, the surge of complex and dynamic data—ranging from satellite imagery and vegetation indices to IoT sensor feeds—often imposes a significant cognitive load on users when presented through traditional static maps and dashboards. This presentation explores the application of Motion UI (Motion User Interface) grounded in perceptive psychology to enhance data interpretation and intuitive decision-making. "Cognitive Motion" redefines interaction within map applications frameworks through three core pillars: 1. Multiscale Spatial Continuity: By employing transition easing and parallax effects, the system maintains object permanence as users navigate from macro-level overviews down to field-level specifics. This approach mitigates spatial disorientation and ensures the user’s mental map remains cohesive despite rapid changes in scale. 2. Temporal Fluidity and Pattern Recognition: Moving beyond discrete "before-and-after" snapshots, this methodology utilizes smooth interpolation to animate crop growth cycles and pest migration patterns. By mimicking natural motion, the system enables the human brain to detect subtle anomalies and trends, leading to more accurate predictive analysis. 3. Visual Feedback and System Trust: The integration of micro-interactions and pulsing signifiers establishes a robust action-response loop for IoT sensors. These subtle "digital pulses" confirm connectivity and data integrity in real-time, reducing user anxiety and fostering trust in remote farm management systems. In conclusion, Cognitive Motion demonstrates that purposeful Motion UI design is more than an aesthetic enhancement it is a critical tool for aligning geospatial data visualization with human instinct. By transforming complex agricultural data into actionable insights, this approach optimizes perception and empowers precise decision-making in modern precision farming. false https://talks.osgeo.org/foss4g-2026/talk/PRQKM7/ Conference Management Room2 General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 Maps often rely on color to communicate spatial information, yet about 3% of the global population has color vision deficiency, making interpretation difficult. This study proposes accessible map design approaches that reduce reliance on color using inclusive palettes, symbols, patterns, and visual hierarchy, resulting in practical guidelines for GIS developers. foss4g-2026-5286-expanding-map-perception-designing-inclusive-experiences-for-colorblind-users Panupong Promklum en Humans primarily rely on visual perception as the main channel for understanding spatial information from maps. As a result, cartographic design often uses color as a key element for classifying and communicating information, since color allows users to quickly and efficiently distinguish between different data elements. However, in reality, approximately 3% of the global population, or around 300 million people, experience Color Vision Deficiency (CVD). This condition prevents them from clearly distinguishing information conveyed through color in the same way as typical users, which can make interpreting map data more difficult or lead to misinterpretation. Although contemporary map design often emphasizes visual clarity and aesthetic quality, many systems still rely heavily on color as the primary means of communication. Consequently, users with color vision deficiency may not be able to access map information equally. Furthermore, design approaches that specifically consider this group of users have not yet been widely established as standard practices in map design and spatial interface development. This study therefore proposes user experience (UX) design approaches for maps and introduces the concept of accessible map design by reducing reliance on color as the sole communication channel. It presents design techniques that enable all users to interpret spatial information more clearly, including the use of accessible color palettes, the creation of visual hierarchy, the application of symbols and texture patterns, and the organization of layout structures that enhance the distinction between different data elements. The results of this study lead to the development of accessible map design standards that can be applied to real-world map interfaces. The outcomes are summarized in the form of a design checklist and a set of resources for GIS developers and designers, providing guidelines that help maps communicate information effectively for users with different visual abilities. false https://talks.osgeo.org/foss4g-2026/talk/S3PXJP/ Conference Management Room2 General session talk 2026-09-02T14:30:00+09:00 14:30 00:30 This presentation introduces the "Data Transition" framework, using FOSS4G pipelines to transform satellite imagery into actionable disaster solutions. By bridging technical capability and human necessity, it provides real-time situational awareness, strategic urban planning, and predictive intelligence, empowering vulnerable communities to save lives and protect livelihoods through informed decision-making. foss4g-2026-5386-beyond-the-pixels-a-pm-s-perspective-on-data-transition-transforming-satellite-imagery-into-actionable-disaster-solutions Raksina Maneechan en In the era of Cloud-Native technology and the rapid growth of FOSS4G, processing satellite imagery via platforms like Open Data Cube or QGIS has become increasingly accessible. However, a critical question remains for Project Managers: "Are we merely generating data, or are we solving real-world problems?" The true challenge lies beyond technical execution; it is about the strategic design of Data Pipelines that transform raw pixels into life-changing solutions for vulnerable communities. This presentation introduces the "Data Transition" framework—a strategic workflow that repurposes satellite data into multi-dimensional outputs tailored to each stage of the disaster management cycle: Real-time Situational Awareness: Leveraging automated processing pipelines to extract flood extents and deliver them via OGC Web Services. This serves as "actionable intelligence," enabling citizens to make precise evacuation decisions and allowing first responders to prioritize rescue missions and safe routes effectively. Strategic Blueprint: Utilizing open-source software to process monthly historical data and identify "Flood Frequency Zones." These outputs act as evidence-based tools for dialogue between governments and communities, facilitating a shift from rigid engineering (e.g., dams) toward sustainable "Sponge City" designs and risk-based urban zoning. Predictive Intelligence: Integrating real-time flood data with the HEC-RAS model to generate probabilistic forecasts. This grants humanity’s most precious asset during a crisis: Time. It allows families to protect their livelihoods and enables industries to implement Business Continuity Planning (BCP), preventing global supply chain disruptions and mitigating massive economic losses. The Project Manager serves as the essential bridge between "Technical Capability" and "Human Necessity." By utilizing an entirely open-source stack, we move beyond static mapping toward dynamic decision support. Ultimately, the goal of geospatial technology is not just the delivery of data (Output), but the realization of an Outcome: empowering humanity to face uncertainty with knowledge, choices, and the dignity to protect their own lives. false https://talks.osgeo.org/foss4g-2026/talk/CAEGH7/ Conference Management Room2 General session talk 2026-09-02T15:30:00+09:00 15:30 00:30 Voxelizer, which produces 3D grid data—voxels—from 2D/3D spatial information raw data. foss4g-2026-5182-voxelization-cubing-3d-space-for-machine /media/foss4g-2026/submissions/SYBPRD/voxelizer_ZHaKkCf.png Hak joon KimSeungmin Kwonjoohyoung Kim en Currently, the data used to train and operate Deep/Machine Learning-based AI models is primarily two-dimensional raster data. However, when considering AI for use in three-dimensional spatial information environments like Digital Twins, three-dimensional grid data cannot be ignored. This research involves producing three-dimensional grid data, specifically voxel data, for use as training/input data for AI, and verifying whether this produced voxel data can be utilized in autonomous drones, vehicles, and indoor robots. This year marks the first year of this research, focusing on studying the process of converting raw data into voxel data. This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport. (Grant RS-2025-02317649, NTIS Grant:2610000447) false https://talks.osgeo.org/foss4g-2026/talk/SYBPRD/ Conference Management Room2 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 Noodles.gl is an open-source visual programming environment that simplifies the creation of high-performance, cinematic geospatial animations by integrating Deck.gl and MapLibre into a reactive, node-based workflow. This session explores how GIS practitioners can use its "noodles and wires" interface to create beautiful GPU-accelerated visuals foss4g-2026-4832-noodles-gl-a-visual-programming-language-for-animation-geospatial-and-the-web Adam Krebs en Stop building static maps and start telling dynamic stories. Noodles.gl is an open-source, node-based programming language that bridges the gap between high-performance GIS and cinematic animation. By fusing deck.gl and MapLibre into a reactive "noodles and wires" interface, it allows developers to choreograph complex spatial data flows and GPU-accelerated visuals without getting buried in low-level WebGL boilerplate. This session demonstrates how to transform raw geospatial data into bespoke, interactive narratives using a modular workflow. We will explore how integrating keyframes enables precise, timeline-based control over mapping layers, making it easier than ever to visualize fluid trajectories and evolving urban landscapes. Discover how to reclaim your creative freedom and build the next generation of high-fidelity, web-based cartography. false https://talks.osgeo.org/foss4g-2026/talk/UTMSUJ/ Conference Management Room2 General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 What actually constitutes a valid polygon is a question that various stakeholders have very different views on – and even open-source libraries like GEOS and S2 are far from a consensus. This talk explores practical problems at the crossroads of coffee industry, geodata, and EU regulation. foss4g-2026-5606-between-coffee-farmers-and-an-eu-portal-when-is-a-polygon-actually-valid Christoph Friedrich en One would believe it to be quite straightforward: The user uploads their geodata, it is processed, and then gets passed on. However, what actually constitutes a valid polygon is a question that various stakeholders (from farmers to engineers to authorities) have very different views on – and even open-source libraries are far from a consensus. This technical yet light-hearted talk illustrates the wide range of pitfalls and how to overcome them, exploring practical examples from the crossroads of the worldwide coffee industry, user-supplied geodata, standardised file formats, interconnecting systems, and well-intended EU regulation. On the technical side, it will especially focus on the differing behaviour of `GEOS` vs `S2` and hence applications and frameworks that use these libraries, like Python's `Shapely` or R's `sf`. It will also touch on the use of `GeoJSON` by authoritative regulators. false https://talks.osgeo.org/foss4g-2026/talk/UV7LF7/ Conference Management Room2 General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 This presentation describes how we connected Japan's national open geospatial data platform to AI agents using the Model Context Protocol — covering architecture, real-world use cases including infrastructure maintenance, and our vision for AI-ready open spatial infrastructure. foss4g-2026-5568-building-an-mcp-server-ecosystem-on-japan-s-national-scale-geospatial-open-data-platform Mami Enomoto en Japan's Ministry of Land, Infrastructure, Transport and Tourism (MLIT) operates the MLIT Data Platform (国土交通データプラットフォーム) — a national-scale open geospatial data platform that aggregates and provides access to hundreds of datasets spanning infrastructure, transportation, land use, facility inventories, BIM/CIM models, and disaster risk information. While the platform has made significant progress in open data accessibility, a new question is taking shape: how do we make this rich spatial dataset ecosystem truly usable by AI agents and large language models? This presentation describes our work designing and implementing an MCP (Model Context Protocol) server ecosystem on top of the MLIT DPF. MCP, an open standard for connecting AI assistants to data sources and tools, provides a natural bridge between large language models and structured geospatial datasets. Key datasets exposed as MCP tools include national facility inventories, road and infrastructure asset data, and disaster risk layers. Key topics covered include: - Why MCP for geospatial open data: The motivation behind connecting DPF to AI agents via the Model Context Protocol - Architecture and design: How DPF's APIs and data catalogue were structured as MCP tool definitions - Use cases: AI-assisted facility search, infrastructure maintenance support, and cross-dataset summarization - AI-ready open geospatial ecosystem: Lessons and outlook for national spatial data infrastructures Open geospatial platforms have mastered data access. The next frontier is AI-readiness. This presentation shares what we have built, what we have learned, and what we think comes next. false https://talks.osgeo.org/foss4g-2026/talk/DNNRNG/ Conference Management Room2 General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 Source Cooperative's data proxy lets users access open datasets through S3-compatible tools. We rebuilt it from scratch as Multistore, an open source S3 gateway designed to be reusable across the ecosystem. This talk covers why we rebuilt, what we learned, and how others can adopt it. foss4g-2026-5228-multistore-an-s3-compliant-data-distribution-api Anthony Lukach en Source Cooperative is an open data platform where researchers and organizations publish and share datasets. Its data proxy lets users access hosted data through familiar S3-compatible tools like aws-cli, boto3, obstore, GDAL, and DuckDB, with backends spanning AWS S3 and Azure Blob Storage. The proxy works, but it was built specifically for Source Cooperative and is difficult for others to adopt or extend. Multistore is our effort to rebuild the data proxy as a modular, open source project that any organization can use. Rather than extracting and refactoring the existing proxy, we started fresh with a focus on clean interfaces and pluggable components. The result is an S3-compliant gateway that resolves incoming requests to the correct storage backend. Its zero-copy passthrough approach means the proxy never buffers file contents, keeping resource usage low and throughput high regardless of file size. Beyond modularity, we used the rebuild as an opportunity to rethink deployment. The original runs on a small cluster of ECS Fargate nodes in a single AWS region. Multistore can compile to both native and WebAssembly targets, which lets us run a hybrid architecture: a Cloudflare Workers layer handles global edge routing to insure fast data access from across the globe, while regional servers handle heavier workloads that benefit from proximity to storage backends. This combination improves latency for users worldwide while keeping operational costs predictable. Authentication moves from long-lived access keys to OIDC token exchange, supporting both interactive and machine-to-machine flows with temporary credentials. The system is organized around a set of pluggable interfaces for routing, authorization, credential storage, and backend I/O, so adopters can customize behavior without forking the project. In this talk, we will walk through the limitations that motivated a ground-up rebuild, the architectural decisions we made along the way, and the tradeoffs involved in designing software that serves one platform's needs while remaining genuinely reusable. We will demo Multistore as it runs in Source Cooperative and discuss how other open data platforms, catalogs, and data repositories might integrate it into their own infrastructure. false https://talks.osgeo.org/foss4g-2026/talk/HK3WBZ/ Conference Management Room3 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 This talk shares results from a security review of widely used open‑source GIS libraries using a lightweight SAST and SBOM methodology. Attendees will learn how common patterns create risk and how simple, repeatable practices can strengthen the security and resilience of geospatial tools. foss4g-2026-5222-what-could-possibly-go-wrong-a-practical-security-review-of-popular-open-source-gis-libraries Jared MarcotteRobert Cheetham en As geospatial tools continue to power disaster response, environmental resilience, urban planning, and civic decision making, the software we rely on becomes part of our critical infrastructure. Yet many of the open-source GIS libraries we use every day—whether in desktop workflows, cloud pipelines, or web maps—were never designed with modern cybersecurity expectations in mind. This talk shares the results of a focused security review of several widely used open-source GIS libraries, using a lightweight methodology adapted from the Center for Internet Security's RABET-V program (https://rabetv.org) and built around industry-standard Static Application Security Testing (SAST) and Software Bills of Materials (SBOM) analysis tools. This work takes a collaborative, community-friendly approach by scanning a curated subset of libraries for common software supply chain issues, dependency risks, and code-level patterns that could expose geospatial systems to unintended vulnerabilities. All of the findings were handled under responsible disclosure principles, with maintainers notified privately and no exploitable details shared publicly. This session is designed for both programmers and non-programmers. For developers, you’ll walk away with concrete examples of how memory unsafe parsing, dependency chains, build configurations, and API patterns can introduce security risk—and what you can do in your own projects to reduce it. For GIS practitioners, analysts, and decision makers, the talk will aim to demystify cybersecurity concepts and show how everyday choices in tools, data sources, and deployment environments affect the trustworthiness of the maps and analyses you build and depend on. The ultimate goals of this talk are not to raise alarm but, rather, to raise awareness of cybersecurity considerations when building and using geospatial tooling; to show how simple, repeatable scanning practices can strengthen the health of the geospatial open-source ecosystem; and to suggest how our community can come together to build safer, more resilient tools for the work ahead. false https://talks.osgeo.org/foss4g-2026/talk/UVERAE/ Conference Management Room3 General session talk 2026-09-02T11:30:00+09:00 11:30 00:30 point-tiler is a Rust CLI that converts massive LAS/LAZ point clouds into modern 3D Tiles (v1.1) via a glTF/GLB-centric workflow. This session covers city-scale reliability: LAZ-first I/O, coordinate/axis handling, external sorting beyond RAM, and practical compression choices. foss4g-2026-4906-introduction-to-point-tiler-a-tool-for-converting-large-scale-point-cloud-data-into-3d-tiles-and-its-implementation-methods nokonoko_1203 en In theory, converting point cloud data to 3D Tiles is a "simple" task. You choose a tool, select your LAS files, and run it. However, in practice, pipelines often grind to a halt due to subtle yet troublesome issues. Examples include Coordinate Reference Systems (CRS) or axis definitions that don't match the data, workflows that assume uncompressed LAS despite LAZ being the industry standard, and city-scale point cloud data that far exceeds available memory. Furthermore, conversion often takes several hours, and it is not uncommon for the resulting file count and size to be massive. A casual "just try re-running it" approach becomes a costly habit. In this session, I will provide a deep, implementation-level explanation using "point-tiler"—a Rust-based CLI tool I developed and released to bring ease of automation to large-scale data conversion. We will examine the entire pipeline in detail: streaming reads of massive amounts of LAS/LAZ files, parallel decoding of LAZ, coordinate handling, spatial sorting for efficient tiling, and GLB-centric output compliant with the latest 3D Tiles 1.1 workflow. We will also delve into how tile hierarchies are actually constructed. What processing is required to accurately calculate bounding volumes? How should we vary the target values for tile extent and density? We will explore how "conversion speed," "output size," and "visual aesthetics" interact with one another. I will also explain "scaling beyond memory limits." In point-tiler, we adhere to user-defined memory limits and employ external sorting—a technique where sorted runs are offloaded to temporary storage and then merged—allowing for the processing of large datasets without manual partitioning. I will explain the factors that truly impact execution time in large-scale processing (disk I/O, memory bandwidth, temporary disk throughput), how to plan and manage temporary storage, and why simple parallelization can be counterproductive (I/O contention, allocator overhead, and cache impact). Finally, I will compare this approach with common alternatives. I will also discuss output optimization, weighing compatibility against file size. Specifically, we will look at when KHR_mesh_quantization is effective, the value of introducing EXT_meshopt_compression, and the role of gzip in delivery via CDN. Participants will gain concrete patterns for building reliable, large-scale data conversion tools, including I/O optimization, setting memory limits, estimating temporary storage size, selecting safe default values, and avoiding the pitfalls of parallel processing. false https://talks.osgeo.org/foss4g-2026/talk/WLBRPR/ Conference Management Room3 General session talk 2026-09-02T13:00:00+09:00 13:00 00:30 Japan’s cadastral land-parcel open data ships as massive proprietary XML files. This talk is about how I made a Rust port of the official Python converter in Rust, rewriting and optimizing, cutting nationwide conversion down from hours to minutes. foss4g-2026-4893-rust-for-geospatial-etl-high-throughput-conversion-of-japan-s-land-parcel-data Keita Kobayashi en Japan’s Ministry of Justice has released nationwide cadastral land parcel data every year since 2022. The data is provided in a proprietary XML format, so it must be converted before it can be used in common GIS tools. The Digital Agency released a Python converter, but the full dataset is huge (around 90–100GB). Converting everything can take 6 to 8 hours, which slows down repeatable builds and downstream workflows. This talk explains the XML format, how I ported the converter to Rust, then reworked it for maximum speed using profiling, streaming processing, and parallelism. The current Rust tool can convert the entire dataset in about 15 minutes. Repository: https://github.com/KotobaMedia/mojxml-rs false https://talks.osgeo.org/foss4g-2026/talk/YM7FDU/ Conference Management Room3 General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 In many rapidly urbanizing regions, infrastructure data is often outdated, or non-existent. Traditional satellite imagery provides a "top-down" view but fails to capture the key details necessary for utility management and road safety. This session presents a proven framework for using street-level imagery to tackle these challenges. foss4g-2026-5276-from-street-level-to-grid-level-scaling-urban-resilence-through-open-mapping Sia Moadeh KamandaGibril Ahmed Lansana en The technical workflow transforms raw street-level imagery into structured geospatial intelligence through a rigorous, three-stage process of data collection, data extraction, and data integration. It begins with a strategic sensor deployment, utilizing high-resolution cameras mounted on multi-modal transport (preferably a motor bike) to capture sequential 360° imagery at intervals, ensuring comprehensive coverage and optimal feature visibility for computer vision processing. Once uploaded via edge-computing tools, the data enters the extraction phase where utility assets such as power poles and transformers are identified to build functional network graphs, while road geometries and building metadata are digitized to profile carriage-widths and structural attributes. Finally, this data undergoes a dual-layer validation process, cross-referencing computer-vision outputs against satellite imagery before being synchronized with OpenStreetMap via the JOSM editor, ensuring all infrastructure data adheres to global standards for interoperability and long-term utility. The collaborative framework centers on a "Human Stack" approach that ensures long-term sustainability by embedding mapping expertise within local institutions. This process begins with strategic stakeholder alignment, where needs-assessment workshops with municipal planners and utility providers define mission-critical data attributes and establish clear protocols for open data governance. To transition partners from data consumers to creators, a "Train-the-Trainer" model is implemented, combining hands-on field "mapathons" with technical instruction on open-source tool chains like OSM ID editor, HOT Tasking manager, Mapillary, QGIS and JOSM. By integrating these datasets into existing asset management systems and establishing continuous feedback loops for data validation, the framework fosters deep institutional ownership and ensures that the digital infrastructure remains accurate, scalable, and locally maintained long after the initial project phase. false https://talks.osgeo.org/foss4g-2026/talk/E7UV3F/ Conference Management Room3 General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 Web map engines perform dozens of optimizations to render geographic features at interactive frame rates. This talk reveals the internal mechanisms — from GPU optimization that collapse 10,000 draw calls into a handful, to zero-copy parsing pipelines and Web Worker task scheduling that keep the main thread free. foss4g-2026-5104-how-map-engine-optimizes-your-visualization Keiya Sasaki en ### Description When a map engine renders smoothly, it's rarely by accident. Behind every tile transition and feature layer lies a carefully engineered stack of performance strategies that most developers never see. This session dives into the internals of a map engine to explain *why* these optimizations exist and *what* you can learn from them to improve your own visualizations. **Efficient feature rendering** — Rendering 10,000 individual features naively means 10,000 draw calls. We'll explore how batching and GPU instancing dramatically reduce this overhead. Styling those batched features by their attributes presents its own challenge — we'll look at how engines evaluate styles against feature attributes ahead of time, pack the results into a texture, and let the GPU look up each feature's style at render time via a per-vertex batch ID, keeping attribute-driven styling fast without breaking the batching model. **Post-processing on the GPU** — Once features are rendered, visual effects are often still needed. We'll explore why screen-space post-processing techniques like SMAA and FXAA are significantly faster than per-mesh approaches like MSAA, and how applying effects to the final rendered image rather than to individual geometries keeps the pipeline lean regardless of feature count. **Zero-copy data pipelines** — Traversing feature data twice is wasteful. We'll look at how parsing pipelines can be structured to complete as much work as possible in a single pass, and how data structures like flattened 1D polygon arrays enable large geometry transfers to Web Workers without memory copying. **Threading and the main thread** — Web Workers are essential for keeping UIs responsive, but they come with constraints. We'll discuss how engines manage worker pools based on `navigator.hardwareConcurrency`, assign tasks to idle workers, and structure transferable data to avoid serialization overhead. **Network and connection management** — Browsers impose hard limits on concurrent HTTP connections. We'll cover strategies for aborting stale requests early, queuing tile fetches intelligently, and preventing connection saturation from blocking critical resources. **Memory and GC pressure** — Garbage collection pauses are a silent framerate killer. We'll examine how engines minimize GC exposure, and why languages like Rust compiled to WebAssembly offer structural advantages for memory-safe, GC-free data handling. **Perceived performance through progressive rendering** — Actual render time and perceived render time are not the same thing. Map engines exploit this gap by rendering cheaper, lower-fidelity representations first — such as a single parent tile instead of four child tiles — and progressively refining toward full quality. **Improve coordinate precision** — When you set the position to the GLB itself or put a model through Three.js, you may notice that the model shakes. This is due to the vertex precision on the GPU. In this case, you must use a technique called RTE or RTC. --- ### Intended Audience Someone who wants to optimize your visualization from the perspective of a map engine's internal structure. --- ### Takeaways - Insight of how to optimize your visualization - Knowledge of various optimizations in a map engine false https://talks.osgeo.org/foss4g-2026/talk/ZH893R/ Conference Management Room3 General session talk 2026-09-02T14:30:00+09:00 14:30 00:30 By using openEO and titiler, geospatial analysts can reduce data preparation time for Sentinel-2 datasets, enabling rapid, actionable insights for accelerating climate action. This cloud-native workflow delivers insightful GIS dashboards, empowering decision-makers with timely analytics on forest health. foss4g-2026-5262-accelerating-climate-action-building-gis-dashboards-with-openeo-and-titiler /media/foss4g-2026/submissions/EGDDKB/forest_dashboard_mock_k1K2rvu.png Firza en Geospatial analysts often spend the majority of their time on laborious data discovery and pre-processing tasks. In the critical context of accelerating climate change, we need rapid, actionable analytics to support domain experts, policy-makers, and non-technical users in making impactful decisions, such as prioritizing forest protection, biodiversity conservation, or monitoring forest health. This talk introduces a cloud-native workflow utilizing openEO and titiler to cut down data preparation time and focus squarely on delivering high-impact analytics. The talk will demonstrate how to leverage openEO to process Sentinel-2 datasets in the cloud, automatically handling data discovery and pre-processing (like mosaicking and atmospheric correction). This clean, analysis-ready data is then streamlined to the dashboard. By integrating this output with titiler, we can efficiently serve dynamic, tiled maps for a product-ready, GIS-based web dashboard. The platform also integrates a conversational AI chatbot interface to empower non-coder and non-technical users to interact with the complex geospatial data and drive their own insights through natural language commands. The output is an analytical product capable of generating critical indices (e.g., burn and vegetation indices). Combining them with contextual information such as natural disturbance analytics, the dashboard could give insights on forest health and prompt non-technical users to make actions. Learn how this unified approach allows geospatial professionals to move beyond manual data wrangling and empower non-technical decision-makers with the timely insights needed to drive effective climate action. false https://talks.osgeo.org/foss4g-2026/talk/EGDDKB/ Conference Management Room3 General session talk 2026-09-02T15:30:00+09:00 15:30 00:30 The Pasig River in Metro Manila is a major global source of ocean plastic pollution. Leveraging digital twin and AI-Machine Learning models, this project enables real-time monitoring and predictive analysis of plastic waste flows. foss4g-2026-5123-using-ai-and-digital-twin-in-solving-one-of-the-world-s-most-plastic-polluting-river /media/foss4g-2026/submissions/EJDCLE/PasigRiver_Digital_Twin_jYkoxOq.jpg Rudolph Peralta en Plastic pollution in the Pasig River is a critical environmental and social challenge, with the river contributing over 6% of global ocean plastic waste from rivers. The impacts of plastic waste are far-reaching, affecting water quality, aquatic ecosystems, and the health and livelihoods of communities along the riverbanks. In response to the Asian Development Bank’s Discovery Space Challenge, Arup developed a scalable open source AI enabled digital twin to provide actionable insights for plastic waste management. The digital twin integrates geospatial, environmental, and socio-economic data, enabling identification of pollution hotspots, waste types, and contributing activities. This initiative is not only a technological advancement but also a catalyst for positive change in waterway health and community well-being. The Pasig River Plastic Waste Digital Twin, powered by advanced AI-ML models, represents a transformative approach to riverine plastic waste management. By enabling real-time monitoring, predictive analytics, and community engagement, the project delivers measurable improvements in waterway health and quality of life for local residents. The outcomes extend beyond environmental benefits, fostering social cohesion, economic opportunity, and a culture of sustainability. Continued refinement of the models and expansion of the digital twin platform will further enhance its impact, supporting the vision of clean, healthy, and resilient waterways for communities across the region. The outcomes include improved waterway health and tangible benefits for local communities, such as reduced flooding, enhanced public health, and increased community engagement in environmental stewardship false https://talks.osgeo.org/foss4g-2026/talk/EJDCLE/ Conference Management Room3 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 The M4S Project leverages a low-barrier FOSS stack to map seagrass in Metinaro, Timor-Leste. By integrating participatory drone imagery and mobile data collection into a community-led workflow, we transition from manual methods to digital baseline mapping, empowering community stewardship to protect marine biodiversity and sustain local livelihoods. foss4g-2026-5588-the-m4s-project-scaling-seagrass-conservation-in-timor-leste-through-participatory-foss-workflows /media/foss4g-2026/submissions/FDPFRV/M4S_FOSS4G_Nuxmuj5.png Honey FombuenaDinar Adiatma en Seagrass meadows in Metinaro, Timor-Leste, are vital yet undervalued ecosystems. They act as carbon sinks, protect endangered dugongs, and sustain the fisheries that the local economy depends on. Despite their importance, standardized geospatial data for these areas has historically been limited. The M4S (Monitoring for Seagrass) Project aims to address this by closing the data gap using an open-source and participatory approach. This session explores how the M4S project moves beyond traditional, labor-intensive manual transects toward a streamlined, digital workflow. We will detail the technical and social architecture of our mapping process, which is designed to be fully adaptable by local communities. The project’s methodology relies on an integrated stack of mainly FOSS tools that ensure transparency and local ownership: 1. Aerial Data via DroneTM: We utilize HOT’s DroneTM for web-based flight planning and image processing. This allows the team to capture high-resolution imagery necessary to identify seagrass extent that is often invisible in standard satellite imagery, all while keeping the planning and processing pipeline accessible via the web. 2. Ground-Level Insights with Mapillary: To complement aerial views, we use Mapillary for street-level (and coast-level) imagery. This provides a geolocated visual record of the seagrass habitats, helping to validate findings on species types and degradation levels. Mapillary provides open data but is currently not open-source. 3. Mobile Field Mapping with KoboToolbox: We replaced paper-based notes with KoboToolbox. By using customized digital forms, local mappers collect real-time data on species distribution and seagrass condition, significantly reducing human error and speeding up the data collection timeline. By prioritizing FOSS, we ensure that the community is not dependent on expensive proprietary licenses to maintain their own data. This project is set to generate Metinaro’s first baseline seagrass data and produce maps that are grounded in local knowledge and community ownership. Attendees will learn how the integration of DroneTM, Mapillary, and KoboToolbox creates a replicable framework for marine conservation in data-scarce environments. Beyond the tech, we demonstrate how this participatory approach shifts the power to the community, providing the evidence-based insights they need to independently prioritize high-risk areas for conservation. We are moving toward a future where seagrass protection in Timor-Leste is not just streamlined, but led by locally-owned, data-driven stewardship. false https://talks.osgeo.org/foss4g-2026/talk/FDPFRV/ Conference Management Room3 General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 Open-source border datasets have limitations in contested regions due to geometric simplification and differing boundary definitions. Using the Bhutan–China border case, this study shows village locations may not align with available boundaries. Single-line representations can mislead; multiple boundaries help, but such data are often incomplete. foss4g-2026-5326-lack-of-open-source-border-datasets-on-sensitive-border-areas-the-case-of-the-china-bhutan-border-and-arunachal-pradesh Jo Sakai en Open-source border datasets such as GADM, Natural Earth, and OpenStreetMap provide broad coverage but have limitations for fine-scale mapping, especially in contested regions. Boundary mismatches at high zoom often result not only from geometric simplification, but also from differences in how boundaries are defined across datasets. Coverage may also be uneven in conflict-affected areas, and representations of disputed boundaries can vary depending on available sources and mapping practices. As a result, these datasets can complicate detailed visualizations used in journalism and research. This presentation examines the Bhutan–China border and Arunachal Pradesh, where Chinese “cross-border villages” can be precisely located using satellite imagery (Barnett, 2024; New York Times, 2024) , but do not always align with available boundary data. Such mismatches reflect both geometric limitations and the existence of multiple boundary interpretations. In areas with overlapping claims, no single authoritative boundary may exist. Representing these regions with a single line can therefore be misleading; instead, overlaying multiple boundary representations can help communicate uncertainty and differing interpretations, although such alternative boundary data are often incomplete or unavailable. Barnett, R., 2024. Forceful Diplomacy: China’s Cross‑Border Villages in Bhutan. Turquoise Roof, 15 October 2024. Available at: https://turquoiseroof.org/forceful-diplomacy-china-cross-border-villages-in-bhutan/ (Accessed 23 March 2026). Xiao, M. and Chang, A., 2024. China’s Great Wall of Villages. The New York Times, 10 August 2024. Available at: https://www.nytimes.com/interactive/2024/08/10/world/asia/china-border-villages.html (Accessed 23 March 2026). false https://talks.osgeo.org/foss4g-2026/talk/FQ3BSJ/ Conference Management Room3 General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 V-World, Korea’s national geospatial platform by MOLIT, provides 2D/3D data and APIs. Supported by Digital Twin Platform policy, it advances 3D services and analytics. This study reviews operations, recent upgrades, and future plans including improved data management and Geo-AI integration foss4g-2026-5099-v-world-platform-enhancement-for-digital-twin-based-geospatial-services Song Pyo Hong(Spatial Information Industry Promotion Agency) en Recently, geospatial information has been widely used as foundational data in various fields, including urban management, disaster response, environmental analysis, and smart cities. Therefore, it has become increasingly important to establish national platforms that can efficiently provide and utilize spatial data. In this context, V-World was developed as a public geospatial service platform linked with the National Spatial Information Integration Platform (K-GeoP). The platform provides a range of services, including map services, spatial analysis tools, data downloads, and Open APIs, to support both public and private service development. Currently, V-World is connected to numerous systems through its Open API and data download services. It is utilized in diverse fields such as urban planning, transportation, environment, and disaster management. By providing 3D spatial data and policy-related datasets, the platform also supports spatial data–based decision-making. Under the Digital Twin Platform (DTP) policy, V-World is being improved to strengthen spatial data utilization. The first phase of improvement expanded spatial analysis functions, enhanced public services, and introduced integrated above-ground and underground spatial information services. In addition, features such as user-customized map creation, support for various data formats, and automatic data management were introduced to improve the spatial data environment. In the future, the second phase will strengthen the digital twin–based system for spatial data collection, management, and provision. It will also improve the management of large-scale spatial data and ensure interoperability based on established standards. Furthermore, spatial analysis and simulation environments will be developed, and Geo-AI technologies will be applied to expand spatial analysis capabilities. Through these efforts, V-World aims to evolve from a simple spatial data service platform into core infrastructure that supports digital twin–based land management and data-driven decision-making, while expanding the geospatial ecosystem utilized by both the public and private sectors. false https://talks.osgeo.org/foss4g-2026/talk/FYBAPK/ Conference Management Room3 General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 Re:Earth is an open-source data platform. Re:Earth and Eukarya have been engaged in various initiatives related to peace and education in many different ways. In this session, in conjunction with the FOSS4G conference being held in Hiroshima, we will introduce our initiatives related to peace and education. foss4g-2026-5194-does-re-earth-dream-of-a-peaceful-earth-re-earth-initiatives-for-peace-and-education Michi Okada en Eukarya Inc., the company developing and operating the open-source data platform Re:Earth, was founded by members of the laboratory of Professor Hidenori Watanave, known for creating the Hiroshima Archive. Re:Earth was developed with the vision of democratizing digital archives—making it possible for anyone, non-engineers, to create and share them easily. Since its launch, Re:Earth has been used in a variety of projects related to peace and education, including preserving the memories of atomic bombing survivors, visualizing war-related sites, and supporting peace education for younger generations. In this session, taking the opportunity of FOSS4G being held in Hiroshima, we will introduce these initiatives and explore how open geospatial platforms can contribute to preserving memory and supporting education. false https://talks.osgeo.org/foss4g-2026/talk/G8D98U/ Conference Management Room4 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 This study applies Sentinel-2 satellite imagery and open-source Python tools to detect and map quarry pond remnants on Kitagi Island, a designated heritage site in Japan's Seto Inland Sea, revealing spatial distributions consistent with the island's historical quarrying records. foss4g-2026-5591-detecting-quarry-pond-remnants-on-a-japanese-island-heritage-site-using-sentinel-2-imagery-and-open-source-remote-sensing-tools /media/foss4g-2026/submissions/GC3KYK/815be010-668c-4c3c-91e8-d941acaf3e22_Nnmm5BP.jpg Noboru Otsuka en Kitagi Island (Kitagi-shima), located in Kasaoka City, Okayama Prefecture, Japan, has been a center of granite quarrying since the early 17th century. At its peak in 1957, the island hosted 127 active quarry sites (called "dojo") with a population of up to 12,000 people. As the quarrying industry declined, the abandoned sites filled with rainwater and groundwater, forming isolated ponds enclosed by steep granite walls. In 2019, the island's stone culture was designated as part of Japan's national heritage under the "Stone Islands of Setouchi" program. Despite their heritage significance, no systematic spatial inventory of these quarry pond remnants has been published. This study applies Sentinel-2 L2A satellite imagery—retrieved via the Microsoft Planetary Computer STAC API—to detect and map water bodies on Kitagi Island using NDWI (Normalized Difference Water Index) and MNDWI (Modified NDWI). To improve detection of small water bodies subject to spectral mixing at 10 m resolution, we used a composite union condition (NDWI > −0.2 OR MNDWI > −0.1) combined with an NDVI-based vegetation mask. We compared spring imagery (March 2025, 0.0% cloud cover) and summer imagery (August 2025, 0.7% cloud cover) to characterize seasonal detection differences. Spring imagery detected 113 intra-island water polygons (≥100 m²) with a maximum area of 1.28 ha. Summer imagery detected 145 polygons, with spatial concentrations in the northern, southeastern, central, and western parts of the island—a distribution consistent with historical records of quarry locations. The NDVI vegetation mask contributed minimally to exclusion (9 pixels), suggesting that quarry ponds and vegetation zones do not substantially overlap in this granite-dominated landscape. Results are exported as GeoJSON and GeoTIFF for use as base data in future fieldwork and heritage documentation. The analysis pipeline uses exclusively open-source Python libraries: rasterio, numpy, shapely, pystac-client, planetary-computer, and folium. This poster presents the methodology, detection results, and their correspondence with the island's quarrying history, and discusses the potential for extending the approach to other quarried islands in the Seto Inland Sea. false https://talks.osgeo.org/foss4g-2026/talk/GC3KYK/ Conference Management Room4 General session talk 2026-09-02T11:30:00+09:00 11:30 00:30 We present an open, global, building-level resolution, reproducible and dynamic exposure model with the aim to provide global exposure data on the building level for applications in natural hazard risk estimation, resilience planning, disaster recovery, rapid loss assessments and humanitarian tasks foss4g-2026-5240-every-building-on-earth /media/foss4g-2026/submissions/HAUFHA/Vesuvius_circle_m21fuS3.png Danijel Schorlemmer en The built environment is, globally speaking, the largest unknown in the understanding of the effects of disasters and in assessing their risk. This includes not only the location of buildings but also their size, occupancy, structural type, vulnerability and value. Detailed knowledge of it is necessary for many tasks in disaster risk reduction but also in other fields, e.g. climate-related sustainability, urban planning and management, insurance and re-insurance. While in well-regulated countries cadastral data is available that provides various details about the buildings, in most parts of the world such information is lacking. In some areas not even the locations of buildings and settlements are known to the authorities. Buildings, the core part of the built environment, can be strongly mixed within small areas in their structural types, sizes, shapes and number of people in them and the socio-economic structure can vary highly on these scales. This heterogeneity cannot adequately be described by classical exposure models that provide aggregated building data over larger areas. A global model describing the built environment at the scale of individual buildings has never been achieved, nor has such a model been dynamic, with continuous updates reflecting changes in input data. Here, we present a global, building-level resolution, open, reproducible and dynamic exposure model with the aim to provide global exposure data on the building level. This model is based on volunteered geographic information, predominantly OpenStreetMap and open data that is created with earth observation and machine learning, e.g. the building footprints of the Google Open Buildings and Microsoft ML Building Footprints, and the Global Human Settlement Layer to estimate the extent of built area. Further datasets like EUBucco and full 3D building geometries are added where available and the height information covering approx. 70% of all buildings is used to further create 3D models at the Level-of-Detail 1. The distribution of different structural types of buildings per region are taken from open aggregated exposure models or developed from cadastral data. Every building is assessed separately and its exposure indicators are computed deterministically, where possible, or probabilistically. This level of detail is necessary when it comes to localized hazards, such as strong shaking of earthquakes, floods or tsunamis due to local site conditions. In particular 3D buildings are now becoming part of the next-generation seismic risk framework. The model covers every country and territory globally and is to a large degree building complete with approx. 3 billion buildings described in detail. false https://talks.osgeo.org/foss4g-2026/talk/HAUFHA/ Conference Management Room4 General session talk 2026-09-02T13:00:00+09:00 13:00 00:30 The IFRC launched the Global Crisis Data Bank (Montandon) — the world's largest repository of natural hazard and impact data, enabling evidence-based decisions for financial and operational crisis planning. This talk covers how we're building a harmonized data repository using open standards like STAC for fast, reproducible analysis. foss4g-2026-5406-open-standards-and-tools-to-accelerate-global-crisis-response /media/foss4g-2026/submissions/HYNJ9W/1_PPvWDYiGeyNh8U9lS3KHPA_23u8lw6.jpeg Sajjad Anwar en ### Background The latest Emergency Events Database (EM-DAT) report recorded 393 natural-hazard related disasters. We are collectively experiencing this, and we recognise that this number is trending upwards. As a rule-of-thumb, that's at least one disaster every day of the year. This scale is staggering and requires major shifts in our humanitarian response operations. When a crisis happens, a data crisis unfolds. Several agencies are trying to build a situational report, learn from previous response strategies, and their impact while lives are at stake. IFRC is trying to solve this data crisis through Montandon. IFRC started Montandon initiative with support from UNDRR, UN OCHA, and the WMO. The Montandon's goal is to create a centralized database harmonizing hazard, impact, and corresponding response data for every event from various sources, including EM-DAT and DesInventar. This addresses crucial gaps in the humanitarian data ecosystem, with a focus on IFRC’s own National Societies, to understand the past and better prepare for the future. Montandon will be the foundation for risk and forecast models and systems and a dynamic database constantly reflecting the humanitarian community's approaches. ### Building Montandon We helped IFRC build the foundation of Montandon using open standards and tools. The humanitarian data ecosystem is historically siloed but the choice of technology, and metadata can play a huge role in breaking these silos and create fundamental shifts in evidence-based decisions. Montandon uses STAC, and custom but extensible Montandon STAC extension. This is based on years of humanitarian data management experience and a consultation with groups like IFRC, ESA, and NASA. This infrastructure is AI-ready, allows caliberating models, trigger anticipatory action, and streamline disaster response globally. Reproducible analysis is at the core of Montandon. With humantarian funding under stress, it is important to imagine workflows that are easy to replicate and scale. This talk will present Montandon, the design philosophy, data taxonomy, and share how this approach is supporting real-world crisis response. false https://talks.osgeo.org/foss4g-2026/talk/HYNJ9W/ Conference Management Room4 General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 This paper presents the current state of development of a spatial data infrastructure, based on GeoNode, focusing on data applied to natural disasters, especially geological hazards and susceptibility to mass movements and flooding. foss4g-2026-5479-geosdc-a-spatial-data-infrastructure-to-support-geological-risk-management-in-santa-catarina-state-brazil Carlos Eduardo MotaMauricio MarinoMatheus Klein FlachTiago Antonelli en The Secretariat of State for Protection and Civil Defense of Santa Catarina State, Brazil (SDC), in partnership with the Geological Survey of Brazil (SGB), is leading a strategic initiative for territorial risk management in the state. Through a Research, Development, and Innovation agreement, this collaboration encompasses the development of risk mapping and the implementation of a Spatial Data Infrastructure (SDI) platform. This joint effort addresses the historical lack of critical data in several municipalities, enabling risk reduction, disaster prevention, and the optimization of land-use planning. To support and enhance this large-scale generation of knowledge, the project relies on the implementation of an advanced Spatial Data Infrastructure based on GeoNode 5.0. This open-source platform serves as the technological core of the initiative, integrating maps, metadata catalogs, and databases into a single high-performance ecosystem. By consolidating this robust database, the SDC aims to eliminate data fragmentation and ensure that all geospatial layers adhere to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), thereby enhancing technical efficiency and institutional agility in risk-related information management. The decision to choose GeoNode as a replacement to the proprietary SDI software is justified not only by the absence of a license cost, but also by its user-friendly interface for non-GIS specialists, ease of content management, and the high level of software customization potential. The challenge lies in building a team of human resources within the SDC capable of maintaining and disseminating SDI throughout the organization. The GeoSDC project began in January 2026 with the deployment of two GeoNode environments in the SDC infrastructure - testing and production. After installation and initial contact with SDI, activities began to customize the "look and feel," as well as including State Government’s visual identification elements. The need to translate Geonode 5 terms into Brazilian Portuguese was also identified, and also the definition of authentication and authorization mechanisms. In parallel, an inventory of SDC data is underway, whose portfolio consists of various types of formats – from vector data and spreadsheets to multidimensional grids of meteorological data. The order of magnitude of the number of datasets is estimated at 20,000 items. Meteorological datasets, in particular, constitute a major challenge to overcome, as they are information that is produced daily and has a high volume of traffic and storage. GeoSDC will be integrated into the SGB's spatial data infrastructure through automated metadata harvesting from spatial datasets and documents, enabling the indexing of selected SGB data directly from the GeoSDC infrastructure. Furthermore, some SGB products, such as Mass Movement and Flood Susceptibility maps, will be directly synchronized between the two SDIs. The GeoSDC SDI is expected to be fully operational and publicly accessible by the end of 2027. false https://talks.osgeo.org/foss4g-2026/talk/JHUY7N/ Conference Management Room4 General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 We share how the Open Exposure Taxonomy integrates several open data taxonomies and improves the quality of data for disaster risk. foss4g-2026-5644-using-building-exposure-taxonomies-in-hot-workflows Sam WoodcockDoren Calliku en Geospatial exposure data is essential for disaster risk assessment, but it is often fragmented across independent taxonomies such as the Prompt Assessment of Global Earthquakes for Response (PAGER), the Global Earthquake Model (GEM), the OASIS Loss Modelling Framework (OASIS LMF), and the Building Stock Observatory (BSO). Each framework provides valuable information, but differences in terminology and structure limit integration and reuse. OpenStreetMap (OSM) offers a strong foundation to address this challenge. It supports an open ecosystem focused on disaster risk management, with tools such as the iD Editor, the HOT Tasking Manager, and Field Tasking Manager (FieldTM) for coordinated field campaigns. Combined with mobile data collection tools such as OpenDataKit (ODK) and QField, OSM enables large-scale, collaborative mapping across both remote and in-situ workflows. The Open Exposure Taxonomy (OXT) builds on this foundation by providing a structured way to integrate exposure-related information into the OSM ecosystem. It uses a YAML-based schema to define building attributes in a consistent and machine-readable format. This allows concepts from different exposure taxonomies to be aligned and translated while remaining compatible with OSM tagging practices. Through exporters and mappers, the taxonomy can be transformed into documentation, survey forms, and editor presets, enabling direct use within OSM workflows. OSM focuses on features that can be observed in the field, while exposure models rely on attributes that are often derived from statistical sources. OXT bridges this gap by separating observable attributes from exposure attributes. Observable properties follow standard OSM tagging, while exposure-related values are represented using controlled ranges that reflect uncertainty. In this way, OSM serves as both a semantic and infrastructural backbone, and OXT extends it with additional structure without altering core mapping principles. In practice, OXT integrates into HOT workflows through forms and presets generated from the taxonomy. Contributors can map buildings using standard OSM tags and, where appropriate, enrich them with additional exposure information collected through tools such as ODK or QField. OSM-relevant attributes are contributed directly to the open dataset, while exposure-related information, particularly data that may be sensitive or derived, is managed separately and used for analysis only in aggregated form. By embedding this approach into existing OSM tools, contributors can work within familiar environments while producing more consistent data. The resulting datasets can be exported and directly used in exposure modeling platforms such as the Global Dynamic Exposure (GDE) model, enabling rapid estimation of risk, improved post-event assessments, and more consistent integration between field data collection and analytical workflows. false https://talks.osgeo.org/foss4g-2026/talk/JPVSXA/ Conference Management Room4 General session talk 2026-09-02T14:30:00+09:00 14:30 00:30 Technology can scale solutions, but the impact on the ground happens through human action. Drawing on HOT’s experiences from around the world, this talk shows how FOSS tools when paired with collaboration and local engagement empower communities to become local changemakers. foss4g-2026-4835-the-power-of-local-connections-for-social-impact-in-a-technocratic-world-experiences-from-hot Leen D'hondtHoney Fombuena en Open, accessible technology is essential for social impact, but lasting change depends on the people behind it. At the Humanitarian OpenstreetMap Team (HOT, hotosm.org) we build open source tools to lower the barrier for anyone, in particular people in vulnerable communities, to access geospatial technology for disaster management and development challenges. Technology alone is not enough. Engaging people in software development, tool usage, and securing local ownership ensures solutions are meaningful and trusted. This presentation shares examples from Latin America, Africa, and Asia, demonstrating how the combination of accessible technology and strong human connections can empower communities to become active changemakers. false https://talks.osgeo.org/foss4g-2026/talk/K7MCCM/ Conference Management Room4 General session talk 2026-09-02T15:30:00+09:00 15:30 00:30 Communities face urgent societal and environmental challenges, while geospatial technologies and AI advance rapidly. Our curriculum uses scaffolded project-based learning to teach an applied three-tier GIS system with cloud-based open-source tools and AI workflows, enabling students to design actionable, socially responsive, and environmentally regenerative geospatial solutions. foss4g-2026-5231-responding-to-rapid-ai-innovation-and-real-world-environmental-challenges-teaching-applied-three-tier-gis-systems-for-regenerative-futures Qian (Chayn) SunShinjita Das en Communities worldwide face urgent societal and environmental challenges, including climate change, urban heat, rapid urbanization, and social inequities. At the same time, geospatial technologies and AI are advancing at unprecedented speed, creating opportunities for actionable spatial solutions while exposing a critical skills and knowledge gap among graduates. Traditional GIS teaching, focused primarily on system development, is insufficient to prepare students to integrate AI-driven workflows with real-world applications effectively. To address this gap, our curriculum centers on scaffolded project-based learning, guiding students to progressively master an applied three-tier GIS system architecture—Presentation, Application Logic, and Data layers—within cloud-based open-source GIS platforms enhanced with AI workflows. Each project is designed to close the loop between technology and real-world pressing issues, enabling students to translate complex spatial and socio-environmental data into actionable insights, maps, and reports that inform stakeholder-focused solutions. Scaffolded projects gradually increase in complexity, helping students gain technical competence in system design while developing strategic understanding of applying GIS and AI to societal and environmental challenges. Students learn a range of industry-relevant tools, including PostGIS for spatial databases, QGIS for geospatial analysis, GeoAI plugins for AI-assisted workflows, Cesium for 3D visualization, Google Earth Engine (GEE App) for environmental monitoring, and Leaflet and Mapbox for interactive web mapping. These tools are embedded in projects that address regenerative futures challenges such as urban heat mitigation, green infrastructure planning, disaster resilience, and sustainable urban development. Student learning is tracked at multiple stages. Initial responses capture prior knowledge, experience, and confidence in GIS, AI, and spatial problem-solving. During the course, students develop individual cloud-based GIS projects applying the tools to authentic challenges. End-of-course responses and reflective statements highlight progression in adaptive skills, critical thinking, and stakeholder-oriented problem-solving, demonstrating how students synthesize knowledge from geospatial science, computer science, and social-environmental studies. Examples from student projects show how scaffolded learning enables students to apply the three-tier architecture in practical contexts, integrate AI-assisted analysis, and produce actionable geospatial solutions. By embedding system development within project-based learning, the curriculum ensures that graduates acquire technical expertise and the judgment necessary to apply geospatial technologies meaningfully, addressing both rapid technological change and pressing societal-environmental needs. This innovative curriculum provides a comprehensive model for adaptive GIS education, linking scaffolded project learning, applied three-tier system development, AI integration, cloud-based open-source GIS tools, and real-world problem solving. It prepares graduates to design innovative, socially responsive, and environmentally regenerative solutions, equipping them to navigate the rapidly evolving landscape of geospatial technology and societal challenges. false https://talks.osgeo.org/foss4g-2026/talk/LV9Z3P/ Conference Management Room4 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 Open-source geospatial tools and GeoAI can be used to map flood risk and support early warning systems in informal settlements. Using Sentinel-2 data, machine learning, and socio-economic indicators, it presents a scalable workflow for improving climate resilience and disaster risk management in resource-constrained environments. foss4g-2026-5646-geoai-for-social-impact-open-source-flood-risk-mapping-and-early-warning-systems-in-informal-settlements-of-south-africa EMMANUEL en Across South Africa and much of the Global South, informal settlements are often located in environmentally vulnerable areas such as floodplains, riverbanks, and poorly drained land. These communities face disproportionate exposure to climate-related hazards, compounded by limited access to reliable data, early warning systems, and formal planning frameworks. The talk presents a practical, scalable GeoAI-driven approach to flood risk mapping and early warning using entirely open-source tools and openly available datasets. The workflow integrates satellite imagery from Sentinel-2, digital elevation models (DEMs), rainfall data, and socio-economic indicators within platforms such as Google Earth Engine, QGIS, and Python-based libraries. Spectral indices such as NDVI and NDWI are derived to characterize vegetation and surface water dynamics, while terrain variables and hydrological features are incorporated to improve flood susceptibility modelling. A key component of the methodology is the application of machine learning techniques to classify flood-prone areas and generate risk surfaces. Importantly, the model goes beyond physical hazard mapping by integrating social vulnerability indicators, enabling a more holistic understanding of risk that reflects both environmental exposure and community sensitivity. The result is a multi-dimensional flood risk map that can support targeted interventions and resource allocation. Using case studies from Gauteng Province, the presentation demonstrates how this open-source workflow can identify high-risk zones within informal settlements, validate model outputs, and produce actionable insights for disaster management practitioners. The talk will also highlight practical challenges encountered, including data quality issues, model transferability, and implementation constraints in resource-limited settings. By sharing lessons learned and reproducible methods, this presentation aims to contribute to the broader FOSS4G community by showcasing how open geospatial technologies can be applied to real-world problems. It emphasizes the role of open data, collaborative tools, and accessible platforms in bridging information gaps and supporting climate resilience. Ultimately, the session seeks to inspire practitioners, researchers, and policymakers to adopt and adapt open-source geospatial solutions for disaster risk reduction and sustainable urban development. false https://talks.osgeo.org/foss4g-2026/talk/MENVQT/ Conference Management Room4 General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 This research develops interactive peace education platforms integrating GIS with storytelling functions. In 2025, all atomic bomb–related sites within five kilometers of Hiroshima’s hypocenter were mapped on Re:Earth. By layering historical and global data, the platform encourages users worldwide to reflect on peace and their own responsibilities. foss4g-2026-5185-mapping-peace-a-gis-based-storytelling-platform-for-global-peace-education-from-hiroshima Hiroshima Funairi High School en This research aims to create an interactive digital platform that encourages people around the world engaged in peace education to explore the meaning of peace from multiple perspectives and discover new values related to it. By integrating geographic information systems (GIS), storytelling, and multimedia resources, the platform seeks to make learning about war, nuclear weapons, and peace more accessible, engaging, and personally relevant. Our ultimate goal is to encourage users not only to learn about the past but also to reflect on their own attitudes and future actions toward peace. A key feature of this research is the use of GIS technology, which allows different types of information to be layered on a digital map, enabling users to visually understand spatial relationships and compare information from different places at a glance. This method is particularly effective for peace education because it connects historical events to specific locations and helps users comprehend complex global issues through spatial perspectives. In 2025, we mapped all known atomic bomb–related sites located within a five-kilometer radius of the hypocenter in Hiroshima using the Web-GIS platform Re:Earth. These include approximately ninety-six monuments and remains that preserve the memories and realities of the devastation caused by the atomic bomb. Currently, we are expanding the spatial scope of the project and increasing the amount and diversity of information on the platform. By layering a wider range of datasets, we aim to strengthen the platform’s role as an information-rich resource for peace education. Storytelling also plays a central role in the platform. Through personal narratives, users can experience the lives of individuals connected to the atomic bombing and gain a deeper emotional understanding of historical events. Many people feel that topics related to war or nuclear weapons are distant or difficult to understand. By presenting information through narratives and interactive media, we aim to make peace education more relatable and meaningful. As part of this research, we conducted interviews with fifty-five individuals from seventeen countries, including the United States, France, Germany, and the Philippines, and incorporated their messages for peace into the platform. We also created narrative content about three individuals connected to the atomic bombing: a third-generation atomic bomb survivor who shares messages of peace through dance while conveying her grandmother’s testimony; Eizo Nomura, a survivor exposed very close to the hypocenter who later suffered from the aftereffects of radiation; and Chiyo Miyazaki, the daughter of a Korean second-generation survivor, whose story highlights the experiences of foreign forced laborers who were also victims of the bombing. These stories are presented through maps, photographs, videos, and text, alongside visual features such as three-dimensional models showing the number of nuclear weapons possessed by each country and augmented reality reconstructions of the Nakajima district before the bombing. Furthermore, we are developing concrete peace education curricula that utilize this GIS platform as a learning tool. false https://talks.osgeo.org/foss4g-2026/talk/NQDC83/ Conference Management Room4 General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 This talk explores the co design process and technology stack behind a visual cultural database for Indigenous organisations in Australia, using open source geospatial tools and rapid field based software prototyping to make maps and media more accessible for non GIS users foss4g-2026-5511-a-cultural-database-for-down-to-earth-users-open-source-maps-media-and-design-in-indigenous-australia Staf Smith en This presentation will focus on the co design process behind a visual cultural database for Indigenous organisations in Australia, built using open source geospatial technologies. Rather than concentrating only on the final platform, the talk will reflect on the practical realities of designing spatial systems in close collaboration with users in remote and regional settings. A key part of the process has involved travelling to ranger bases and community locations, working side by side with Traditional Owners and ranger teams to gather requirements, discuss workflows, and sketch interface ideas in real time. With a mobile off grid 4WD truck set up as a travelling office, including Starlink connectivity and onboard power, this has made it possible to spend days on Country doing design conversations and UI mockups, then rapidly prototype ideas at night while still in the field. The talk will also reflect honestly on the strengths and limits of this approach. Rapid prototyping with large language models made it possible to test and communicate interface ideas very quickly, but it also led at times to shortcuts and rework when early prototypes were pushed too far without enough consolidation. Part of the story is therefore not just about speed, but about learning when fast iteration helps and when systems need to be rebuilt more carefully. Alongside this process, the presentation will touch on the open source tools that supported the work, including MapLibre, React, and Django, and how they have been used to build a map and media focused platform that is visually engaging and accessible for non GIS users. The session will be relevant to people interested in open source GIS, participatory and co design methods, field based requirements gathering, user experience, and building spatial systems for audiences beyond professional GIS specialists. false https://talks.osgeo.org/foss4g-2026/talk/P8ZVSM/ Conference Management Room4 General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 This study introduces a three-dimensional GIS-based wildfire fireline visualization system developed using open-source GIS technologies. The ultimate goal is to further develop the system into a real-time wildfire monitoring platform that can support rapid and effective responses to large-scale wildfires in the future. foss4g-2026-5305-development-of-a-wildfire-fireline-visualization-pilot-system-and-its-enhancement-plan-using-open-source-gis-technologies /media/foss4g-2026/submissions/PG3N79/foss4g2026_nR92ZXy.png Juhyeon GimYOUNGWOOK YIMdawoon Kim en In recent years, the impacts of climate change have led to a continuous increase in the scale, speed, and simultaneous occurrence of wildfires. As a result, there is a growing need to establish response systems capable of rapidly and accurately identifying the extent of wildfire damage, as well as the rate and direction of fire spread. In particular, technologies that quantitatively generate and utilize wildfire fireline information from the initial ignition stage through the spread phase play a critical role throughout the entire field response process, including resource allocation, evacuation planning, and the development of firefighting strategies. To address this need, a pilot system capable of real-time visualization of wildfire firelines and hotspots has been developed as one of the foundational technologies for wildfire response systems. First, three-dimensional building and terrain data were generated using mago 3DTiler and mago 3DTerrainer and visualized in a web environment based on CesiumJS. In addition, a spatial data management environment based on GeoServer and PostgreSQL was established to collect and manage various datasets required for wildfire monitoring. Within this 3D GIS visualization environment, wildfire fireline and hotspot data in GeoJSON format were displayed, and additional functions were implemented to facilitate easy inspection and analysis of the data. The ultimate goal of the wildfire fireline visualization pilot system is to collect fire hotspot and fireline data obtained from various observation platforms in an integrated manner, visualize them in real time, and provide diverse visualization functions that support decision-making in wildfire response. Currently, collaborating research institutions are also developing a fireline extraction algorithm based on an integrated three-tier observation framework combining ground-based, aerial, and satellite observation resources. The results of this algorithm will be visualized through the system. Through further advancement of this pilot system, it is expected to serve as a foundational technology that supports the integrated utilization of wildfire observation data and enhances real-time situational awareness in wildfire monitoring and response. false https://talks.osgeo.org/foss4g-2026/talk/PG3N79/ Conference Management Room5 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 OGC API Environmental Data Retrieval is a powerful new API standard for accessing time series data about geospatial areas. This session will explain how EDR standardizes time series data access and how it can be integrated with RDF vocabularies and JSON-LD to standardize cross-organizational terminology. foss4g-2026-5230-standardizing-geospatial-time-series-access-with-ogc-api-edr-and-rdf-linked-data-standards Benjamin WebbColton Loftus en OGC API Environmental Data Retrieval (EDR) allows for a convenient access pattern of time series data within a geospatial region. EDR standardizes many common array based data storage formats like Zarr, GeoParquet, and NetCDF into a common web-accessible OGC API. However, until now, one of the historical challenges with standardizing data with common APIs is that semantically equivalent parameters may be named differently in different data sources. For instance, “Topographic height” and “Elevation above Sea Level” may refer to the same information, but have poor UX when combining together in the same API due to disparate parameter names. This talk will reference how W3C Linked Data Resource Description Framework (RDF) standards and vocabularies like SKOS can be used to solve this semantic access issue. We will reference how you can use a semantic resolution service in your OGC API servers to map parameters to a canonical term and in doing so, improve the UX of your data integrations. For instance, instead of referencing the raw string “Elevation above Sea Level” a client can provide the standardized schema.org RDF vocabulary term “schema:elevation” and abstract away the intricacies of each data source while preserving data provenance. This talk will reference water data hubs created for the United States Bureau of Reclamation and Arizona State University that use such tools and methodology. false https://talks.osgeo.org/foss4g-2026/talk/9FN7PW/ Conference Management Room5 General session talk 2026-09-02T11:30:00+09:00 11:30 00:30 A lightweight Geospatial Data Gateway built with Go that composes spatial features from multiple databases and APIs at request time, serving enriched GeoJSON through OGC API Features standard without upfront data preparation. foss4g-2026-5211-geospatial-data-gateway-composing-spatial-features-across-databases-and-apis Athitaya Phankhan en In our work with geospatial data, we store GeoJSON Features in MongoDB as the primary source, complete with geometry and standard properties. But when collaborating with multiple agencies, related data is scattered across different database systems and external APIs from various organizations. Whenever a complete picture is needed, teams must go through a process of gathering, preparing, and transforming data from each source before it can be used together, a process that is time-consuming, difficult to maintain, and must be repeated every time the upstream data changes. This talk presents a lightweight Geospatial Data Gateway built with Go that takes a different approach. Instead of preparing data in advance, it composes it at request time. The gateway reads spatial features from MongoDB as the primary source, then performs virtual joins by fetching related properties from other data sources such as SQL Server, Elasticsearch, or external APIs. The composed result is served as a geospatial data service following the OGC API Features standard, with responses in GeoJSON format, making it immediately consumable by any OGC-compatible GIS client without custom integration. This approach was born from real-world challenges of working across agencies where data lives in both databases and APIs across different systems. By composing at request time and delivering through a standards-compliant geospatial service, teams can build interoperable spatial APIs faster, with less infrastructure and fewer barriers to cross-agency collaboration. All components are open source and designed to work with data you already have, exactly where it already lives. false https://talks.osgeo.org/foss4g-2026/talk/AUMRSA/ Conference Management Room5 General session talk 2026-09-02T13:00:00+09:00 13:00 00:30 Released 1.5 years ago, GeoParquet 1.1's features had evolving ecosystem support in late 2025. We built a custom spatial indexing pipeline using Dagster and GeoPandas while waiting for native tools to fully mature. foss4g-2026-5585-bridging-the-gap-practical-spatial-partitioning-of-geoparquet-in-an-evolving-ecosystem Mitsuha Miyake en **Ecosystem Gaps** In late 2025, while GeoParquet 1.1 had been available for over a year, practical library support for its spatial partitioning and metadata was still catching up. As a data engineer managing large point datasets on S3, we needed a way to leverage these features while the community's toolset was in development. **Architecture: Decoupling Metadata and Data** To avoid inefficient full-table scans and redundant downloads, we implemented a practical indexing strategy: * Manual Indexing: We built a secondary GeoParquet "Catalog" containing polygon boundaries and partition keys to act as a spatial index. * Orchestration: Using Dagster, we managed the dependency between this catalog and the primary data processing, ensuring consistent partitioning. * Efficient Filtering: Using GeoPandas, we queried the catalog first, fetching only the necessary data fragments from S3. **Reflections on a Shifting Landscape** Since the project’s inception, the ecosystem has moved quickly. Tools like DuckLake and various query engines are increasingly adding native support for these spatial operations, potentially turning our "manual bridges" into legacy code. This talk reflects on the engineering mindset required during the gap between a new standard and its adoption. Sometimes we build our own wheels, and sometimes we are happy to see them replaced by the community. **Key Takeaways** 1. Designing custom spatial indexing with GeoParquet 2. Practical patterns for Dagster and GeoPandas in geospatial data pipelines false https://talks.osgeo.org/foss4g-2026/talk/AYWHPP/ Conference Management Room5 General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 Global demand for GIS is rising, but countries adopt 3D city models at different stages. This talk explores Japan’s Project PLATEAU in its mature phase and shares how open Web GIS platforms help initiate small-scale GIS projects abroad—highlighting disaster resilience initiatives in Peru. foss4g-2026-5181-from-gis-visualization-needs-to-3d-city-models-the-current-trajectory-of-project-plateau-in-a-global-context Haruka YasudaEukarya Inc.Misaki Baba en The global demand for GIS is rapidly increasing across domains such as urban planning, disaster management, infrastructure monitoring, and climate adaptation. In response, many governments and cities have begun developing 3D city models as the next generation of geospatial infrastructure. However, the global landscape of 3D urban data development is far from uniform. Some countries have already established advanced ecosystems where large-scale 3D city models are systematically developed and openly published. In contrast, many other regions are still at an earlier stage where the immediate need is simply to visualize and share geospatial data through GIS platforms. As a result, a clear gradient of adoption exists—from basic GIS visualization to sophisticated 3D urban digital infrastructure. Japan represents one of the countries where 3D urban data initiatives have advanced significantly. Led by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Project PLATEAU has developed and openly published 3D city models across numerous municipalities. After several years of rapid expansion, the project is now entering a more mature phase. This stage presents new opportunities: learning from international approaches while also sharing Japan’s experiences and lessons with the global geospatial community. In this context, Eukarya Inc., a company involved in Project PLATEAU and developer of an open Web GIS data platform, has been working with the Japanese government and international development agencies to explore how similar initiatives can be introduced in other countries. This talk presents how these projects are being implemented abroad through practical case studies, focusing particularly on situations where the demand for full-scale 3D city models does not yet exist. Instead of starting with complex infrastructure, these initiatives often begin with small, practical GIS visualization projects that address immediate local needs. Through iterative development and stakeholder collaboration, such projects can gradually evolve into more advanced geospatial ecosystems. A key example discussed in this session is Peru, where GIS data platforms are being introduced to support disaster risk management and resilience planning. The case illustrates how governments, international organizations, and technology providers collaborate to establish accessible geospatial tools that can scale over time. By sharing these experiences, this talk highlights practical strategies for initiating geospatial initiatives in emerging contexts and discusses how open standards, open data, and open-source technologies can support sustainable growth toward 3D city models. The session is intended for a wide audience involved in the geospatial ecosystem, including government agencies, technology providers, researchers, municipalities, and community members who ultimately benefit from open geospatial data. It aims to foster discussion on how the global FOSS4G community can collaborate to make advanced geospatial technologies more accessible and impactful worldwide. false https://talks.osgeo.org/foss4g-2026/talk/QCWDAW/ Conference Management Room5 General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 Official statistics lack impact without spatial context. This talk presents UNICEF’s open-source stack - GeoRepo, GeoSight, and SDMXConnector - enabling seamless integration of SDMX data with harmonized boundaries. We address key challenges and show how to transform complex indicators into map-ready insights for better decision-making and GeoAI readiness. foss4g-2026-5607-turning-statistics-into-maps-with-unicef-s-open-source-geospatial-solutions Jan Burdziej en Official statistics are critical for decision-making - but without a spatial dimension, their full value often remains untapped. Maps transform tabular indicators into actionable insights, revealing geographic inequalities, trends, and hotspots that are otherwise difficult to detect. Yet, integrating statistical data - especially from SDMX (Statistical Data and Metadata eXchange format) into geospatial workflows remains complex and inaccessible for many users. This talk presents UNICEF’s open-source approach to bridging that gap through a modular geospatial stack: GeoRepo, GeoSight, and a newly developed SDMX Connector. Together, these tools enable seamless integration of official statistics with harmonized administrative boundaries, making it easier to explore, visualize, and analyze subnational, multi-indicator time series data. We begin by outlining the key challenges of geo-enabling SDMX data: matching statistical indicators with the correct administrative boundaries, handling evolving geographies over time, transforming multidimensional datasets into GIS-ready formats, and managing time-series complexity. We argue that sharing reference area codes alone is insufficient - true interoperability requires access to consistent, versioned geometries. Next, we introduce GeoRepo, UNICEF’s open-source repository of global administrative boundaries, offering versioning, smart matching, and scalable APIs, and GeoSight, an open-source platform designed for visualizing statistical indicators alongside contextual geospatial layers. Building on this foundation, we showcase the SDMX Connector - developed in collaboration with Harvard Tech for Social Good - which allows users to directly browse, filter, preview, and import data from an SDMX registry into a geospatial environment. By lowering technical barriers, this ecosystem empowers analysts, policymakers, and developers to move from static tables to dynamic, map-based insights. It also lays the groundwork for GeoAI applications by making statistical data spatially explicit and machine-readable. false https://talks.osgeo.org/foss4g-2026/talk/SA7BNH/ Conference Management Room5 General session talk 2026-09-02T14:30:00+09:00 14:30 00:30 Accurate fiber maps often rely on proprietary operator data. This talk presents an evidence-based methodology to assess fiber presence and connectability using [Overturemaps](https://overturemaps.org/), network measurements from a Brazilian city with [SIMET](https://simet.nic.br/), and routing constraints—explicitly accounting for uncertainty. foss4g-2026-5223-mapping-fiber-without-operator-data-evidence-based-connectivity-indicators CRISTIANE HONORA MILLAN en Fiber optic infrastructure is critical for digital inclusion, yet detailed and reliable maps are rarely available to public authorities. Existing coverage maps often rely on proprietary operator disclosures or make strong assumptions that are difficult to verify. This creates challenges for transparent planning, prioritization, and accountability. This talk presents an alternative approach to mapping fiber infrastructure that deliberately avoids operator data and coverage claims. Instead, it focuses on what can be observed and reasonably inferred. The proposed methodology combines Open Source Data from [Overturemaps](https://overturemaps.org/) and network measurement information to construct evidence-based indicators of fiber presence, proximity, and potential connectability. A key input to this work is network measurement data collected through [SIMET](https://simet.nic.br/), an Internet performance measurement platform developed and operated by [CEPTRO.br](https://medicoes.nic.br)/[NIC.br](https://nic.br). [SIMET](https://simet.nic.br/) has been running continuously for several years and collects large-scale, geolocated measurements directly from end-user devices across Brazil. Unlike operator-reported datasets, these measurements reflect observed network behavior at the edge, providing an independent and empirically grounded view of connectivity conditions. In this project, [SIMET](https://simet.nic.br/) data is used to identify candidate fiber endpoints. Rather than attempting to reconstruct operator networks, the workflow models fiber infrastructure as a graph of observable segments and plausible routes. Connectivity is assessed in terms of routing difficulty, redundancy, and proximity to candidate locations, while uncertainty is treated as a first-class output rather than a residual error. The result is not a definitive coverage map, but a set of indicators that support comparative analysis and decision-making. The presentation introduces the conceptual framework, discusses the data sources and processing steps, and demonstrates the approach using pilot results from selected urban areas. Particular attention is paid to limitations, ethical considerations, and the risks of over-interpretation when mapping critical infrastructure. By focusing on transparent methods and reproducible workflows, this work aims to support public-sector planning, research, and civil society initiatives that require actionable insight without relying on proprietary data. The talk concludes with open questions and directions for collaboration within the FOSS4G community. false https://talks.osgeo.org/foss4g-2026/talk/SZ83SS/ Conference Management Room5 General session talk 2026-09-02T15:30:00+09:00 15:30 00:30 This talk shows how using ComfyUI and the Qwen Image model to build a "magic eraser" for high-res street imagery. We’ll dive into how to use AI-driven masks to scrub away the clutter while keeping the city’s geometry intact, plus some tips on scaling this up. foss4g-2026-4827-streetview-image-inpainting-with-image-edit-diffusion-models Aman Bagrecha en Street-level imagery captured from vehicle-mounted cameras often contains dynamic and undesirable elements such as vehicles, pedestrians, signboards, glare artifacts, or privacy-sensitive regions. These elements limit downstream applications including mapping, urban analysis, visualization, and dataset preparation for computer vision tasks. In this talk, I present a practical, end-to-end inpainting pipeline built using ComfyUI and the Qwen Image-edit diffusion model, and how to make it work for high-resolution street-view imagery. The workflow combines prompt-guided inpainting with mask-based control to selectively remove or modify objects while preserving structural and semantic consistency. I will walk through how segmentation masks are integrated into ComfyUI graphs, how Qwen Image handles context-aware reconstruction, I'll also talk about why other inpainting methods failed in this task, and only certain model succeed. Join to learn about how I used Image-edit model, segmentation model, and applied it to high res panoramic imagery false https://talks.osgeo.org/foss4g-2026/talk/78LBSE/ Conference Management Room5 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 Soil organic carbon (SOC) and total nitrogen were estimated using Sentinel-2 vegetation indices and machine learning in northeastern Thailand. After outlier removal, Random Forest achieved R² = 0.63 for SOC and R² = 0.39 for total N, with BSI and BAEI as dominant predictors. foss4g-2026-5174-estimation-of-soil-organic-carbon-and-total-nitrogen-in-thailand-s-rubber-plantations-using-multispectral-imagery-and-machine-learning-algorithms Pramet Kaewmesri en **Description (≈320 words)** This study investigates the potential of multispectral satellite imagery and machine learning techniques to estimate soil organic carbon (SOC) and total nitrogen (N) in rubber plantation soils in northeastern Thailand. Soil organic carbon and nitrogen are important indicators of soil fertility, nutrient cycling, and ecosystem productivity. In rubber plantation systems, maintaining adequate soil nutrient levels is essential for sustainable agricultural production and long-term soil health. Conventional soil sampling and laboratory analyses provide reliable measurements but are often costly, time-consuming, and limited in spatial coverage. Remote sensing approaches, particularly satellite-derived spectral indices combined with machine learning algorithms, provide an alternative method for large-scale soil property assessment and monitoring. Field-based soil measurements were integrated with spectral indices derived from Sentinel-2 multispectral imagery. Sentinel-2 offers high spatial resolution and multiple spectral bands suitable for vegetation and soil analysis. Several vegetation, moisture, and soil-related indices were calculated, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Leaf Area Index (LAI), Bare Soil Index (BSI), Soil Index (SI), Normalized Difference Built-up Index (NDBI), Urban Index (UI), Built-up Area Extraction Index (BAEI), Normalized Difference Red Edge Index (NDRE), Red-Edge Chlorophyll Index (CIre), and MERIS Terrestrial Chlorophyll Index (MTCI). These indices capture variations in vegetation condition, soil exposure, moisture dynamics, and surface reflectance characteristics that may influence soil nutrient variability. Data preprocessing involved removing missing values and detecting outliers to improve model reliability. After preprocessing, 79 samples were retained for SOC modelling and 95 samples for total nitrogen modelling. Random Forest regression was applied due to its ability to capture nonlinear relationships and interactions among predictor variables. The modelling results indicate that SOC estimation achieved moderate predictive performance with a coefficient of determination (R²) of 0.63, root mean square error (RMSE) of 0.198, and mean absolute error (MAE) of 0.166. Feature importance analysis showed that the Bare Soil Index (BSI) and Built-up Area Extraction Index (BAEI) were the most influential predictors, followed by the Normalized Difference Built-up Index (NDBI) and Urban Index (UI). For total nitrogen prediction, the model showed lower predictive performance (R² = 0.39, RMSE = 0.0107, MAE = 0.0088), with key predictors including soil organic matter, MERIS Terrestrial Chlorophyll Index (MTCI), Modified Normalized Difference Water Index (MNDWI), Green Normalized Difference Vegetation Index (GNDVI), and Soil Index (SI). The results demonstrate the potential of integrating Sentinel-2 multispectral data and machine learning techniques for soil property estimation. Future work should improve field sampling strategies and incorporate additional environmental variables to enhance model accuracy and support soil monitoring in rubber plantation systems. false https://talks.osgeo.org/foss4g-2026/talk/ATYYH3/ Conference Management Room5 General session talk 2026-09-02T16:30:00+09:00 16:30 00:30 A few collected thoughts about the impact of the current coding revolution for the future of FOSS4G projects, including a brief take on the economics of FOSS4G. foss4g-2026-5524-ai-accelerated-development-and-foss4g-a-perspective-into-the-future Daniel Araújo Miranda en In February 2026, Claude Code's creator Boris Cherny stated that "Coding is solved." Irrespective of opinion, what does the current coding revolution mean for the future of FOSS4G? These are a few collected thoughts I'd like to share, and maybe contribute to decision-making and to shape our future as a community. - FOSS4G vs building from scratch. - Which tools survive? - What changes for community projects health? - technical debt - backlog - maintainer responsiveness - tooling - bitrot at the speed of light (or not?) - Case study: pyCSW vs GeoNetwork / GeoServer vs building from scratch. - The bar is now *much* higher And, finally: - A brief take on the economics of FOSS4G **This abstract is entirely human-written using a simple text editor. No review, no prior or posterior opinion or any input from I.A., not even auto-correct** false https://talks.osgeo.org/foss4g-2026/talk/D98FC9/ Conference Management Room5 General session talk 2026-09-02T17:00:00+09:00 17:00 00:30 While LLMs excel at synthesizing text, they lack geospatial awareness and cannot reason over spatial networks. This talk demonstrates how Geo-GraphRAG and DGGS, grounded in open standards, enable geospatial reasoning with transparent, traceable results. foss4g-2026-5598-methods-for-introducing-geospatial-awareness-to-large-language-models-with-integrity-provenance-and-trust-through-open-standards-and-open-source /media/foss4g-2026/submissions/FQGS8T/Screenshot_2025-05-14_at_11.33.40AM_IUFwvNA.png Nathan McEachen en Although Large Language Models (LLMs) excel at natural language processing and general reasoning, they lack the cross-sectoral, domain-specific, and up-to-date geospatial knowledge needed for decision support in disaster and crisis response, public health, economic analysis, and security applications. National Spatial Data Infrastructures (NSDIs) contain a wealth of such data, but they are not published in interoperable formats suitable for use by LLMs. Incompatible representations of the same locations across systems make feature-level integration (i.e., common geographies) prohibitively labor-intensive, particularly in lower-resourced settings. This talk presents two complementary methods for introducing geospatial awareness into LLMs by shifting interoperability to the point of data publication rather than relying on point-to-point integrations. Discrete Global Grid Systems (DGGS)—a new Open Geospatial Consortium (OGC) standard—implement the concept of common geographies by quantizing data into hierarchical grid cells identified by standardized zone IDs. When datasets are published using the same DGGS reference system, they become immediately interoperable. LLM agents can translate natural language questions into DGGS API calls with CQL2 parameters, enabling queries such as identifying areas where flood levels exceed specified thresholds. Spatial Knowledge Graphs (SKGs) provide a complementary capability by representing networks of interconnected geographic features and their semantic relationships across domains. Through Geo-GraphRAG, LLM agents can translate natural language questions into GeoSPARQL queries, enabling reasoning over feature networks such as infrastructure, populations, and services. When combined with DGGS, these approaches enable integrated analysis linking semantic networks with aggregated and statistical data, while maintaining transparency and traceability in model outputs. However, no standard currently exists for publishing SKGs with interoperability by common geography. This makes maintaining referential integrity across evolving datasets complex and resource-intensive. Interoperable Spatial Knowledge Graphs (iSKGs) address this challenge by enabling geo-objects to be aligned at publication, allowing graphs from different organizations to be integrated on demand in a federated architecture. By incorporating metadata standards such as DCAT and leveraging OGC Building Blocks, provenance, traceability, and shared geo-ontologies can be established to support trusted, reusable knowledge. This talk will demonstrate how these capabilities can be implemented using open-source software. The work builds on collaboration with the OGC community through disaster and resilience research pilots, as well as contributions developed in partnership with the U.S. Army Corps of Engineers Civil Works Division. By shifting the paradigm to interoperability at the point of publication, this approach creates a force multiplier—enabling local talent to build and sustain LLM-based geospatial applications by providing access to integrated, trustworthy geospatial data that has historically been out of reach. false https://talks.osgeo.org/foss4g-2026/talk/FQGS8T/ Conference Management Room5 General session talk 2026-09-02T17:30:00+09:00 17:30 00:30 The future of geospatial analysis lies in combining the scalability of cloud AI services with the flexibility of open source tools. This session showcases architectural patterns for leveraging the best of both worlds, alongside case studies demonstrating how organizations use this combination to achieve their goals. foss4g-2026-5224-bridging-cloud-and-community-aws-ai-services-meet-open-source-geospatial-tools Guyu YeIvan Cui en Discover how to unlock the full potential of geospatial analysis by combining AWS AI services with FOSS4G tools. This session showcases AWS AI services and practical architectural patterns for building scalable, flexible, and agentic workflows that leverage cloud computing power alongside open source innovation, with case studies demonstrating how organizations use this combination to achieve their goals. false https://talks.osgeo.org/foss4g-2026/talk/JQXC9L/ Conference Management Room6 General session talk 2026-09-02T11:00:00+09:00 11:00 00:30 This study develops an R package "spCF" that provides functions for coarse-to-fine spatial modeling, enabling scalable spatial prediction, regression, and multi-scale analysis for large samples. foss4g-2026-5555-spcf-an-r-package-for-coarse-to-fine-spatial-modeling Daisuke Murakami en Coarse-to-fine spatial modeling (CFSM) is a recently developed framework for spatial statistical modeling that offers scalability in both computational efficiency and predictive accuracy for large samples, and is implemented in the spCF package. Currently, this package supports relatively simple spatial models for continuous, count, and binary data. Ongoing developments aim to extend its capabilities to spatiotemporal modeling, spatially varying coefficients, downscaling, and other advanced tasks. In this talk, after explaining the latest development status, we will demonstrate its use through several empirical applications. false https://talks.osgeo.org/foss4g-2026/talk/JXRUK3/ Conference Management Room6 General session talk 2026-09-02T11:30:00+09:00 11:30 00:30 STAC enables structured geospatial search, but GeoAI introduces semantic search with vector embeddings. This talk shows how to combine both, using STAC for discovery and embeddings for similarity, to support modern geospatial analysis workflows. foss4g-2026-5605-beyond-metadata-search-stac-vector-embeddings-and-geoai Matthew Hanson en STAC has become the standard for discovering and accessing geospatial data, providing robust search capabilities based on spatial, temporal, and metadata filtering. However, as GeoAI workflows mature, there is growing demand for new forms of search that go beyond structured queries, such as similarity-based and feature-level search. This talk explores how vector embeddings can extend, rather than replace, STAC-based search. In emerging architectures, STAC continues to serve as the foundation for data discovery and access, while embedding-based systems enable semantic search over image content, derived features, and learned representations. We’ll examine patterns for combining these approaches, including workflows that use STAC to identify candidate data and vector indexes to support similarity search and analysis. We’ll also introduce the emerging STAC Embeddings extension, which provides a standardized way to describe and reference embedding data within the STAC ecosystem. Finally, we’ll discuss tradeoffs and design considerations: where STAC’s model is sufficient, where embedding-based approaches add value, and how to build systems that integrate both effectively. This session provides a practical view of how structured and semantic search can be combined to support modern GeoAI applications. false https://talks.osgeo.org/foss4g-2026/talk/KHBUHX/ Conference Management Room6 General session talk 2026-09-02T13:00:00+09:00 13:00 00:30 This presentation introduces a National Map Agent that integrates open geospatial standards, knowledge graphs, and GraphRAG to enable intelligent, standards-aware mapping workflows. Built on open-source tools, the system transforms authoritative mapping specifications into machine-readable knowledge, supporting automated feature modeling, validation, and collaboration between mapping agencies. foss4g-2026-5575-national-map-agent-an-open-geospatial-architecture-for-knowledge-graph-driven-geoai /media/foss4g-2026/submissions/LZJFAW/National_Map_Agent_FOSS4G_e1osNBV.png Dongpo Deng en National mapping agencies face increasing pressure to manage complex geospatial data ecosystems while ensuring interoperability, data sovereignty, and timely updates. At the same time, open-source geospatial technologies have matured into a robust foundation for scalable, standards-compliant infrastructure. This talk introduces a new architectural paradigm—the National Map Agent—which integrates open geospatial stacks with knowledge graphs and Graph Retrieval-Augmented Generation (GraphRAG) to enable intelligent and explainable mapping workflows. The core idea is to transform authoritative mapping specifications—such as feature models, classification schemas, and survey regulations—into a machine-readable knowledge graph aligned with OGC and ISO 191xx standards. This semantic layer encodes domain knowledge that is traditionally locked in documents, allowing it to be queried, reasoned over, and reused across systems. By structuring national mapping rules as a knowledge graph, we create a foundation for automation and interoperability that remains fully transparent and auditable. On top of this foundation, we implement a GraphRAG pipeline that combines graph queries (e.g., Cypher or SPARQL) with large language models to support context-aware reasoning. This enables AI agents to assist in tasks such as feature classification, schema alignment, and attribute validation, while maintaining consistency with official standards. Unlike purely text-based AI approaches, this method grounds reasoning in structured geospatial knowledge, improving both accuracy and explainability. The system is built almost entirely on open-source components, including PostGIS, GDAL, GeoPandas, PMTiles, MapLibre, and Neo4j. This ensures extensibility and alignment with the FOSS4G ecosystem. A prototype implementation using Taiwan’s national mapping datasets demonstrates how the National Map Agent can bridge authoritative data and collaborative platforms such as OpenStreetMap, enabling more efficient and consistent mapping workflows. This work highlights a shift from traditional GIS systems toward intelligent, knowledge-driven geospatial infrastructures. It demonstrates how open standards and open-source tools can serve as the backbone for next-generation GeoAI systems, while preserving data sovereignty and interoperability. The National Map Agent provides a practical and scalable approach for modernizing national mapping workflows and advancing the role of open geospatial technologies in the era of AI. false https://talks.osgeo.org/foss4g-2026/talk/LZJFAW/ Conference Management Room6 General session talk 2026-09-02T13:30:00+09:00 13:30 00:30 From GeoNetwork semantic search to agentic GIS automation and the French National Digital Twin, Camptocamp shares two years of hands-on open source GeoLLM experimentation — what works, what doesn't, and what the OSGeo community should build next. foss4g-2026-5631-geollm-in-the-wild-open-source-ai-meets-geospatial Florent Gravin en Large Language Models are reshaping how we interact with data — but most implementations ignore geography entirely. At Camptocamp, we've spent the last two years embedding LLMs deep into open source geospatial workflows, and this talk is a frank account of what works, what doesn't, and where the field is heading. ** GeoNetwork as a GeoAI laboratory GeoNetwork, the OSGeo flagship metadata catalog, is where much of our work has been grounded. We'll walk through the integration of semantic search — moving beyond keyword matching to meaning-based retrieval powered by embedding models — and the development of a conversational assistant that lets users query geographic datasets in plain language. We'll also share our ongoing work on exposing GeoNetwork capabilities through the Model Context Protocol (MCP), enabling LLM agents to interact directly with catalog APIs. *** Agentic geospatial: bleeding edge techniques Beyond search and chat, we'll dive into what agentic AI looks like when applied to geospatial workflows: function calling to orchestrate GIS operations (buffer, intersection, spatial queries against OpenStreetMap), LLM-driven QGIS automation via MCP, and the architectural patterns — RAG pipelines, intent extraction, hybrid search — that make these systems reliable enough to put in front of real users. ** The French National Digital Twin: an open source GeoAI at scale We'll close with our role leading the LLM workstream of the French National Digital Twin project (France 2030), a consortium bringing together IGN, INRIA, Cerema and others. This initiative is tackling GeoAI at territorial scale — and doing it entirely in the open. We'll share early architectural decisions, the challenges of grounding LLMs in authoritative geographic knowledge bases, and why open source is not just a preference here but a sovereignty requirement. ** Key takeaways for the FOSS4G community Attendees will leave with a clear picture of the current state of open source GeoLLM tooling, practical patterns for integrating LLMs into OSGeo-stack applications, and an honest assessment of the remaining challenges — from data quality to model size optimization — that the community needs to solve together. false https://talks.osgeo.org/foss4g-2026/talk/LZKUU8/ Conference Management Room6 General session talk 2026-09-02T14:00:00+09:00 14:00 00:30 GeoAI embeddings span models, representations, and systems, but are often conflated. This talk introduces a clear framework and compares approaches like Clay and TESSERA to help practitioners understand and apply geospatial embeddings in real-world workflows. foss4g-2026-5611-open-geo-embeddings-models-representations-and-systems Matthew Hanson en Vector embeddings are becoming a key building block in GeoAI, enabling new forms of search, analysis, and data integration. But the landscape of geospatial embeddings is still emerging and often conflated, spanning foundation models, derived representations, and the systems used to manage them. This talk provides a practical introduction to geospatial embeddings, focusing on how the pieces fit together. We will outline a simple framework that distinguishes between foundation models such as Clay, pre-computed embedding representations such as the TESSERA temporal grid, and the systems used to store and query these vectors at scale. We will compare how these approaches differ in terms of inputs such as imagery, time series, and multimodal data, as well as training strategies and intended use cases including similarity search, classification, and change detection. We will also examine the role of general-purpose vision models and how they are being adapted for geospatial workflows. Finally, we will discuss the current state of openness in this space, including what is truly open-source, what is partially accessible, and where gaps remain. This session offers a structured view of the geo-embedding ecosystem, helping practitioners understand what is available today and how to evaluate and combine these approaches in real-world applications. false https://talks.osgeo.org/foss4g-2026/talk/MF97D9/ Conference Management Room6 General session talk 2026-09-02T14:30:00+09:00 14:30 00:30 This study proposes a Conversational GIS system that integrates LLM with MCP, allowing non-technical users to access and analyze geospatial data through natural language, supporting flood risk analysis and lowering technical barriers for GIS interaction. foss4g-2026-5384-bridging-llm-and-gis-via-model-context-protocol-for-conversational-flood-data-analysis Narathorn Noophum en Currently, geographic information systems (GIS) are commonly developed and provided through Application Programming Interfaces (APIs). This approach allows different systems to access and exchange spatial data in a flexible way. However, using APIs usually requires knowledge of programming, API requests, and an understanding of spatial data structures and geospatial analysis processes. As a result, accessing and using GIS data is still difficult for general users, data analysts, or policy makers who do not have a background in software development. This study proposes an approach to integrate Large Language Models (LLMs) with GIS using the Model Context Protocol (MCP) architecture to create a conversational interface for accessing, searching, and analyzing geospatial data. The goal is to change the traditional way of interacting with GIS from direct API calls to communication through natural language. The proposed system architecture consists of three main layers. 1. Conversational Layer – This layer uses an LLM to receive and interpret user questions written in natural language and understand the context of geospatial-related queries. 2. MCP Integration Layer – This layer acts as an intermediary that manages context and converts user questions into commands that can call geospatial tools or services. It uses the MCP communication structure to connect the LLM with external services in a systematic way. 3. Geospatial Processing Layer – This layer includes APIs and geospatial processing modules for spatial data retrieval, spatial statistics calculation, analysis, and flood risk assessment. In this study, several geospatial datasets related to flood analysis are integrated into the system. These datasets include Digital Elevation Model (DEM) data for terrain analysis, rainfall data, soil data, and historical flood data for flood risk assessment and identifying potential flood areas. In addition, population and household data are used to evaluate the potential impact on communities and infrastructure. Other supporting spatial datasets such as administrative boundaries, road networks, schools, and hospitals are also included to provide contextual analysis of flood-prone areas. With this architecture, users can ask questions using natural language, such as identifying flood-risk areas, retrieving population or infrastructure information, specifying geographic coordinates, or obtaining statistical data for a specific area. The system interprets the question, analyzes its context, and converts it into appropriate geospatial processing tasks. The results are then returned as structured data such as GeoJSON or statistical information, which can be further used in GIS systems or map visualization platforms. The proposed approach reduces the complexity of accessing geospatial data and lowers the technical barriers for users. It also improves interaction with GIS analysis systems through natural language. Furthermore, this work demonstrates the potential of integrating artificial intelligence with geospatial infrastructure to support the concept of Conversational GIS, which transforms traditional API-based geospatial services into interactive systems that allow users to communicate with spatial data more naturally. This approach may represent an important direction for future geospatial platforms. false https://talks.osgeo.org/foss4g-2026/talk/P8JGA8/ Conference Management Room6 General session talk 2026-09-02T15:30:00+09:00 15:30 00:30 Open Schools Kenya has put all schools on the map using OpenStreetMap. foss4g-2026-5412-open-schools-kenya /media/foss4g-2026/submissions/FZDL3U/1000193263_UKngmwt.jpg Joshua Ogure en The idea to map all the schools sounded crazy until Map Kibera together with Kibera youth went around mapping each and every school using ODK/ Kobo Collect. Starting with Kibera slum we went ahead to map three other informal settlement of Nairobi. Produced maps and developed a website for schools where each and every school has a profile page.(https://openschoolskenya.org/) The page showed all the details about that particular school ranging from population to contact information to school fees among others. Parents make informed choices of which schools to send their kids to depending on preferences and capabilities. The project was basically meant to make education information easily available, accessible and useful to everyone. Making the invisible schools visible. false https://talks.osgeo.org/foss4g-2026/talk/FZDL3U/ Conference Management Room6 General session talk 2026-09-02T16:00:00+09:00 16:00 00:30 Geochicas turns 10 this year. For five of those years, we have built the Atlas 8M: a community-governed, open-source archive of 3,183 georeferenced feminist mobilizations across 63 countries. This talk presents the project's technical evolution and current work toward automated data collection and open visualization infrastructure. foss4g-2026-5443-building-a-spatial-archive-of-feminist-activism Selene Yang Rappaccioli en Since 2019, the collective has coordinated the systematic cartographic documentation of feminist mobilizations on and around March 8. The Atlas 8M is now a transregional, multilingual, community-built archive documenting 3,183 georeferenced actions across 63 countries and 10 thematic categories, with all datasets maintained openly on GitHub. Five years of community mapping have also generated five years of methodological questions. How do you document mobilizations at global scale without centralizing control? How do you sustain contributor participation across languages and time zones? How do you make the data legible and useful beyond the immediate collective? Geochicas is currently iterating on two open tools to address these questions. 8m-global-mapper is a Python pipeline for automated collection of 8M actions from public web sources, using multilingual keyword configuration, geocoding, and export to uMap-compatible CSV. atlas-8m-dashboard is an interactive Plotly dashboard deployed via GitHub Pages that visualizes the full 2019–2025 dataset with filters by year, country, and theme. Both are active works in progress and open to community contribution. This talk reflects on what five years of feminist open mapping look like in practice, the infrastructure built, the decisions made, and the questions still open. Attendees are invited to contribute data, code, translations, or local knowledge, and to help build the infrastructure that makes feminist spatial memory visible and persistent. false https://talks.osgeo.org/foss4g-2026/talk/PJEY98/ Himawari General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 A practical look at how the _Pacific Community_ is shifting from short‑term geospatial projects to __sustainable__, open data products through __shared standards__, regional __governance__, and coordinated __stewardship__. foss4g-2026-5639-from-projects-to-products-operating-open-geospatial-data-as-a-regional-public-good-in-the-pacific /media/foss4g-2026/submissions/3C8AZ7/logo_title_left_PnsqZD6.png Thomas Tilak en Across the Pacific, geospatial data is often produced through short‑term projects, managed in silos, and delivered via platforms whose lifespan ends with funding. This creates duplication, inconsistent metadata, limited re‑use, and fragile services. This presentation shares lessons from the Pacific Community (SPC) on shifting from project outputs to durable geospatial data products by operating open geospatial infrastructure as a regional public good. Drawing on experience from the Pacific Data Hub, the talk explores how shared metadata standards, coordinated stewardship, and regional governance can improve discoverability, interoperability, and long‑term sustainability of geospatial data across sectors. The session is delivered as a joint narrative: SPC frames the regional operating and governance challenges, while a co‑presenter from a member country or SPC programme grounds the discussion in real implementation experience. Participants will gain practical insights into reducing portal proliferation, improving metadata quality at scale, and designing open geospatial services that remain usable beyond project cycles - particularly in small‑island and resource‑constrained contexts. false https://talks.osgeo.org/foss4g-2026/talk/3C8AZ7/ Himawari General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 Step into the world of mapping superheroes! The Global Open Mapping Guru Network empowers digital volunteers to mentor, validate, and lead open mapping initiatives worldwide. Learn how this global movement combines skills, collaboration, and impact, leveling up the future of open mapping while driving real-world change in communities everywhere. foss4g-2026-5597-global-open-mapping-gurus-leveling-up-the-future-of-open-mapping /media/foss4g-2026/submissions/8RSVEW/561337036_1113567110904306_2077931224417663083_n_WEklkKf.jpg Mikko Tamura en The Global Open Mapping Guru Network is a dynamic, global community of highly skilled volunteers—our “mapping superheroes”—who are leveling up the future of open mapping. This session explores how these Gurus harness open mapping tools to mentor peers, validate data, and lead initiatives that empower communities and support humanitarian action. Participants will discover how the Network transforms digital volunteerism into tangible local and global impact. Gurus contribute to projects such as mapping flood-prone areas, identifying health facilities, and documenting community infrastructure. Through storytelling, visuals, and real-world examples, this session demonstrates how collaborative mapping can fill critical data gaps, inform decision-making, and strengthen communities. The session also highlights mentorship and capacity-building within the Network. Gurus guide newcomers, share knowledge across borders, and foster inclusive, open mapping communities. Attendees will see how volunteers evolve from learners to leaders, making a difference in both their local communities and globally connected projects. This talk invites participants to become part of a worldwide movement. Whether you’re a mapper, developer, or humanitarian enthusiast, you’ll gain insights into how open mapping can be gamified, scaled, and applied for real-world impact. The session concludes with actionable steps for getting involved: joining the Network, contributing to mapping initiatives, or mentoring the next generation of open mapping heroes. Attendees will leave inspired, seeing how global collaboration can empower individuals, transform communities, and level up the future of open mapping. false https://talks.osgeo.org/foss4g-2026/talk/8RSVEW/ Himawari General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 This talk presents lessons from QGIS workshops, highlighting hands-on learning, open data and adaptive teaching to build user skills from beginners to advanced practitioners. foss4g-2026-5630-from-zero-to-mapping-mojo-teaching-qgis-through-hands-on-workshops Tomaž Šturm en This presentation explores practical lessons learned from designing and delivering QGIS workshops in Slovenia with a focus on creating effective, engaging and outcome-oriented learning experiences. These workshops have been developed for a wide range of participants, from complete beginners to advanced and professional users, requiring a flexible and adaptive teaching approach. A key principle of the workshops is a clear and straightforward learning path, where participants quickly achieve tangible results—most often by creating their own maps. This immediate sense of accomplishment helps build confidence and motivates further learning. At the same time each workshop is carefully tailored to the specific needs of client organizations, ranging from basic QGIS skills to more advanced workflows involving spatial databases such as PostgreSQL in combinatin with PostGIS and the use of mobile applications Mergin Maps and QField for field data collection. The workshops are structured into three levels—beginner, advanced, and professional—allowing participants to progress from their current level of competence toward more independent and efficient use of geospatial tools. Special attention is given to beginners as they represent an opportunity to introduce open-source geospatial technologies (FOSS4G) from the ground up and establish good practices early on. Interactivity is a central component of the learning process. Participants are encouraged to ask questions at any point, creating a more dynamic and responsive learning environment. Practical exercises and tasks are used not only to reinforce knowledge but also to assess understanding and encourage participants to approach spatial problems from different perspectives. An important aspect of this work is the continuous evolution of workshop content. As QGIS is actively developed and new Slovenian open datasets become available, training materials must be regularly updated and expanded. Open data plays a particularly important role in the workshops, as it allows participants to immediately apply their skills to real-world datasets, increasing both relevance and engagement. The role of the instructor is not only to deliver content but also to remain flexible and transparent. Acknowledging when an answer is not immediately available—and committing to follow up—helps build trust and demonstrates a realistic approach to problem-solving in a rapidly evolving technical field. The presentation will highlight how combining open-source tools, open data and adaptive teaching methods can significantly enhance user competence and confidence. It will also reflect on how workshops can provide participants with new perspectives on spatial analysis and support the integration of geospatial thinking into their everyday workflows. false https://talks.osgeo.org/foss4g-2026/talk/BNZVJR/ Himawari General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 This session shares the 16-year evolution of FOSS4G Hokkaido, Japan’s first local community. Moving beyond a simple success story, we offer a practical "Survival Toolkit" to overcome organizer burnout and organizational challenges, providing actionable insights to help communities foster Geospatial Sovereignty through long-term resilience. foss4g-2026-5549-from-6-people-classroom-meetup-to-100-people-regional-conference-16-years-building-an-open-foss4g-community-in-hokkaido Yasuto FURUKAWA en The term "FOSS4G" was born in 2006. In Japan, after initial events in major cities like Tokyo and Osaka, FOSS4G Hokkaido was established in 2010 as the first local community. Our adventure began with the curiosity of just six people experimenting with "QGIS 1.4 Enceladus" in a classroom at Hokkaido University. What started as a tiny gathering has flourished into one of Japan’s most active regional conferences, attracting up to 100 participants and maintaining a continuous presence for over 16 years. Our grassroots efforts went beyond meetups. We became a blueprint for other regional FOSS4G communities across Japan and helped catalyze a broader open innovation ecosystem. Along the way, we promoted OSS and Open Data and even helped inspire commercial entities like MIERUNE. These relationships have become "resilient soil" for Geospatial Sovereignty, helping regional societies keep freedom of choice in their digital infrastructure. However, this journey has faced significant structural challenges. Reflecting the sustainability crises in the global OSS ecosystem, we have navigated issues of organizer burnout, the concentration of responsibility, and the dramatic shift in engagement caused by the pandemic. In this session, we will share a "Survival Toolkit" for community sustainability, shaped by these real-world struggles. We will cover practical strategies to keep a healthy balance, including: welcoming new generations through inclusive mechanisms; adopting a Code of Conduct (CoC) early (when it was still rare in Japan) to ensure a safe environment; and clarifying the value of local, offline interaction in a digital-first world. false https://talks.osgeo.org/foss4g-2026/talk/FCT3TC/ Dahlia1 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 This presentation reports the renewable energy zoning project conducted in Urahoro, Hokkaido. By combining QGIS with various open data sources and utilizing a custom-built participatory mapping system developed with generative AI, we visualized locations valued by residents to achieve an appropriate balance between conservation and renewable energy development. foss4g-2026-4952-participatory-renewable-energy-zoning-using-qgis-and-open-data-a-case-study-in-urahoro-hokkaido /media/foss4g-2026/submissions/NEVSDP/Participatory_survery_system_nfa0VHV.png Shota Furuya en Urahoro Town, Hokkaido, aims to become a zero-carbon city by 2050 and emphasizes the introduction of renewable energy that coexists with the local community. To advance this goal, the town utilized the Ministry of the Environment's renewable energy zoning project in fiscal year 2025 to formulate its own Renewable Energy Zoning Plan. In developing this plan, the Institute for Sustainable Energy Policies (ISEP) handled map creation, expert meetings, stakeholder interviews, and seminars/workshops for residents. In creating the renewable energy zoning maps, a total of 37 layer maps across 6 categories were produced. By overlaying these, the town established Conservation, Adjustment, and Promotion areas for both solar power (buildings/land) and wind power. Ultimately, they succeeded in creating a zoning map that secures the renewable energy potential necessary to achieve a zero-carbon city while preserving the town's natural environment and culturally significant spots. The maps were created using QGIS. Starting with almost zero GIS experience, the presenter learned how to use the software step-by-step from AI, utilizing spatial operations and field calculations to complete the project. Initially, the presenter did not even know how to set the CRS (Coordinate Reference System) and learned the basics in a short period by persistently asking ChatGPT questions and occasionally referring to explanatory articles on QGIS LAB. Most of the layer maps were created using data publicly available from the Ministry of Land, Infrastructure, Transport and Tourism’s National Land Numerical Information download site. A unique feature of Urahoro's zoning is that it visualizes locations that residents personally value or consider important to the community, collected through participatory surveys. While the basic layer information required for zoning consists of quantitative data that allows for objective derivation of site conditions, carefully capturing the memories and meanings those places hold for residents is essential for introducing renewable energy that coexists with the community. Using the AI web app builder Bolt, the presenter built a custom participatory survey system for submitting locations and called on residents to post their important places via government public relations channels and workshops. In the end, 37 people participated, submitting a total of 112 locations, which visualized the places people cherish in Urahoro. (104 public entries can be viewed online at https://urahoro-mapping-card.bolt.host/). The zoning plan requires renewable energy developers to respect these sites valued by the local community when considering locations. Urahoro's renewable energy zoning plan is expected to play a vital role in introducing renewable energy that exists in harmony with the region. The administration has stipulated that the zoning maps will be reviewed approximately every three years. To ensure the municipality can add or modify map data itself, the presenter plans to transfer QGIS skills to the staff in charge. As the methodology for renewable energy zoning using open-source GIS and open data has been largely established, there are plans to expand this approach to other interested municipalities in the future. false https://talks.osgeo.org/foss4g-2026/talk/NEVSDP/ Dahlia1 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 The global push for Net Zero has turned our eyes toward the horizon—specifically, the vast, untapped energy of our oceans. This session explores using FOSS4G in building a comprehensive ocean renewable energy platform. foss4g-2026-5593-developing-an-ocean-renewable-energy-platform-with-foss4g Luis Caezar Ian PanganibanCharmyne Mamador en The global transition toward sustainable energy is no longer a secondary objective; it is a technical and environmental necessity. As we look to the horizon, the untapped potential of the "Blue Economy" offers a significant opportunity for renewable energy generation. However, the development of offshore energy platforms is often restricted by the high costs and opaque methodologies of proprietary geospatial tools. This session explores a shift toward transparency and collaboration by showcasing the development of an ocean renewable energy platform built entirely within an open geospatial ecosystem. Foundational Analysis and Spatial Modeling The process begins with the rigorous analysis of the marine environment. We examine how open-source frameworks allow for the seamless integration of various factors from resource, physical, economic and environmental perspectives. Identifying suitable sites for energy requires a deep understanding of these factors. By utilizing community-driven geospatial tools, developers can ensure that their site selection processes are reproducible, verifiable, and free from the constraints of "black box" algorithms. Advanced Analytics and Decision Support Beyond simple mapping, the platform leverages sophisticated spatial informatics to handle the heavy lifting of energy potential calculations. For instance, determining the viability of a specific marine location involves processing multi-dimensional environmental data. By applying standardized physical formulas—such as those used to calculate resource potential—within an automated analytical workflow, we can transform raw oceanic data into actionable insights. Visualization and Global Democratization The final stage of the platform focuses on accessibility. Through the deployment of interactive, browser-based visualization layers, complex spatial models are converted into intuitive dashboards. This accessibility is vital for stakeholders, environmental agencies, and policymakers who require real-time monitoring and clear evidence of project viability. Ultimately, this session demonstrates that choosing open geospatial frameworks is more than a cost-saving measure. It is a commitment to the democratization of technology, empowering organizations of all sizes to contribute to a sustainable future without being tethered to restrictive software licensing. false https://talks.osgeo.org/foss4g-2026/talk/3E7SKZ/ Dahlia1 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 In this session, we will discuss cloud-native drone processing pipeline using OpenDroneMap (ODM). Learn how to reliably transform standard 2D drone imagery into high-resolution Ortho mosaics, 3D point clouds, and elevation models, balancing automated quality assurance with manual cloud cost controls. foss4g-2026-5623-scaling-drone-imagery-processing-a-cloud-native-approach-with-opendronemap-and-cloud /media/foss4g-2026/submissions/BZFFUR/3d_point_cloud_ZVPmbqe.gif Santhosh M en Managing massive drone datasets from the field to final geospatial products requires robust orchestration. To solve this, our team developed a custom, cost-efficient cloud processing pipeline. We will explain exactly how this architecture worked for us, showing how standard 2D drone imagery is transformed into high resolution ortho mosaic (basemap) rich, measurable 3D outputs and elevation models using open-source tools. We will discuss the photogrammetry pipeline, focusing on practical implementation with OpenDroneMap (ODM). Structure from Motion (SfM): Understanding the core concept how algorithms calculate camera positions and match features in overlapping JPEGs to extract dense 3D point clouds. OpenDroneMap & Docker: A quick overview of what ODM is, how to deploy containerized Docker ODM for reproducible environments, and a guide to the most important processing flags. Elevation & Canopy Modeling: Moving beyond flat Ortho mosaics to generate Digital Surface Models (DSM), bare-earth Digital Elevation Models (DEM), and Canopy Height Models (CHM) for advanced vegetation analysis. Hardware & Cloud Compute: Photogrammetry requires significant computing power. We will explain how AWS services helped in our implementation. In this session for any geospatial professionals predominantly working with the macroscopic scale of satellite imagery, this session will offer an practical introduction to the ultra-high-resolution capabilities of drone data processing. false https://talks.osgeo.org/foss4g-2026/talk/BZFFUR/ Dahlia1 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 The NISAR mission launched in July 2025, and data is now publicly available. This talk presents a range of open source tools available for accessing and transforming L-band data from the mission. foss4g-2026-5163-open-source-access-options-for-nasa-isro-synthetic-aperture-radar-nisar-mission-data Heidi KristensonAlex Lewandowski en The NASA-ISRO Synthetic Aperture Radar (NISAR) mission launched in July 2025, and data is now freely available to the public. SAR sensors use active microwave signals that penetrate through clouds and smoke, allowing imagery of Earth’s surface to be acquired on every pass. Regular imaging cycles, combined with the ability to quantify surface deformation on the centimeter scale, make NISAR a powerful tool for monitoring landscape processes. NISAR carries sensors for both L-band (24 cm) and S-band (12 cm) wavelengths. The L-band dataset is hosted by NASA, while the Indian Space Research Organisation (ISRO) hosts the S-band data. We will focus on the L-band data products, which are collected globally with a 12-day repeat cycle. NISAR data can be searched for and accessed using map-based web interfaces as well as programmatic approaches. Minimally processed data products are available, but there are also a number of analysis-ready products generated by the mission, making SAR data more accessible to the broader geospatial community. NISAR data files are archived in cloud storage using an HDF5 file format that has been optimized to support cloud computing workflows. Not only is this file format less familiar to many users, the footprint of each NISAR data acquisition is very large. As such, it is important to be aware of approaches for efficiently accessing, subsetting, and loading the data. The Alaska Satellite Facility has published the [NISAR Data User Guide](https://nisar-docs.asf.alaska.edu/) to help users find all the information they need to know about accessing and working with NISAR data in one place. We will present a brief introduction to SAR remote sensing and NISAR datasets, focusing on the most accessible product types. We will describe the resources available for accessing and loading NISAR L-band data, including methods that leverage cloud computing; direct users to guidance for working with the data in QGIS; and highlight open-source software (isce3, xarray, rioxarray, s3fs, h5py, gdal, asf_search, earthaccess, etc.) available for use in programmatic workflows. false https://talks.osgeo.org/foss4g-2026/talk/FGVKPS/ Dahlia2 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 This presentation describes a cloud-based approach to managing large-scale maritime data, enabling near real-time visualization alongside historical analysis. It highlights challenges in handling high data volumes, ensuring performance, and controlling access, while supporting efficient and scalable geospatial data publication. foss4g-2026-5474-operating-maritime-ais-at-enterprise-scale-with-geoserver Andrea AimeNuno Oliveira en The volume of data to be processed and published continues to grow rapidly, particularly in domains such as maritime monitoring, where continuous streams of AIS data must be ingested, processed, and visualized. At the same time, the infrastructure, technologies, and methodologies required to manage these data streams are steadily advancing and maturing. GeoServer, an open-source web service for publishing geospatial data, supports industry standards for vector, raster, and map delivery, and is widely used by organizations to disseminate geospatial information at scale. In this work, we integrated GeoServer with established big data technologies, including Apache Kafka and Databricks, deploying the solution on Microsoft Azure. The resulting architecture is designed to support demanding maritime use cases, enabling near real-time visualization of incoming AIS data while also supporting large-scale batch processing and analysis of historical datasets. This presentation describes the system architecture and the key challenges addressed by GeoSolutions in publishing high-volume, high-velocity data through GeoServer’s OGC services (WMS, WFS, and WPS). Particular attention is given to achieving an effective balance between data ingestion throughput and visualization performance. The solution integrates with a streaming processing platform responsible for ingesting, transforming, and storing data in an Azure Data Lake, allowing GeoServer to efficiently query the most recent features while enforcing complex authorization policies. To meet these requirements, several custom GeoServer extensions were developed, addressing advanced authorization scenarios, specialized styling needs for maritime data, and seamless integration with big data platforms. false https://talks.osgeo.org/foss4g-2026/talk/9VBKGE/ Dahlia2 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 GeoSolutions has been involved in several projects, from local administrations to global institutions, involving GeoNode deployments, customizations and enhancements. A gallery of projects and use cases will showcase GeoNode's versatility and effectiveness, both as a standalone application and service component, for building secured geodata catalogs and web mapping services. foss4g-2026-5467-geonode-use-cases-custom-applications Stefano Bovio en GeoNode is a Web Spatial Content Management System based entirely on Open Source tools whose purpose is to promote the sharing of data and their management in a simple environment where even non-expert users of GIS technologies can view, edit, manage, and share spatial data, maps, prints and documents attached. Using an open source stack based on mature and robust frameworks and software like Django, MapStore, PostGIS, GeoServer and pycsw, an organization can build on top of GeoNode its own SDI or geospatial portal. GeoNode provides a large number of user-friendly capabilities, broad interoperability using Open Geospatial Consortium (OGC) standards, and a powerful authentication/authorization mechanism. Over the years, GeoSolutions has been involved in a number of projects, ranging from local administrations to global institutions, involving GeoNode deployments, customizations and enhancements. A gallery of projects and use cases will showcase the versatility and effectiveness of GeoNode, both as a standalone application and as a service component, for building secured geodata catalogs and web mapping services, dashboards and geostories. In particular the recent advancements in data ingestion and harvesting workflows will be presented, along with the many ways to expose its secured services to third party clients. Examples of GeoNode’s builtin capabilities for extending and customizing its frontend application will be showcased. false https://talks.osgeo.org/foss4g-2026/talk/DSBBHU/ Dahlia2 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 This presentation covers our experience with the migration of **GEODES** (the Earth observation catalog of the French space agency, CNES) from proprietary systems to an Open Source, cloud-optimized architecture, highlighting the strategic 'how' and 'why' behind the shift. foss4g-2026-5140-modernizing-earth-observation-access-the-geodes-shift-to-open-source-and-cloud-optimized-architectures Hugo FournierBenjamin HUSSON en The French space agency (**CNES**) has developed **GEODES**, an innovative Earth Observation catalogue that leverages open-source technologies in order to simplify access to geospatial data and offer advanced services to end-users. GEODES offers a wide range of open EO data and advanced products originating from CNES missions and from ESA'S collaborative ground segment initiative, as well as fully integrated processing chains providing on-demand products generation. As a free service dedicated to the global community, including researchers, developers, policymakers and enthusiasts alike, GEODES covers various non-EO-specific activities. The GEODES portal (geodes-portal.cnes.fr) has launched in November 2024, providing access to a wide range of EO data and advanced products from satellites such as Sentinel, Landsat, Venus and SPOT. At the time, GEODES was built from a mix of open-source technologies and proprietary solutions developed at CNES. However, as data volumes grew and user needs evolved, GEODES's legacy infrastructure has started to become a bottleneck for rapid feature delivery. Users increasingly required better **scalability** and **interoperability** to interconnect with third-party platforms, and more transparent, well-documented interfaces. Furthermore, new, highly requested datasets, such as the very high resolution Pléiades products, as well as new services and **cloud optimized data formats** expected by the community have driven us to re-evaluate the GEODES architecture. This is why starting in 2026 we have completely overhauled the GEODES architecture. This revamp focuses on two core pillars: **Open Source software** and **Cloud-Optimized solutions**. By moving away from proprietary silos, we have aligned the platform with modern geospatial standards improving performances and interoperability with other geomatic platforms. In this presentation, we will discuss why and how we successfully transitioned GEODES from a monolithic, on-premises model to a highly scalable, cloud-ready, and open-source architecture.We will then detail the new architecture built on a foundation of open-source software and OGC Standards, including: - **OpenSearch** as the indexed catalog (https://github.com/opensearch-project) - **STAC Fast API** for metadata standardization and data access (https://github.com/stac-utils/stac-fastapi) - **OGC processes** for On-demand Processing (https://github.com/opengeospatial/ogcapi-processes) - **GeoServer** and **Titiler** for data display and visualization (https://github.com/geoserver/geoserver and https://github.com/developmentseed/titiler) - **Keycloak** for secure authentication and granular access control (https://github.com/keycloak/keycloak) - **Kong Gateway** for API management (https://github.com/Kong/kong) Finally, we will provide a live demo of the updated GEODES ecosystem, showcasing the new tools and services available to researchers, developers, and the general public. false https://talks.osgeo.org/foss4g-2026/talk/MB7STW/ Dahlia2 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 This presentation highlights how GeoSolutions leverages cloud infrastructure and geospatial technologies to enable precision farming at scale. Drawing on 10 years of experience, it covers data ingestion, optimization, modeling, and GeoServer deployment strategies, along with real-time visualization techniques for designing scalable, high-performance agricultural data solutions. foss4g-2026-5564-supporting-precision-farming-with-geoserver-past-experiences-and-way-forward Andrea AimeSimone Giannecchini en The growing availability of data from drones, Earth observation, and agricultural machinery (i.e., telemetry), combined with the advent of cloud infrastructure, has significantly accelerated innovation in how farmers and agricultural systems are supported. These advances have democratized access to data and capabilities, enabling precision farming solutions at an unprecedented scale. This has become one of the main use cases for GeoServer deployments in recent years. At GeoSolutions, we have collaborated with a wide range of clients—from NGOs to large private companies, from startups to research institutions, helping them to extract value from data through GeoServer and other open-source geospatial technologies deployed at scale in cloud environments. This presentation summarizes 10 years of experience in ingesting, managing, and disseminating data at scale for the precision farming industry. Key topics include: * Optimization and organization of raster data * Optimization and organization of vector data * Data modeling for performance and scalability in GeoServer and PostGIS * Deployment guidelines for scaling and performance of GeoServer * On-the-fly styling for NDVI and other visualizations By the end of the presentation, attendees will be able to design and plan GeoServer deployments to efficiently serve precision farming data at scale. false https://talks.osgeo.org/foss4g-2026/talk/WWEXHZ/ Ran1 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 This presentation introduces a QGIS plugin developed under Japan’s Project PLATEAU to help municipal staff calculate and visualize urban structure evaluation indicators using 3D city models and public datasets. foss4g-2026-5117-developing-a-qgis-plugin-for-urban-structure-evaluation Satoshi Kitashima en This presentation introduces the development of a practical QGIS plugin designed to support urban structure evaluation by municipal staff. In many Japanese municipalities, computing and interpreting urban structure evaluation indicators still depends heavily on external consultants, as the work requires specialized GIS skills and the integration of diverse datasets. Developed under Japan’s Project PLATEAU, the plugin supports workflows for using 3D city models and related public datasets to calculate indicators and visualize results through maps and charts within QGIS. The tool was evaluated through pilot workshops and interviews with municipal staff from multiple municipalities in Japan. The presentation will cover the background of the project, the functions and intended use of the plugin, findings from pilot workshops with municipal staff, and lessons learned from developing an open-source geospatial tool intended for municipal practice. false https://talks.osgeo.org/foss4g-2026/talk/3B7AYV/ Ran1 General session talk 2026-09-03T11:30:00+09:00 11:30 00:30 A tour of New Zealand's Future Topographic Mapping project, aiming to replace legacy tools with modern open source technology foss4g-2026-5019-building-a-national-topographic-system-in-qgis Jonathan Ball en Toitū Te Whenua - Land Information New Zealand is rebuilding NZ's entire topographic production pipeline from the ground up: national-scale feature editing, data validation, cartographic production, and open data publication, all on open-source tools. This talk dives into the architecture, the wins, the hard lessons, and what it really takes to run a whole country's topographic mapping on FOSS4G. false https://talks.osgeo.org/foss4g-2026/talk/3QFGAN/ Ran1 General session talk 2026-09-03T13:00:00+09:00 13:00 00:30 This presentation introduces how GIS is currently used in agriculture in Hokkaido. We also present Hokuren’s initiatives and future plans for utilizing FOSS4G technologies, particularly QGIS, to support agricultural data management and farm operations. foss4g-2026-5060-current-gis-applications-in-hokkaido-agriculture-and-hokuren-s-foss4g-initiatives-with-qgis Yuhi Yamamoto en Hokkaido is one of the regions that supports Japan’s food production. However, the number of farmers has been decreasing, while the area of cultivated land managed by each farmer has been increasing. Under these conditions of large-scale farming, improving operational efficiency and reducing labor through effective data utilization has become increasingly important. Geographic Information Systems (GIS) provide useful tools for managing field-level agricultural data. GIS enables the integration and visualization of diverse datasets, including planting and work records, remote sensing data, and weather data. By linking these datasets through spatial information, GIS supports data integration and facilitates decision-making in farm management. In recent years, GIS-based farming support tools have gradually become more widespread in Japan. Hokuren is a federation of Agricultural cooperatives in Hokkaido. We operate a Sales Business supplying agricultural and livestock products produced in Hokkaido to domestic and international markets. In addition, Hokuren carries out Purchasing Business and Farming Support Activities to assist agricultural producers. Through its farming support activities, Hokuren works to address challenges at agricultural production sites by utilizing various technologies. In particular, geospatial technologies have the potential to support farm management by enabling the integration of diverse agricultural data. However, the practical adoption of such technologies in agricultural production sites remains limited due to gaps between field-level needs and available IT solutions. To address this issue, we aim to utilize Free and Open Source Software for Geospatial (FOSS4G), particularly QGIS, as a platform for developing practical tools that support agricultural production sites. QGIS provides a flexible and extensible environment that enables the integration, visualization, and management of agricultural data. In this presentation, we discuss our approach to bridging agriculture and IT through QGIS-based solutions and explore the development of user-friendly tools that can be applied in real agricultural production environments. false https://talks.osgeo.org/foss4g-2026/talk/778RKA/ Ran1 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 This talk details an end-to-end analysis pipeline for early-season crop mapping. We demonstrate how to process multi-sensor Sentinel data , automate CWT scalogram generation , and deploy optimized PyTorch CNN models. This workflow enables rapid, scalable geospatial inferencing by minimizing data sequence requirements. foss4g-2026-5556-end-to-end-deep-learning-analysis-pipeline-for-early-season-crop-mapping-using-sentinel-data-and-continuous-wavelet-transforms Sarawut NinsawatThantham Khamyaisittichai choosumrong en Transitioning from localized scripts to robust geospatial machine learning pipelines requires a strong foundation in Free and Open Source Software (FOSS). This session details the technical engineering of a deep learning pipeline designed to automate crop classification and early Start of Season (SOS) estimation. Development focuses on the programmatic ingestion and preprocessing of high-resolution multi-sensor data from Sentinel-1 (SAR) and Sentinel-2 (optical) constellations. To mitigate irregular observation gaps and cloud cover, the open-source pipeline computes vegetation indices and constructs 3D NumPy data cubes formatted for deep learning ingestion. The preprocessing module applies a 3rd-order Butterworth filter to interpolate missing values and smooth the signal into consistent weekly intervals, preventing false peaks from data anomalies. The core feature engineering component leverages Continuous Wavelet Transforms (CWT) to project 1D temporal signals into 2D time-frequency scalograms. This transformation isolates distinct phenological wave properties, generating highly separable spatial feature maps.We will explore the PyTorch implementation of this pipeline, comparing the data flow and tensor operations in CNN2D based deep learning model architecture. A key technical highlight is the custom automated incremental analysis module, which programmatically detects the mathematical knee point of validation accuracy curves to dynamically identify the minimum temporal data sequence required for reliable inferencing. By automating this threshold discovery, the PyTorch-based CNN2D architecture successfully processes CWT scalograms to achieve mapping process utilizing only a 6- to 7-month data sequence. Geared toward GIS developers and machine learning engineers, this talk provides actionable patterns for structuring complex spatial data cubes, integrating advanced signal processing into automated workflows, and deploying optimized deep learning models using a fully open-source stack. false https://talks.osgeo.org/foss4g-2026/talk/TSHBPR/ Ran1 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 Rekichizu is a web service for exploring historical maps of Japan in a modern digital map style. Through data creation with QGIS and collaborative open data production with CODH, it makes historical landscapes accessible to everyone, preserving cultural memory through open-source technology. foss4g-2026-5166-rekichizu-designing-modern-style-historical-maps-to-preserve-cultural-memory Hajime KatoAsanobu Kitamoto en Historical maps are invaluable records of how landscapes, cities, and communities have evolved over time. However, traditional Japanese historical maps present significant barriers for non-specialists: place names written in cursive script (kuzushiji), distorted landforms compressed to fit the page, and orientation conventions that differ from modern maps. Rekichizu (rekichizu.jp) addresses these challenges by reconstructing historical maps in the familiar visual style of contemporary digital maps, making them intuitively understandable to anyone. The project began in 2019 when a social media post showing a modern-styled Edo-period map generated enormous response, revealing strong public demand for accessible historical cartography. Since then, Rekichizu has grown into a full-featured web map application covering multiple historical periods, built entirely with open-source tools and open data. A key aspect of the project is its collaborative approach to open data creation. In partnership with the Center for Open Data in the Humanities (CODH) at the National Institute of Informatics, we have jointly produced and published new geospatial datasets — including approximately 7,700 km of major Edo-period road networks and townhouse area polygons extracted from all 29 historical Edo kiriezu maps. These datasets are freely available in GIS formats. Rekichizu also integrates CODH's dataset of over 80,000 historical place names and the Edo Maps place name dataset, evolving beyond a map visualization into a platform for historical geographic information infrastructure. Map data creation and processing is handled with QGIS. The frontend is built with MapLibre GL JS, serving vector tiles for smooth, interactive map exploration. Map style design is done using Maputnik. The tile data and map styles are also published under a Creative Commons license, enabling reuse by other projects. This talk will cover the design philosophy and production process behind Rekichizu, as well as the collaborative effort with CODH to build a historical geographic information infrastructure through open data creation. As Hiroshima embodies the importance of preserving memory, we believe this project resonates deeply with the conference theme of bridging technology and humanity. false https://talks.osgeo.org/foss4g-2026/talk/8HX9KE/ Ran1 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 This presentation introduces a practical workflow for converting proprietary stormwater model data into open formats using CSV and SWMM inputs, enabling reuse and visualization in QGIS and Giswater environments. foss4g-2026-5186-a-practical-workflow-for-reusing-proprietary-stormwater-model-data-in-open-source-gis-using-csv-and-swmm-formats-with-giswater Albert BofillYoshihiro Shibuo en Many municipalities and practitioners rely on proprietary stormwater management software to maintain and operate drainage network models. While these models represent valuable public assets, their reuse in open-source environments is often limited by vendor-specific data formats and licensing constraints. This study presents a proof-of-concept workflow that enables the reuse of such existing assets in open-source GIS platforms through a simple and transparent data conversion process, with application in Giswater. The proposed workflow assumes that core network data—nodes, links, coordinates, and basic attributes—are exported from proprietary stormwater models as CSV files using standard export functions. These CSV datasets are then mapped to the EPA SWMM input format (SWMM .inp) using lightweight Python scripts based on straightforward table-to-table mappings. The resulting .inp files are subsequently visualized and inspected in QGIS, allowing users to verify network structure and attributes without requiring access to proprietary software. This workflow is intended for municipalities, consultants, and researchers who manage stormwater networks in closed-source systems but wish to incrementally integrate open-source tools into their workflows. By treating CSV files as a clearly defined boundary format and SWMM .inp as a common open schema, the approach minimizes vendor dependency while preserving existing data investments. The converted datasets can also serve as inputs for downstream open-source platforms, specifically Giswater. This opens the possibility for more advanced data management and analysis within the Giswater environment. The presentation shares the conversion concept, data assumptions, and implementation considerations, providing a practical reference for organizations seeking to unlock proprietary stormwater model data for open-source GIS use. false https://talks.osgeo.org/foss4g-2026/talk/ATUYMC/ Ran2 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 We present the Geonorge.Forvaltning.Client, a web-based GIS interface designed for the collaborative maintenance of Norway’s national map data. By lowering technical barriers for public administrators, this tool fosters inter-agency transparency and democratizes spatial data management, aligning with the FOSS4G spirit of open, accessible geospatial technology. foss4g-2026-5143-smarter-geospatial-management-a-collaborative-web-client-for-national-spatial-data /media/foss4g-2026/submissions/CSBFQN/smarter_management_hiroshima_kq4eDkV.png Henrik Gulliksen SchüllerAnders Eirik Hynne Haugen en Maintaining accurate national spatial data infrastructure (NSDI) often requires specialized, heavy desktop software, creating silos between data experts and local administrators. The Geonorge.Forvaltning.Client ("Smarter" Geospatial Management) changes this paradigm. Developed by Kartverket (the Norwegian Mapping Authority), this open-source web application allows diverse public entities—from small municipalities to national agencies—to collaboratively edit, manage, and analyze geographic datasets directly in the browser. In this session, we will explore: - The "GIS-lite" Philosophy: How the client simplifies complex GIS tasks (geometry editing, metadata entry, and tabular updates) for non-expert users. - Dynamic Schema Management: Enabling users to define and modify dataset structures on the fly while maintaining strict data integrity through validation. - Collaboration & Access Control: How the system manages permissions across organizational boundaries to ensure "Smarter Management". - Real-world Impact: Case studies on how this collaborative approach improves the speed and quality of Norway’s map data updates. This talk is ideal for developers and decision-makers interested in modernizing NSDIs using open-source web technologies and collaborative workflows. false https://talks.osgeo.org/foss4g-2026/talk/CSBFQN/ Ran2 General session talk 2026-09-03T11:30:00+09:00 11:30 00:30 Decision-makers invest in innovations and programs to create positive social and environmental outcomes. However, there is no consistent or standardized method to evaluate the process or impact of these decisions. A decision support tool provides access to machine learning (ML) features, generating insights revealing core predictors behind key outcomes. foss4g-2026-4985-from-data-to-insights-decision-support-tool-application-with-the-goa-agriculture-department Rishika Jerath en The decision-making process in impact investing often relies on heuristics and subjective assumptions, unable to address the complexity of the variables involved. This challenge affects approximately USD 1.57 trillion in impact investments (Hand et al., 2024) and roughly 46 percent of fund flows in the food and agriculture sectors in the Asia-Pacific region (CLIC, 2025). The need for dynamic information systems driven by feedback loops becomes increasingly crucial in these sectors, given the changes in climate, evolving crop calendars, and unpredictable extreme weather events. Tools leveraging free and open-source solutions (Python, FastAPI), data stacking with ML insights, and the use of GIS unlock insights driven by field data and infuse existing systems with greater transparency. The decision-making landscape in agriculture is impacted by various input variables. From seed to soil conditions, land management practices to final crop yields – there are numerous variables impacting productivity. ML tools can identify the primary drivers of yield, leading to more targeted implementation of schemes and improved advisories for farmers. The Directorate of Agriculture of the Goa government collects crop data annually, yet continues to ask: What is driving rice productivity in the state? They seek insights without the burden of costly software or tools that require extensive technical training. Additionally, seek ways to investigate the impact of extreme weather events and the factors that help mitigate crop loss or maintain yields. GRANULENS addresses this challenge by offering an online tool that moves directly from data to insight. Through a concise three-step process, it distills complex datasets into clear, visualized insights that highlight the factors driving defined measures of success or risk. Its central aim is to deliver predictive analytics and actionable insights intuitively. Based on insights generated on rice yield models (n= 9445 over 3 Kharif cycles), the core drivers or predictors of rice yield were identified, and additions to the existing data collection system of the Department of Agriculture were made. This created a data framework that can investigate or reveal the core predictors of rice yield using the Kharif 2025 season's data, across its 11 administrative zones. The state of Goa saw unprecedented rain close to the harvest season last year. The tool offers insights where farmers who harvested earlier (than the median harvest date range in a specific region/zone) mitigate crop loss due to the untimely rains. Additionally, in a smaller subset of the sample, the sowing date relative to the median of the region also played a role in driving yield. The tool can run any permutation combination of the success criteria (yield in this example) and set of input indicators to investigate the core drivers. It identifies the top 8-10 core predictors from over 70 indicators in the existing use case example using government data at the farmer field level. The ML feature of the tool has been tested using datasets from agricultural research conducted at the International Maize and Wheat Improvement Center (CIMMYT) and Environmental Defense Fund. false https://talks.osgeo.org/foss4g-2026/talk/M3P3RH/ Ran2 General session talk 2026-09-03T13:00:00+09:00 13:00 00:30 GeoSampa 2.0 modernizes São Paulo’s geospatial platform through the adoption of the OSGeo-based GIFramework Maps, combining technical evolution with international collaboration. The upgrade improves usability and scalability while preserving an open SDI, demonstrating how open-source technologies can deliver high-performance, interoperable solutions under real institutional constraints. foss4g-2026-5638-from-openlayers-legacy-to-giframework-modernizing-geosampa-s-web-mapping-interface Carolina Bracco Delgado de AguilarAndre Santana Meireles en The city of São Paulo (Brazil) stands out as the largest metropolis in Latin America, with approximately 12 million inhabitants across a territory of about 1,500 km², presenting significant complexity in terms of urban planning and the implementation of smart city solutions. Given its strategic relevance, the municipal public administration is frequently exposed to pressure to adopt proprietary web mapping solutions offered by major technology companies. In response, São Paulo has deliberately maintained and evolved an open-source geospatial strategy, prioritizing interoperability, cost sustainability, and control over data and technical knowledge. GeoSampa, the official geospatial information system of the city of São Paulo, was developed in 2014 based on an open-source spatial data infrastructure, using PostGIS as the spatial database, GeoServer for service publication, and OpenLayers 2.2 as the web visualization library. Over the years, the data infrastructure has been consolidated and new integrations with corporate systems have been established, forming a Spatial Data Infrastructure (SDI) that is considered a reference in the Brazilian context. However, the evolution of the web interface and user experience remained limited, becoming a constraint for expanding the platform’s use. Starting in 2024, a modernization process was initiated, including updates to the backend environment and restructuring of services on Linux servers. In 2025, based on recommendations from the OSGeo community, GIFramework Maps was adopted as the solution for the visualization layer. This approach allowed the preservation of the existing database and OGC services architecture, while introducing a more modern, responsive, and configurable interface. The GeoSampa 2.0 project was structured through technical exchange between teams from the São Paulo municipality and Dorset County (UK), with GIFramework as the main component of the interface modernization. The solution was deployed on on-premises infrastructure managed by PRODAM, the municipal IT company, with content management handled by the Municipal Secretariat of Urbanism and Licensing (SMUL). The adoption of GIFramework reinforced the existing open architecture, enabling greater autonomy in configuring layers and content, previously concentrated in the backend, and reducing operational complexity in geospatial information management. The new version also expanded functionalities for data access, visualization, download, and service consumption, which were previously limited or unavailable. As a result, improvements were observed in usability, performance, and the platform’s ability to support different user profiles, particularly in processes related to zoning, licensing, and environmental management. These results demonstrate that an open-source geospatial strategy can achieve robust performance at scale while consistently addressing external pressure toward proprietary solutions. false https://talks.osgeo.org/foss4g-2026/talk/XZYNGV/ Ran2 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 From our production “KnoWaterLeak” leakage-risk service, we show real-time, on-demand vector tiles for large pipeline datasets. We cover in-database MVT generation, safe DSL-to-JSON translation, routing between tile and aggregation endpoints, and data-model choices that keep latency low and database load predictable. foss4g-2026-4855-inside-the-engine-of-knowaterleak-real-time-search-and-on-demand-vector-tiles-for-large-pipeline-data /media/foss4g-2026/submissions/ECGZTK/v2map_sample_kNjws6l.png Kenji TakaseEugene Kim en This talk shares a production case study from “KnoWaterLeak,” a multi-tenant water pipeline leakage-risk assessment service. Tenants manage hundreds of thousands of pipe segments, and users expect smooth map navigation with on-the-fly filtering by asset attributes and risk overlays. Since tile content varies with both query conditions and permissions, pre-generating static tiles is not practical. We present an architectural pattern we use in production to deliver dynamic vector tiles with low latency and predictable database load. The core idea is to treat the database as the rendering engine: we generate Mapbox Vector Tiles (MVT) inside PostGIS using tile bounds and ST_AsMVT. The application layer acts as a gateway that validates and translates request parameters into database-function inputs for the tile endpoint, and routes requests to separate non-tile endpoints when users need aggregated results (for example, total pipeline length). This keeps MVT encoding and heavy spatial computation in the database, while the application focuses on safe parameter handling and efficient streaming of responses. A key challenge is enabling expressive, user-defined filters without exposing the system to SQL injection or unbounded query complexity. We introduce a small DSL for filter expressions, validate it with a strict whitelist, and convert it into a structured JSON parameter. With a tile-serving layer that can map HTTP query parameters to PostgreSQL function calls (Martin in our deployment), we avoid dynamic SQL in the application: the database-side tile function interprets the JSON and applies optimized query paths such as early returns, zoom-threshold checks, and EXISTS-based joins. We also highlight data-model and preprocessing choices that are essential for performance at scale. Concrete examples include designing a single table that co-locates domain attributes, geometry, and zoom-level visibility thresholds; precomputing expensive metrics such as intersection lengths between pipe segments (LineString) and risk zones (Polygon) for aggregation and visualization; and building composite (and, where appropriate, partial) indexes tailored to common filter combinations to minimize joins and keep query plans predictable. Grounded in lessons from this production deployment, we present a set of reusable patterns for PostGIS-driven vector tile delivery. The talk covers key trade-offs—precompute vs. on-demand computation, safe and maintainable dynamic filtering, and database function design for stable performance—so attendees can learn from the design decisions behind a real-world, PostGIS-driven tile pipeline. false https://talks.osgeo.org/foss4g-2026/talk/ECGZTK/ Ran2 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 WoSIS is a global soil information service that safeguards, standardises and shares soil profile data from contributors worldwide. Built on PostgreSQL/PostGIS, GraphQL and OGC services, it covers the full cycle from ingestion to dissemination. This presentation discusses the architecture, soil data workflows, and latest developments. foss4g-2026-5106-wosis-global-soil-information-service-powered-by-open-source-tools Luis Calisto en WoSIS is developed and maintained by ISRIC with the aim to safeguard world soil profile data, quality-assess and standardise the data, and serve it to support digital soil mapping and environmental applications. WoSIS draws on voluntary contributions from data holders worldwide who share their data for the benefit of the international community. - The architecture is built entirely on open source technologies: - PostgreSQL/PostGIS as the core spatial database, with a data model based on the ISO 28258 standard; - GraphQL APIs (PostGraphile and Node.js) for data ingestion and dissemination; - Automated ETL pipelines that validate, harmonise and standardise contributed datasets; - OGC web services, dashboards, metadata catalogues and DOI referenced snapshots. Data ingestion follows a strict protocol. Submitted soil profiles undergo automated checks for overlapping horizons, implausible values, unit inconsistencies and vocabulary mismatches. WoSIS never alters the original data; it flags issues for the contributor to resolve before re-submission. Only error-free datasets enter the system. In return, providers receive dashboards, downloads in multiple formats, and if needed a DOI for findability and citeability. Standardised data flows out through GraphQL, OGC services and freely available snapshots, feeding downstream products such as SoilGrids. In this presentation we will walk through the soil data workflows, the open source architecture behind them, and the latest developments in the WoSIS APIs. false https://talks.osgeo.org/foss4g-2026/talk/GRBRKS/ Ran2 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 This presentation concerns migrating an SDI from a single-machine, multi-container Docker architecture to a cluster-based solution. The authors will share their experience, including performance testing results and an evaluation of potential bottlenecks. They will also discuss the critical human factors involved in such a migration. foss4g-2026-4993-shifting-from-a-monolithic-to-a-scalable-sdi-architecture-is-it-worth-it /media/foss4g-2026/submissions/HBPAGE/diagram_fl9wi24.png Antonio CercielloJoana Simoes en This question is a bit rhetoric, as the answer will depend on a number of factors such as requirements, but also capacities. However, it will give us a good excuse to share some of our experience in a journey of migrating from a single machine, multi container docker architecture to a cluster based solution. The system we migrated is a Spatial Data Infrastructure (SDI) based on a stack free and open source components, many of them OSGeo projects (e.g.: pygeoapi, PostGIS, GeoHealthCheck). We will share some performance testing of the two deployments and evaluate some possible bottlenecks. We will also share some insights into the human component of the SDI, which can prove critical on the decision to embrace this migration path. We hope this experience can prove useful to someone considering attempting this journey. If you think this is a discussion worth having, we look forward to seeing you at our talk! false https://talks.osgeo.org/foss4g-2026/talk/HBPAGE/ Conference Management Room1 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 Mastering QGIS Map Themes streamlines cartographic workflows, automates Atlas production and enhances field data collection using Mergin Maps and QField. This approach ensures consistent, efficient map production and rapid switching between map views for diverse GIS tasks. foss4g-2026-5633-one-project-all-views-qgis-map-themes Tomaž Šturm en This presentation focuses on map production in QGIS using Map Themes. The goal is to demonstrate how Map Themes can streamline cartographic workflows, improve consistency and support both indoor and outdoor GIS tasks. Map Themes in QGIS provide a powerful way to manage layer visibility, styling, and map content for different purposes. By defining multiple themes within a single project, users can quickly switch between different map views without duplicating data or creating separate project files. This is particularly useful in projects that require multiple outputs, such as thematic maps, analytical views or complex layouts. The Atlas tool in QGIS enables automated map production by combining Map Themes, users can generate large series of maps efficiently, ensuring consistent styling and structure across all outputs. This approach significantly reduces manual work, minimizes errors making and enhace cartographic production. The workflow is further extended into the field using Mergin Maps and QField. These mobile applications uses Map Themes as faster switch beetwen different layers and basemaps. The presentation will demonstrate how combining these tools—Map Themes, atlas generation, and mobile GIS applications—enables a faster, more efficient and flexible approach to geospatial workflows. It will also highlight best practices for integrating these components into everyday GIS tasks, supporting both individual users and organizational needs. false https://talks.osgeo.org/foss4g-2026/talk/GHYWFP/ Conference Management Room1 General session talk 2026-09-03T11:30:00+09:00 11:30 00:30 Topology validation is an essential tool for GIS quality control; however, discrepancies frequently arise between ArcGIS and QGIS results. This presentation compares the outcomes of both tools, analyzes the practical issues stemming from these differences, and proposes potential solutions to ensure consistent data integrity. foss4g-2026-5257-quality-control-leveraging-the-characteristics-of-proprietary-and-open-source-gis-discrepancies-in-topology-validation-and-strategic-selection misako ueno en In our daily operations, we utilize the open-source software QGIS for data production, incorporating topology validation for quality assurance. However, discrepancies in quality standards emerged because our clients conduct their quality control using ArcGIS. This difference in software environments led to inconsistent validation outcomes. Consequently, we compared the quality check results from both platforms and examined potential solutions to address the challenges stemming from these inter-software discrepancies. false https://talks.osgeo.org/foss4g-2026/talk/GZFWDL/ Conference Management Room1 General session talk 2026-09-03T13:00:00+09:00 13:00 00:30 Caucaia's story reveals how transitioning from fragmented files to an open spatial database is transforming urban management. By adopting PostGIS and QGIS, we overcame historical challenges and are currently structuring our data architecture to achieve the interoperability required by the national SINTER framework by the end of 2026. foss4g-2026-5503-from-legacy-data-to-national-standards-preparing-a-brazilian-city-for-federal-interoperability-with-foss4g /media/foss4g-2026/submissions/KFUPGQ/Screenshot_from_2026-03-21_22-24-46_mqr9c4X.png Narcélio de Sá en Modern urban management requires precision and integration, yet many local governments operate with disconnected data silos, relying on outdated CAD files and static PDFs. At the Secretariat of Finance in Caucaia, we faced the colossal challenge of translating this chaotic legacy into a dynamic, georeferenced Multipurpose Territorial Cadastre. Our journey began with the strategic decision to adopt an infrastructure based entirely on open-source tools, ensuring true technological sovereignty for the municipality. We structured this transformation by migrating isolated data into a robust architecture using PostgreSQL and PostGIS. Harnessing the power of QGIS, we processed high-resolution aerial imagery, enabling us to extract precise features, digitize urban boundaries, and feed our new Real Estate Value Observatory. This technical effort represented a paradigm shift in how the municipality applies fiscal justice, abandoning a valuation plan stagnant since 1995. A major driver of this modernization is preparing the municipality for National System for Territorial Information Management - SINTER, a watershed federal initiative in Brazil aiming to unify cadastral and tax data under the Brazilian Real Estate Registry (CIB). With the national integration deadline set for the end of 2026, Caucaia is actively using the FOSS4G ecosystem to pave the way. The flexibility of free software is proving crucial in modeling our data extraction and transformation (ETL) processes, cleansing historical legacy data, and ensuring our local database is ready to act as a seamlessly integrated node within the federal infrastructure. The results of this ongoing journey demonstrate the profound administrative and technical impact of leveraging open geographic intelligence. By transitioning to a structured PostGIS architecture and processing high-resolution imagery, we successfully identified tens of thousands of unmapped built areas and corrected historical topological errors. Our experience demonstrates how free technology prepares cities for the complex challenges of national interoperability, offering a technical roadmap for other municipalities racing against the clock. false https://talks.osgeo.org/foss4g-2026/talk/KFUPGQ/ Conference Management Room1 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 This study analyzes multi decadal spatiotemporal land cover change in Dammam, Saudi Arabia (1986–2025) to establish a green infrastructure baseline. An open source data, Landsat and Sentinel-2 data classified using Random Forest in QGIS reveal urban expansion patterns and support greening priority planning using NDVI and NDBI. foss4g-2026-5389-green-infrastructure-baseline-using-open-source-geospatial-data-for-dammam-saudi-arabia Sultan Hasan Alsultan en The rapid expansion of metropolitan areas in arid regions necessitates a sophisticated approach to environmental management and urban planning. Dammam, the primary administrative and industrial hub of Saudi Arabia’s Eastern Province, has undergone significant transformation over the past four decades. As the city generates substantial solid waste, estimated between 20000 and 35000 m³ per month, maintaining a balance between built-up infrastructure and ecological health has become a central priority for local authorities. In these hyper-arid climates, urban vegetation is vital for improving air quality and elevating overall livability. However, rapid development and climatic stress frequently reduce the available green cover, requiring proactive monitoring and strategic intervention. To address these environmental challenges, the Kingdom has established a comprehensive regulatory and strategic framework under Saudi Vision 2030 and the Saudi Green Initiative. These national objectives, supported by the MAWAN Vision 2040, emphasize the integration of advanced technology to ensure sustainable land use and environmental protection. Remote sensing provides a powerful longitudinal tool for monitoring these complex urban dynamics across varying scales, especially open-source geospatial tools and data that play a crucial role in advancing accessible spatial analysis. The continuous archives of the Landsat mission, including Landsat 5, 7, 8, and 9, offer an unparalleled 40-year record of land cover changes. This allows for the tracking of urban footprints through indices like the Normalized Difference Built-up Index. and the Normalized Difference Vegetation Index. This study presents a multi-scale assessment of Dammam’s evolution from 1986 to 2025 by fusing decadal spatiotemporal analysis with high-precision baseline mapping. By implementing a Random Forest classification for four distinct land classes, which include Vegetation, Built-up, Water, and Barren land, and integrating sub-meter vegetation inventories, this research provides a comprehensive evidence base for environmental enforcement and greening strategy. The resulting neighborhood-level analytical profiles and Greening Priority Index offer a specialized framework for municipal resource allocation, directly supporting the Kingdom’s mandate for a resilient and sustainable urban future. The long-term analysis of the Dammam metropolitan landscape between 1986 and 2025 reveals a significant imbalance between rapid urban expansion and the growth of green infrastructure. Across the three study areas, urban development has progressed much faster than vegetation expansion, creating increasing ecological pressure. Area 1 experienced rapid residential growth after 2016, resulting in a large gap between built-up land and vegetation. Area 3 shows similar patterns driven by industrial expansion, where infrastructure development far exceeded the establishment of green spaces. In contrast, Area 2 highlights the gradual loss of traditional agricultural landscapes due to continuous urban encroachment. The MCDA analysis indicates that roadside corridors and residential areas are the most suitable locations for greening interventions across the study areas. Prioritizing vegetation in these spaces can maximize environmental benefits while directly improving the quality of everyday urban life. these findings indicate that urban growth has occurred with limited ecological compensation, emphasizing the urgent need for more strategic and integrated urban greening policies to support sustainable metropolitan development. false https://talks.osgeo.org/foss4g-2026/talk/NACYNM/ Conference Management Room1 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 Remote regions of the Pilbara, WA often lack access to high-resolution imagery land coverage. This talk presents an open-source workflow using DJI RTK drones, WebODM and QGIS, expanding access to high-resolution imagery in remote regions by enabling land managers to generate centimetre-level spatial datasets on demand. foss4g-2026-5431-from-drone-to-map-an-open-source-workflow-for-high-resolution-imagery-in-remote-landscapes-of-the-pilbara-western-australia /media/foss4g-2026/submissions/NHBBBN/DJI_0306_790cCLw.JPG Danielle Whiteley en Access to high-resolution imagery remains a significant challenge for organisations operating in remote landscapes. In Western Australia’s Pilbara region land managers including pastoral operators, conservation groups, environmental consultants and indigenous ranger program manage vast areas but often rely on satellite imagery that may be outdated, cloud-affected, or insufficiently detailed for operational decision-making. At the same time, advances in drone technology and the maturity of open-source geospatial tools now make it possible to implement fully open, end-to-end drone mapping workflows without reliance on proprietary processing platforms. This presentation introduces a reproducible, open-source drone mapping workflow using DJI RTK drones for data capture, WebODM for photogrammetry processing, and QGIS for spatial analysis and data management. The workflow is designed specifically for remote and low-connectivity environments, with a focus on expanding access to high-resolution imagery in remote regions. Drawing on field-tested implementations across the Pilbara, the talk will step through the complete workflow from capture to analysis, including: • Designing drone survey missions for large and remote areas • Capturing imagery using RTK-enabled drones to reduce reliance on ground control • Processing datasets using WebODM to produce orthomosaics, digital surface models and point clouds • Integrating outputs into QGIS for visualisation, analysis and monitoring workflows • Structuring and managing large imagery datasets in low-connectivity contexts Using this workflow, organisations can generate centimetre-level spatial datasets on demand, expanding access to high-resolution imagery in remote regions and enabling more timely and locally relevant decision-making. The presentation will include applied examples demonstrating how these outputs are used across a range of land management contexts in the Pilbara, including environmental monitoring, fire impact assessment and infrastructure mapping. In addition to the technical workflow, the session will highlight key implementation considerations for remote contexts, including hardware requirements, processing constraints, data storage strategies, and approaches to building workflows that are maintainable by individuals or small teams with varying levels of GIS experience. A key contribution of this work is demonstrating how open-source tools can support a shift toward locally controlled, self-sufficient spatial data production. By reducing reliance on commercial imagery providers and proprietary processing software, organisations are able to take greater ownership of their spatial data, improve responsiveness, and build internal capability. The talk will conclude by sharing lessons learned, common pitfalls, and recommendations for adapting this workflow in other remote or resource-constrained environments, with the aim of supporting broader adoption within the open geospatial community. false https://talks.osgeo.org/foss4g-2026/talk/NHBBBN/ Conference Management Room1 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 Large infrastructure projects rely on complex spatial analysis. This presentation explores how an open-source GIS stack—centred on QGIS, PostGIS and GeoServer—can support corridor planning, environmental assessment and spatial data integration in infrastructure organisations. foss4g-2026-5342-how-open-source-gis-can-support-large-scale-linear-infrastructure-projects /media/foss4g-2026/submissions/NWWKLW/LinearInfrastructureProjects_2_TcgoscG.png Mike Gresham en Large infrastructure projects such as rail corridors and high-voltage transmission networks involve complex spatial decision-making across hundreds or thousands of kilometres. Route selection, environmental impact assessment, land acquisition, terrain modelling and stakeholder engagement all depend heavily on geospatial data and analysis. In practice, many infrastructure projects are delivered using proprietary GIS and engineering software stacks. However, the open-source geospatial ecosystem has matured significantly in recent years and now provides powerful tools capable of supporting many of these workflows. This presentation explores how open-source GIS technologies can support the planning and delivery of large-scale linear infrastructure projects. Drawing on practical experience from major Australian infrastructure initiatives including rail corridor development and long-distance transmission line planning, the talk examines the spatial challenges these projects face and how open-source tools can address them. The session will highlight how technologies such as QGIS, PostGIS, GRASS, Geoserver and GDAL can support tasks such as least-cost corridor modelling, environmental impact analysis, land tenure assessment and large-scale spatial data integration. The presentation will discuss the opportunities and limitations of open-source GIS within infrastructure environments and consider where it complements specialised engineering tools rather than replacing them. Attendees will gain a practical perspective on how open-source geospatial technologies could support infrastructure projects at regional and national scale. false https://talks.osgeo.org/foss4g-2026/talk/NWWKLW/ Conference Management Room1 General session talk 2026-09-03T15:00:00+09:00 15:00 00:30 A real-world case from Paraguay showing how a combination of open-source geospatial tools supports biodiversity monitoring, trail management, wildlife tracking, and conservation. The system integrates field data, fire alerts, deforestation monitoring (GLAD), and lightweight drone data into a single, scalable workflow for protected area management. foss4g-2026-5541-caring-for-a-living-park-an-open-source-geospatial-system-inspired-by-field-experience-cerro-kavaju-paraguay Atahualpa Ayala Gómez en Protected areas often lack integrated tools to manage biodiversity, tourism, and environmental monitoring in a unified way. As a result, data remains fragmented, decision-making is limited, and conservation efforts are less effective. This presentation introduces a real-world implementation of an integrated system based on a combination of open-source geospatial tools, designed to support the full management of a protected area, using Cerro Kavaju (Paraguay) as a case study. The project originated from personal field experience in Cerro Kavaju, initially through rock climbing and later through biodiversity research. This direct engagement led to the creation of Tekolab, an initiative focused on the conservation and sustainable management of the area. The system integrates multiple components into a single workflow: web mapping for public engagement, trail planning and management, georeferenced biodiversity data (flora inventories), wildlife monitoring through camera traps, and recreational management including rock climbing routes. In addition, environmental monitoring is incorporated through the integration of fire alerts and deforestation alerts (e.g., GLAD), complemented by lightweight drone data and field observations to provide a multi-scale understanding of ecosystem dynamics. The system is built entirely with open-source technologies, including PostGIS, GeoServer, MapStore, GeoNode, ODK, and Apache Superset, forming a modular and scalable geospatial infrastructure. This talk focuses on lessons learned from implementing an integrated system in a real conservation context, including challenges in data integration, field data collection, and system adoption. The project demonstrates how a combination of open-source geospatial tools can go beyond visualization to support the full lifecycle of protected area management, offering a cost-effective and replicable model for similar initiatives in Latin America and other regions. false https://talks.osgeo.org/foss4g-2026/talk/QM7P9W/ Conference Management Room2 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 National Land Survey of Finland is developing a new system for updating National digital terrain model (DTM). This talk covers the main features of the new solution. Focus is in the process workflow and how the quality management is done. foss4g-2026-5067-updating-national-terrain-model Risto Ilves en National digital terrain model in Finland is based on lidar data. The current data is collected by using 5 points / m2, but this will be changed to 20 points / m2. Old system can not handle dense point clouds and there are also some quality related issues to be solved. This talk covers the development needs and aims. The new production workflow and technology solution is presented. The main idea is to recognize the change areas and the elevation model is updated only from these areas. Typically there are also some other changes in those areas, so this information can also be used for updating other features in the topographic database. false https://talks.osgeo.org/foss4g-2026/talk/QQP7GK/ Conference Management Room2 General session talk 2026-09-03T11:30:00+09:00 11:30 00:30 Stop struggling to share QGIS maps. With Kumoy, you can push your QGIS projects into an interactive web map in just a few clicks. You can also work together on the same project, manage roles and permissions, and keep QGIS maps and data in sync. foss4g-2026-5329-kumoy-turn-your-qgis-maps-into-cloud-native /media/foss4g-2026/submissions/GLKXKR/Kumoy_QGIS_Natural_Earth_tYX6bdj.png Raymond Lay en Kumoy bridges the gap between desktop GIS and modern web mapping. It is a cloud service that synchronizes your QGIS maps and data to the cloud so you can publish an interactive web map in a few clicks using the Kumoy QGIS plugin. It also supports collaboration by enabling multiple people to work on the same project, with roles and permissions, and by tracking changes to maps and data. This presentation provides an overview of how Kumoy works, from QGIS to the web and across user collaboration. false https://talks.osgeo.org/foss4g-2026/talk/GLKXKR/ Conference Management Room2 General session talk 2026-09-03T13:00:00+09:00 13:00 00:30 This talk addresses some common challenges users face, when consuming data from OGC API. It will map these issues to best practices, offering pointers for data consumers to improve their workflow and providing publishers with answers to common user complaints about the standard or its implementation. foss4g-2026-4994-best-practises-for-consuming-ogc-api-data Joana Simoes en The number of servers publishing data as OGC API has been increasing and that means that more users are looking to consume these data and integrate them into their workflows. This is really good news; however, in order to enable an efficient use of resources and a good user experience, there are some questions we should ask before starting to access the data. In this talk we will share some of the challenges we saw users faced, and map them to best practices of the use of the Standard or the technology. If you are a user, maybe you can find some pointers here to make your OGC API access journey more enjoyable. If you are a publisher, maybe you can find some answers to give to your users, when they complain about the Standard, the implementation or the deployment. false https://talks.osgeo.org/foss4g-2026/talk/LSXJXC/ Conference Management Room2 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 Waystones is an open-source tool for designing geospatial data models and deploying production-ready OGC APIs. By automating the configuration of pygeoapi and QGIS Server, it enables organizations to meet High-Value Dataset (HVD) requirements and share standards-compliant data directly to the cloud. foss4g-2026-5137-waystones-bridging-the-deployment-gap-for-authoritative-ogc-api-services /media/foss4g-2026/submissions/Q3FKPQ/waystones_hiroshima_JNy22z8.png Henrik Gulliksen Schüller en Public sector organizations are currently facing a "deployment complexity gap." While regulations like the EU High-Value Datasets (HVD) Implementing Regulation mandate API access to geospatial data, the technical reality of publishing a compliant OGC API stack is daunting. It requires coordinating spatial databases, feature API servers (pygeoapi), map rendering engines (QGIS Server), and transformation scripts—each with unique configuration hurdles. Waystones is a browser-based application designed to democratize this infrastructure. It provides a visual, end-to-end workflow from model design to live service deployment: Visual Model & Style Editor: Users define layers, field constraints, and spatial relationships through an intuitive UI. Waystones automatically generates the necessary QGIS project files and SLD styles, removing the need for desktop GIS expertise during the publishing phase. Standards-First Deployment: Waystones targets modern OGC API standards (Features, Maps) by auto-generating a Docker Compose deployment kit, compatible with any container hosting environment including Fly.io, with a cloud-native architecture built on Parquet and FlatGeobuf served from object storage via DuckDB — enabling fast cold starts without persistent volumes. It bridges the gap between raw data (GeoPackage/PostGIS) and a live, standards-compliant service in minutes. STAC Catalog Generation: Waystones automatically generates STAC-compliant catalogs alongside deployed services, exposing GeoPackage, FlatGeobuf, and Parquet endpoints — making datasets discoverable to both human users and automated harvesters. AI-Assisted Discovery: The platform leverages optional LLM integration to automate the "metadata chore," generating field descriptions and abstracts to ensure services are not just live, but discoverable and well-documented. Waystones ensures that foundational geospatial infrastructure remains open, interoperable, and accessible to organizations of all sizes. This session will include a live demonstration of transforming a raw GeoPackage into a production-ready OGC API endpoint. false https://talks.osgeo.org/foss4g-2026/talk/Q3FKPQ/ Conference Management Room2 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 Which open mobility dataset should you trust for urban function analysis? Using Copernicus Urban Atlas polygons, we compare Eurostat experimental MNO statistics, OpenStreetMap GPS traces, and census commuting flows. You will learn their biases, and get a fully reproducible Python/PostGIS workflow. foss4g-2026-5654-eurostat-vs-osm-vs-census-choosing-open-mobility-data-for-urban-function-maps /media/foss4g-2026/submissions/T3DTHT/foss4g_session_image_v5_fKOxM97.png Marija Ercegovac en Open mobility data is everywhere, but different sources answer different questions -- and the differences are easy to miss until results conflict. This talk presents a reproducible, open-source workflow to compare three widely accessible mobility proxies on a common spatial reference. Using Copernicus Urban Atlas polygons and the DEGURBA classification (derived from 1 km2 population grid cells), we convert each source into comparable density-normalised temporal indicators (day/night ratios, intraday profiles, weekday/weekend patterns). The three sources: - Eurostat experimental MNO statistics (aggregated, anonymised mobile network operator statistics published by national statistical offices) - OpenStreetMap public GPS traces (community-contributed trace archive; participation bias applies) - Census commuting flows from Eurostat (static origin-destination baseline) We apply a simple clustering step (HDBSCAN) to group similar temporal profiles into functional signatures (residential, office, late-evening activity, mixed-use), and use UMAP only as a visual explanation aid. Instead of selling one "best" dataset, we provide a practical decision guide: which source is best for presence vs flows, what biases to expect, and how to combine sources when a single source falls short. Early findings: MNO statistics capture temporal presence well but availability and comparability vary by country; OSM GPS traces reflect contributor behaviour more than population-level patterns; census flows miss intraday dynamics but anchor the OD baseline. Where sources agree, classification is robust; where they diverge, the divergence reveals structural data limitations worth knowing. What you take away: - A decision matrix with concrete rules of thumb (e.g., intraday presence -- start with MNO; commuting structure -- census OD; fine-grain routes/activities -- OSM, with known participation bias) - Typical biases and coverage gaps of each source across different European urban contexts - A reproducible pipeline: Python + PostGIS + OSRM + QGIS-ready layers and scripts - We will publish the full pipeline as an open repository (Docker/conda, pinned environments, end-to-end scripts to regenerate figures and maps) This talk is for anyone choosing open mobility data for urban analysis who wants to stop guessing and start comparing. false https://talks.osgeo.org/foss4g-2026/talk/T3DTHT/ Conference Management Room2 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 This study demonstrates the integration of satellite imagery and weather data for crop monitoring in Kolhapur using open-source GIS tools. It highlights spatial analysis techniques for assessing crop conditions and supporting agricultural decision-making. keys wrods: GIS, QGIS, Remote Sensing, Crop Monitoring, NDVI, Agriculture, Open Source foss4g-2026-5551-gis-based-crop-monitoring-using-satellite-and-weather-data-a-case-study-of-kolhapur Annepu Pavan Kumar en This presentation focuses on the application of open-source geospatial technologies for crop monitoring by integrating satellite data and weather parameters in Kolhapur, India. The study utilizes freely available satellite imagery, such as Sentinel data, along with meteorological variables including temperature, rainfall, and humidity to analyze crop health and variability. Using QGIS, various spatial analysis techniques are applied, including vegetation indices (such as NDVI), temporal analysis, and map visualization to monitor crop conditions over time. The integration of weather data helps in understanding the influence of environmental factors on crop growth and productivity. The study demonstrates how open-source GIS tools can support agricultural monitoring and decision-making, especially in resource-limited regions. It also highlights the importance of combining remote sensing and GIS for sustainable agriculture and early identification of crop stress. This work is particularly relevant for researchers, students, and practitioners interested in applying open geospatial technologies in agriculture and environmental studies. false https://talks.osgeo.org/foss4g-2026/talk/TGYKS8/ Conference Management Room2 General session talk 2026-09-03T15:00:00+09:00 15:00 00:30 Many African regions face critical geospatial data gaps. This talk shows how open-source tools like QGIS, OpenStreetMap, and open Earth Observation data can generate accessible spatial datasets for environmental monitoring, planning, and sustainable development in data-scarce contexts. foss4g-2026-4961-bridging-the-geospatial-data-gap-in-africa-with-qgis-and-openstreetmap Ruvimbo Doreen Supiya en Limited access to reliable and up-to-date geospatial data remains a major challenge across many African countries, affecting environmental monitoring, urban planning, agriculture, and disaster risk management. Proprietary datasets are often costly or unavailable, creating significant barriers for researchers, students, and local institutions. This presentation explores how open-source geospatial technologies, particularly QGIS, OpenStreetMap (OSM), and open Earth Observation data such as Sentinel imagery, can help bridge the geospatial data gap in data-scarce regions. Drawing from practical experiences in Zimbabwe and academic work in geomatics and remote sensing, the talk demonstrates accessible workflows for spatial data creation, validation, and analysis using fully open-source tools. The session will highlight the role of community mapping, youth-led geospatial initiatives, and open data ecosystems in improving spatial data availability across the Global South. It will also discuss challenges related to data quality, technical capacity, and digital inequality, while proposing scalable and inclusive open-source solutions. This talk aligns with the FOSS4G mission by showcasing how open geospatial software and collaborative mapping can support sustainable development, environmental monitoring, and evidence-based decision-making in underserved regions. false https://talks.osgeo.org/foss4g-2026/talk/WNXSPR/ Conference Management Room3 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 Under the BIG-Z project, Zanzibar has geospatially enabled its Municipals Revenue (MUTM) software. This talk demonstrates using a 100% open-source geospatial stack including QGIS, PostGIS, and GeoServer to solve municipal finance challenges by integrating the Municipal Revenue Management (MUTM) database with payment gateway for real-time compliance visualization foss4g-2026-5572-mapping-for-municipal-revenue-geospatial-upgrades-to-local-governments-revenue-systems-in-zanzibar Mohammed ZAHRAN en In Zanzibar, maximizing Own Source Revenue (OSR) for Local Government Authorities (LGAs) is critical for sustainable urban management. The BIG-Z (Boosting Inclusive Growth for Zanzibar) project initiated a comprehensive geospatial upgrade of the legacy MUTM system, connecting all 12 LGAs across Unguja and Pemba islands. By prioritizing an open-source architecture, we ensured long-term viability and avoided vendor lock-in. This presentation details the technical architecture and practical "LGAs Use-Case," specifically how we: • Transitioned tabular business license and property taxation data into a spatially-enabled PostgreSQL/PostGIS database. • Utilized GeoServer and GeoNode to publish spatial data and interactive dashboards for government decision-makers. • Equipped field inspectors with QField and Mergin Maps to capture coordinates and synchronize data with the centralized system. • Symbolized map data dynamically based on real-time payment statuses through integration with the ZanMalipo government electronic payment gateway. The session concludes by highlighting tangible impacts: supervisors can now monitor field inspections spatially, and revenue officers can optimize collection routes based on geographic "hot spots" of non-compliance. false https://talks.osgeo.org/foss4g-2026/talk/XYDDAD/ Conference Management Room3 General session talk 2026-09-03T11:30:00+09:00 11:30 00:30 This presentation introduces a development plan for a web-based map application for "Watashitachi no Hiroshima (Our Hiroshima)", a supplementary social studies textbook for regional studies that has been used in elementary schools in Hiroshima City for over 50 years. foss4g-2026-5098-development-planning-of-a-web-map-application-for-the-supplementary-social-studies-textbook-watashitachi-no-hiroshima Soichiro Takesaki (Chugoku Syoten) en Chugoku Syoten is the company has published "Watashitachi no Hiroshima (Our Hiroshima)", a supplementary social studies textbook used for regional studies in the third and fourth grades of elementary schools in Hiroshima City for more than 50 years, together with a paper map designed for use with the textbook. This presentation introduces a development plan for an open-source–based web map application designed for use with the textbook. The application is being developed as a future alternative to the accompanying paper map and aims to support regional learning through interactive web-based mapping. false https://talks.osgeo.org/foss4g-2026/talk/G9UGMT/ Conference Management Room3 General session talk 2026-09-03T13:00:00+09:00 13:00 00:30 This talk presents a reusable geospatial platform architecture for operational use in the Global South, and explains how ArkEdge Space Inc. uses MapLibre GL JS, STAC, and TiTiler to build adaptable applications across domains, using an agricultural project with the Paraguayan Space Agency as a case study. foss4g-2026-5275-reusable-geospatial-application-design-for-the-global-south-with-maplibre-stac-and-titiler Kota Yuhara en This talk is based on an agricultural application co-developed by ArkEdge Space Inc. and the Paraguayan Space Agency for public-sector use in Paraguay. While the project responds to a specific operational context, we approached it not as a one-off product but as part of a reusable geospatial platform. The session focuses on practical engineering decisions around web mapping and geospatial data architecture, especially our use of MapLibre GL JS for interactive map experiences, STAC for organizing and searching geospatial assets, and TiTiler for dynamically serving raster data as map tiles, previews, and analysis-ready outputs. Using this project as a case study, I will explain how we structured data and services for multi-source integration, how we designed reusable application capabilities across projects, and how open geospatial components helped us build systems that are extensible, practical for field use, and accessible to non-expert users. Rather than presenting the application from a business perspective, this talk concentrates on implementation lessons that can be applied beyond a single project. The session is intended for developers and practitioners interested in web mapping, spatial data platforms, and the real-world use of open geospatial technologies in the Global South and other operational contexts. false https://talks.osgeo.org/foss4g-2026/talk/RM8DQR/ Conference Management Room3 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 This talk reports from the field: how Japanese government agencies are using Re:Earth Visualizer and CMS — a SaaS-based open WebGIS — to build public data portals and run citizen participation programs, and what we learned deploying it with non-technical users. foss4g-2026-5177-beyond-the-demo-how-re-earth-is-being-deployed-in-government-gis RED (XU CONG) en Across government deployments in Japan, a consistent pattern has emerged: agencies have spatial data and clear public interest use cases, but lack the technical staff to build and operate a GIS platform. Because Re:Earth is a SaaS product, the answer is not to teach them how to deploy software — it is to let them start building immediately. A no-code visual editor and a plugin-based customization model mean that non-technical government staff can go from data to a published platform without writing code, managing infrastructure, or waiting on an IT department. This talk presents two categories of real deployments: **Public-facing GIS** — from Japan's national 3D urban model initiative covering 100+ municipalities, to open data portals built by local governments to make spatial datasets publicly accessible. In each case, non-technical staff independently manage data publication through Re:Earth CMS, with no backend to operate. **Participatory GIS** — citizen urban planning workshops and disaster prevention simulation exercises, where residents and local emergency officers engage with spatial scenarios directly in the browser. No installation, no prior GIS experience required. The focus is on lessons learned: where the no-code model reaches its limits, what "light customization via plugins" actually means in practice, and what open-source licensing concretely changes for government procurement decisions. We will also share how plugins developed for one agency have been reused across others — an open-source multiplier effect that proprietary tools cannot replicate. false https://talks.osgeo.org/foss4g-2026/talk/RX9KWB/ Conference Management Room3 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 This talk presents an offline-first geospatial architecture integrating photogrammetry, point cloud processing, and tree-level analysis using FOSS4G, enabling reliable outputs under constrained operational conditions. foss4g-2026-5592-offline-first-geospatial-architecture-for-tree-level-analysis-using-foss4g-pipelines /media/foss4g-2026/submissions/AXE77B/mermaid-diagram_Fy7RyXL.png Aoi Nagae en Many geospatial systems today assume stable connectivity and cloud-based processing. However, in environments such as tropical regions, these assumptions often do not hold due to limitations in infrastructure, power, and operational conditions. This presentation introduces an offline-first geospatial architecture designed to operate reliably under such constraints using open-source technologies. The workflow integrates photogrammetry and point cloud processing with raster and vector analysis. Using tools such as OpenDroneMap, PDAL, GDAL, and GeoPandas, the system enables end-to-end processing from image-based reconstruction to spatial analysis without reliance on cloud infrastructure. A key design principle is observation-aware processing, where data quality is ensured through the definition of acquisition and filtering conditions, such as overlap thresholds and altitude-based filtering to stabilize reconstruction. The pipeline extends beyond standard orthomosaic generation to tree-level analysis using point cloud data. Digital Surface Models (DSM), Digital Terrain Models (DTM), and Canopy Height Models (CHM) are generated to estimate individual tree height and structure. These outputs are further integrated into GIS workflows, allowing spatial validation and filtering of analytical results. All processes are implemented as a reproducible CLI-based pipeline, enabling scalable and consistent processing across large datasets while significantly reducing processing time. The architecture demonstrates that reliable, decision-grade geospatial outputs can be achieved entirely with open-source tools in constrained environments. Rather than opposing cloud-native approaches, this work highlights a complementary paradigm: geospatial system design that remains functional when connectivity assumptions fail. The presentation provides a practical and transferable framework for building such systems using FOSS4G technologies. false https://talks.osgeo.org/foss4g-2026/talk/AXE77B/ Conference Management Room3 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 This study develops a GIS-based flood susceptibility model for Nepal’s Sunkoshi Basin integrating OpenStreetMap-derived hydrography within an MCDA framework. AHP-derived weights and sensitivity analysis evaluate model robustness. Results demonstrate the reliability of OSM data for hydrological hazard assessment in data-scarce mountainous environments. foss4g-2026-4932-openstreetmap-driven-flood-susceptibility-modeling-using-gis-with-multi-criteria-decision-analysis /media/foss4g-2026/submissions/J9NSUC/image_fRCy8W4.jpeg Prativa Thapa en This study develops a GIS-based spatial flood susceptibility model for the Sunkoshi River Basin, central Nepal, explicitly integrating OpenStreetMap (OSM) hydrographic data within a Multi-Criteria Decision Analysis (MCDA) framework. The objective is to evaluate the applicability and reliability of community-generated OSM river network data in hydrological hazard modeling in complex mountainous terrain. OSM river and stream geometries were extracted and preprocessed to generate two hydrologically significant spatial indicators: (1) drainage density and (2) Euclidean distance from river. Drainage density was computed by normalizing total stream length within defined spatial units, while distance-from-river rasters were derived to quantify flood exposure gradients relative to mapped channels. These OSM-derived layers were integrated with topographic (SRTM DEM), morphometric (slope, aspect, TWI), climatic (PERSIANN-CCS rainfall), vegetation (NDVI), land use/land cover (ICIMOD), and soil datasets. A total of nine flood-conditioning factors were standardized and reclassified prior to weighting. Criteria weights were determined using the Analytic Hierarchy Process (AHP), employing a structured pairwise comparison matrix and consistency ratio validation to ensure logical coherence in weight assignment. Weighted linear combination (WLC) was then applied to produce a continuous flood susceptibility index, subsequently classified into four discrete risk categories. To assess model robustness and the relative influence of OSM-derived hydrographic parameters, sensitivity analysis was conducted under multiple alternative weighting scenarios. Comparative analysis of susceptibility outputs quantified the stability of high-risk delineation zones under perturbations in drainage density and distance-from-river weights. The results demonstrate that OSM hydrographic data can effectively support derivation of hydrologically meaningful spatial predictors for flood susceptibility modeling. In data-scarce regions where authoritative hydrographic datasets are limited or outdated, OSM provides a viable open-data alternative for spatial hazard assessment. This study contributes methodological evidence supporting the integration of volunteered geographic information (VGI) into structured, decision-analytic flood risk modeling frameworks false https://talks.osgeo.org/foss4g-2026/talk/J9NSUC/ Conference Management Room3 General session talk 2026-09-03T15:00:00+09:00 15:00 00:30 Nominatim struggles with CJK place name search. Searching for "広島平和記念資料館" (Hiroshima Peace Memorial Museum) returns no results. Its token-based search requires exact token matches and depends on complete alternative name data - which is often missing in OSM. I demonstrate how PostgreSQL extensions can fix this with minimal code changes. foss4g-2026-5264-why-nominatim-can-t-find-hiroshima-peace-memorial-museum-and-how-to-fix-it abetomo en Nominatim is the default geocoder for OpenStreetMap and works well for many languages. However, searching for place names in CJK (Chinese, Japanese, Korean) languages often produces poor results. ### The Problem Nominatim's token-based search has several limitations: * Search queries must exactly match registered tokens. * Alternative name fields (alt_name, short_name) could help, but they are often left empty. * In rare cases, ICU transliteration produces incorrect values for the original name. When any of these issues apply, searching for "広島平和記念資料館" (Hiroshima Peace Memorial Museum) returns no results - even though two matching features exist. ### The Solution I added full-text search capability to Nominatim using a PostgreSQL extension. With this extension and a small set of code changes, searching for "広島平和記念資料館" now correctly returns the two expected results with fast performance. Specifically, I achieved full-text search that works alongside Nominatim's existing token-based search with the following changes: * Adding one text column to the existing search table * Creating an index on that column * Modifying three source files (a few lines each) (This is a proof-of-concept patch. Further work would be needed for upstream inclusion in Nominatim.) The advantage of this approach is that search accuracy improves using existing data alone - without requiring additional alt_name or short_name entries in OSM. ### Why PostgreSQL Extensions Nominatim's strength is that it runs on PostgreSQL alone, without requiring additional services. My approach preserves this by using a PostgreSQL extension rather than adding an external search engine. This keeps the deployment simple and the architecture unchanged. PostgreSQL offers many extensions that could improve geocoding - not just for CJK, but for fuzzy matching, typo tolerance, and more. I suggest that Nominatim could benefit from a plugin mechanism that allows communities to plug in the extensions best suited for their language and use case. ### Broader Impact This is not a Japan-only issue. My analysis confirms that CJK search limitations and sparse alternative name data are common across Japanese, Chinese, and Korean OSM data. The approach presented is applicable to all three languages. I also discuss the complementary need for better OSM data. Technical improvements and community data contributions go hand in hand - improving search technology helps even when data is incomplete, and enriching data makes all search approaches work better. ### Open Source Projects * Nominatim ( https://nominatim.org/ ) * PostgreSQL ( https://www.postgresql.org/ ) * PGroonga ( https://pgroonga.github.io/ ) - used as the PostgreSQL extension in this proof of concept * OpenStreetMap ( https://www.openstreetmap.org ) false https://talks.osgeo.org/foss4g-2026/talk/PETVSS/ Conference Management Room4 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 The Earth System Grid Federation (ESGF) is a global partnership supporting the distribution, archive, and discovery of climate data. Its new architecture introduces STAC catalogues, Kerchunk‑enabled access, and an event‑driven search system synchronising two discovery nodes, improving consistency, reliability, and interoperability across climate and Earth observation communities. foss4g-2026-5146-esgf-next-generation /media/foss4g-2026/submissions/BDZP9T/esgf-logo_4yEcHiV.png Rhys Evans en The Earth System Grid Federation (ESGF) is the international partnership responsible for the distribution, archive, and discovery of both the Coupled Model Intercomparison Project (CMIP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX). In operation since 2009, it was the first decentralised climate data repository of its kind, storing and serving many petabytes of data across tens of global and region data centre partners. Over the last five years, a full rearchitect of the system has been conducted, introducing a cloud-ready deployment architecture and a new system for distributed search, fundamental to ESGF’s federated model for data access. This has involved innovations, translating successful experience with the STAC (Spatio-Temporal Asset Catalogue) specification from the EO world and developing a profile for its use with global climate projections data. Providing a STAC interface to ESGF archives has allowed us to explore alternate access methods for cloud-accessible analysis-ready data formats through the use of tools such as Kerchunk, a lightweight non-conversion approach for referencing existing data, which works with open-source python packages like fsspec and Xarray. Use of STAC also provides the potential for greater integration between EO and climate modelling domains essential for the validation of model outputs. ESGF has traditionally used a distributed model for search services which though powerful has led to challenges around consistency of search content. Over the last twelve months, in preparation for CMIP7, a further fundamental innovation has been made in the architecture to address these issues. The new system adopts a centralised model, with two search nodes, one in the US and one in Europe each hosted on public cloud. These two nodes are synchronised together using a new event-driven architecture. This approach, driven by a shared messaging framework between the nodes, ensures eventual-consistency across the nodes, to reduce or eliminate errors caused by individual node down time and simplify processes such as the replication and retraction of data from the archives distributed at sites across the federation. The move to a message based, event driven architecture has been integrated with STAC records and services. In ESGF-NG publication and updates of data are shared between nodes through events in a Kafka stream in the form of STAC API calls, ensuring a consistent, publicly documented archive distributed across many nodes. The ESGF team have contributed several changes to the STAC project to facilitate this change. Looking forward, we see potential in this new event driven architecture for search systems as a means to integrate across federations - in the European context this could include the ESA Climate Change Initiative open data portal, work with the Copernicus Climate Data Store and DestinE. false https://talks.osgeo.org/foss4g-2026/talk/BDZP9T/ Conference Management Room4 General session talk 2026-09-03T11:30:00+09:00 11:30 00:30 This talk introduces recent development features in Reearth CMS, including an API-first architecture, an interactive API playground for exploring endpoints, and flexible import/export pipelines designed to improve developer experience and simplify integration with external systems. foss4g-2026-5179-extending-reearth-cms-with-api-first-design-and-developer-friendly-data-workflows Maher Alhamoui en As digital platforms increasingly rely on interconnected systems, content management tools must evolve beyond traditional publishing workflows. Reearth CMS is being developed to support these modern requirements by focusing on interoperability, extensibility, and developer accessibility. This session presents three recent development features designed to improve how developers and organizations interact with the platform. First, Reearth CMS adopts an API-first architecture, ensuring that content and structured information are accessible through well-defined APIs. This approach allows external applications and services to interact with the platform in a consistent and scalable way. Second, the platform introduces an API Playground, an interactive environment where developers can explore endpoints, test queries, and better understand the structure of available data. This tool lowers the barrier for integration and improves the overall developer experience. Finally, we will present import and export pipelines that simplify how data moves between systems. These pipelines enable structured data exchange and help integrate the CMS into broader data workflows. Together, these features demonstrate how Reearth CMS is evolving into a more open and extensible platform that supports modern development practices while remaining easy to integrate with other tools and systems. false https://talks.osgeo.org/foss4g-2026/talk/CTC9UQ/ Conference Management Room4 General session talk 2026-09-03T13:00:00+09:00 13:00 00:30 Raster, vector, point cloud: every geospatial format solves the same core problems, including linearization, chunking, compression, and metadata. Let’s build a format from scratch to see these concerns in practice. foss4g-2026-5116-anatomy-of-a-file Jarrett Keifer en Every geospatial format exists to solve the same fundamental problem: how do we persist spatial (and sometimes temporal) data so it can be efficiently stored, shared, and queried? These concerns apply to all data types: raster, vector, point cloud, etc. Yet as a community, we often treat formats as islands, each with its own ecosystem, tooling, and mental model. In this talk, we start from data. We have geospatial data and we need to persist it. How do we linearize multidimensional data into bytes? How do we chunk it for partial reads? Are we going to support writing in parallel? What encoding and compression strategies will we use and what tradeoffs do they carry? As we work through these questions, the need for metadata arises — coordinate systems, data types, spatial indices, encoding indicators— because without it, the data is useless. Piece by piece, we build up the anatomy of a file. We then map what we've built to real formats. The same structures appear in each: chunking strategies, index structures, encoding schemes, metadata organization. The specific tradeoffs differ, but the underlying anatomy is consistent across raster, vector, and beyond. This consistency raises a question worth confronting: if formats share this much of their architecture, why does our tooling treat them as fundamentally different things? Perhaps we should be recognizing our ecosystems have built walls around format tradeoffs that the underlying anatomy doesn't justify. false https://talks.osgeo.org/foss4g-2026/talk/CYWZVB/ Conference Management Room4 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 What if data formats didn't need their own libraries? The cylf ecosystem leverages WebAssembly to make codecs, format drivers, and storage drivers modular, sandboxed, and fetchable on-demand. Each can be developed independently and on its own lifecycle. We'll demo a working proof of concept. foss4g-2026-5533-do-formats-exist-towards-a-unified-foundation-for-data-tooling Jarrett Keifer en Zarr, Parquet, COG: we treat different formats as distinct, each requiring its own libraries, its own codecs, its own tooling. But look closer: each one chunks data, encodes those chunks, linearizes them into a stream, and attaches metadata that explains how to read it back. The differences are real, but they are surface-level. The underlying structure is shared. If that's true, why do we build tooling as if these formats are fundamentally different things? The cylf ecosystem takes this question seriously. It is a new open-source effort to build format-agnostic data tooling on a shared foundation. In cylf, codecs can be sandboxed WASM modules that can be specified declaratively and resolved on demand, whether by registry identifier or by URI, fetched and executed the same way a browser fetches and runs code from the web. Data producers declare which codecs their data requires; data consumers resolve and run them automatically, with no environment setup required and no out-of-band coordination. This is more than packaging convenience. Codecs can be registered with metadata describing their capabilities and target architectures. Clients run them chained together into pipelines with zero-copy memory sharing. The same model can extend upward to format drivers and storage drivers, enabling a modular and decoupled architecture where support for formats, access protocols, and codecs can be developed independently, each with its own lifecycle. The runtime handles orchestration, memory, and multithreaded execution, so format drivers don't have to. We demonstrate this architecture with COG support and a growing set of WASM codec implementations, with Parquet support in progress. The project includes a Python library as a proof of concept. We are seeking collaborators, feedback, and engagement from the standards and data formats communities to help shape what we believe could be a unified foundation for the next generation of data tooling. false https://talks.osgeo.org/foss4g-2026/talk/QEN3RF/ Conference Management Room4 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 STAC has evolved into core geospatial infrastructure. This talk covers its current state, real-world adoption, key challenges, and how it fits into emerging architectures for scalable discovery and analysis. foss4g-2026-5595-state-of-stac-from-specification-to-infrastructure Matthew Hanson en The SpatioTemporal Asset Catalog (STAC) has evolved from a community-driven specification into widely adopted infrastructure for geospatial data. With its recognition as an OGC Community Standard and broad use across government, commercial, and open data platforms, STAC is now a foundational part of modern Earth observation systems. This talk provides a current “State of STAC,” focusing on where the ecosystem stands today in real-world use. We’ll cover the maturity of the core specification and API, patterns that have enabled widespread adoption, and the growing role of extensions in supporting new data types and use cases. We’ll also examine areas where the ecosystem is still evolving, including interoperability with other OGC APIs, managing extension complexity, and scaling catalogs and APIs for increasingly large datasets. Finally, we’ll look ahead to how STAC fits into emerging systems. While STAC remains critical for data discovery and interoperability, many workflows are evolving toward layered architectures where STAC feeds downstream indexing and analysis systems. This talk offers a grounded view of STAC as it exists today—what is stable, what is still changing, and what comes next as the ecosystem continues to grow. false https://talks.osgeo.org/foss4g-2026/talk/DWERNM/ Conference Management Room4 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 Eliminate idle costs with a scale-to-zero, cloud-native GIS architecture. This session explores high-performance analytics using DuckDB and serverless mapping with PMTiles to remove dedicated servers. Learn to build sustainable, fast GIS applications across any cloud provider while paying only for active usage. foss4g-2026-5260-building-modern-gis-applications-on-cloud-native-infrastructure Siriwat Suttipanyo en Why should we settle for exorbitant server costs while our systems sit idle, doing nothing yet still draining the budget? As user demand fluctuates, the ability to scale seamlessly while maintaining cost-efficiency becomes critical to delivering a premium user experience. In this session, I will unveil the architecture of a Modern GIS Application specifically designed to solve these challenges. Built on a Cloud Native foundation on Google Cloud Platform (GCP), this system guarantees a true Scale-to-Zero state, where billable container time drops to absolute zero during inactivity. Leveraging Next.js 16 on Cloud Run, we maintain a rapid Cold Start of 1.5s–2.5s, ensuring a seamless transition from idle to active. We will dive deep into a dual-engine strategy that redefines serverless performance. First, we explore an analytics engine powered by DuckDB and Python that queries Hive-partitioned Parquet files directly on GCS, achieving a sub-second average response time of 959ms for complex aggregations of Ecoplant forecasting data. Second, we showcase a serverless mapping layer using MapLibre GL JS and PMTiles, which eliminates dedicated tile servers by fetching data via HTTP Byte-Range requests. The conclusion of this session goes beyond mere cost reduction; it demonstrates that high-performance GIS doesn't require a massive price tag. Attendees will discover how to embrace Cloud Native principles to push GIS applications beyond traditional boundaries and into a more sustainable, scalable future. false https://talks.osgeo.org/foss4g-2026/talk/E8R8SY/ Conference Management Room4 General session talk 2026-09-03T15:00:00+09:00 15:00 00:30 National-scale geospatial analysis using conventional geospatial RDBMS often faces trade-offs between spatial resolution and computational cost. It makes extracting insights from geospatial big data time-consuming or expensive. This proposal demonstrates two national-scale WebGIS applications built with open source pipelines to mitigate them. foss4g-2026-4938-building-national-scale-webgis-using-open-source-geospatial-pipelines /media/foss4g-2026/submissions/R8DEEX/submit_image_3TZIexx.png Takashi Nojima en # Overview This proposal introduces two representative Japanese national-scale WebGIS applications using open source geospatial pipelines. 1. Land use spatial aggregation 2. Railway network reconstruction using map-matching The pipelines shift heavy computation from runtime queries and uniquely partition raw open data into spatial index cells, enabling national-scale geospatial access and visualization on lightweight serverless infrastructure. The goal is to provide scalable geospatial data access through precomputed spatial lookup. # Architecture The WebGIS applications consist of a backend for parallel and distributed processing of geospatial data, a frontend, and APIs that deliver the preprocessed data to the frontend. The backend implements multiple pipelines using open source libraries, including Python, PySpark and Apache Sedona for geospatial distributed processing, H3 and A5 for geospatial indexing, and NetworkX for graph analysis. Raw geospatial data is indexed, aggregated, mapped to graph structures, and transformed into datasets optimized for seamless access. The frontend uses open source JavaScript visualization libraries including React and deck.gl that dynamically load, render, and visualize the precomputed spatial datasets. By accessing indexed data through APIs from the frontend, this architecture enables seamless national-scale visualization without client-side spatial processing and efficient serverless deployment. # Land Use Aggregation The land use WebGIS pipelines address aggregating millions of land use polygons using H3 and A5 geospatial indexing systems. The following hybrid approach leverages these indices. 1. Land use polygons are covered by H3 cells. 2. The corresponding A5 resolution that matches the H3 cell area is calculated. 3. Each H3 hexagon cell is transformed into A5 pentagons using seven points: one centroid point and six boundary points. 4. The land use is re-aggregated for each A5 cell. 5. The aggregated land use is stored in a key–value store, where the key corresponds to the A5 cell and the value corresponds to the aggregated land use attributes. This key-value indexing strategy not only transforms complex spatial joins into simple lookups, enabling seamless visualization of nationwide land use distributions without server-side spatial processing, but also ensures area-based analytical accuracy. # Railway Map Matching The railway WebGIS pipelines perform graph-based map matching to reconstruct the railway network from stations and railway line segments in two different datasets. The adjacency graph is constructed where stations are represented as nodes and railway line segments as edges. Instead of distance-based adjacency, STRtree-based spatial search extracts adjacency candidates, and geometries in the same H3 cell are defined as adjacent. A shortest-path algorithm is applied to this graph to determine the sequence of track segments from the starting station to the terminal station for each line. Using graph-based spatial search and H3 indexing for adjacency can reduce computational complexity and accuracy loss caused by coordinate-distance approaches. # Conclusion This proposal demonstrates how geospatial pipelines transform raw geospatial data into indexed formats, how the indexed spatial datasets are accessed via APIs on serverless infrastructure, and how the national WebGIS visualizes them seamlessly. These pipelines are built with open source technologies. false https://talks.osgeo.org/foss4g-2026/talk/R8DEEX/ Conference Management Room5 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 We share GISTDA’s Dragonfly project lessons on national-scale agricultural monitoring. We optimize COG performance to reduce TTFB and ensure data reliability using Great Expectations automated quality gates. Attendees gain actionable DataOps insights for building resilient, high-performance cloud-native ecosystems from real-world operational experience. foss4g-2026-5255-from-chaos-to-confidence-practical-dataops-with-cog-optimization-and-great-expectations-for-national-scale-agriculture PEERANAT PRASONGSUK en Operationalizing satellite imagery for national-scale agricultural monitoring within the GISTDA Dragonfly project taught us two critical lessons: first, "standard" Cloud Optimized GeoTIFF (COG) configurations often fail under the pressure of low-latency production requirements, and second, speed is useless if the data is incorrect. This paper shares our practical journey in building a raster delivery pipeline that prioritizes both extreme performance and data reliability. The first part of our story focuses on overcoming performance bottlenecks encountered in the field. We discuss how sub-optimal COG parameters—such as misaligned internal tiling or inefficient compression—directly degraded the user experience. By implementing advanced tuning techniques, including strict byte-alignment and optimized ZSTD compression, we achieved reduction in Time to First Byte (TTFB), enabling seamless data access via OGC Environmental Data Retrieval (EDR) APIs. The second part addresses our most challenging hurdle: "silent failures." From inconsistent spatial resolutions to corrupted metadata, these issues often bypassed traditional checks. To solve this, we integrated Great Expectations (GX) as a mission-critical automated quality gate. We share our experience in designing geospatial-specific "Expectations" to ensure that every 10m pixel and internal structure adheres to rigorous production standards before being served to end-users. Attendees will gain actionable DataOps insights derived from real-world operational experience, moving beyond basic data conversion to building a resilient, high-performance cloud-native ecosystem. Key Takeaways for Attendees: - Deep Understanding: Grasp the critical impact of byte-alignment and internal tiling on cloud storage I/O performance. - Data Reliability: Learn how to design and enforce "Geospatial Data Contracts" to eliminate silent failures. - DataOps Roadmap: A strategic guide for transitioning from manual data processing to automated, continuous cloud-native delivery. false https://talks.osgeo.org/foss4g-2026/talk/JR3EUK/ Conference Management Room5 General session talk 2026-09-03T11:30:00+09:00 11:30 00:30 The Copernicus Data Space Ecosystem gives every user free access to Europe's Earth observation archive — petabytes of satellite imagery, derived products, and land monitoring datasets, all through cloud-native APIs. We showcase how sovereign EU infrastructure and open standards turn this archive into a foundation for geospatial AI. foss4g-2026-5144-cdse-europe-s-sovereign-infrastructure-for-open-earth-observation /media/foss4g-2026/submissions/MRSKCK/logo-cdse-new-blue_ZErcotq.svg Klemen Lovenjak en Europe has quietly built a comprehensive open geospatial data platform. The Copernicus Data Space Ecosystem (CDSE), operated by a European consortium (T-Systems, CloudFerro, Sinergise, VITO, DLR, ACRI-st, RHEA Group) under ESA guidance, provides free access to all Sentinel satellite archives, Copernicus Contributing Missions, and an extensive portfolio of derived products — land cover, soil moisture, snow properties, water quality, surface temperature, and more. Most of these datasets are global, not just European — systematic observations covering every continent, across over 100 collections. This is not just a data catalogue. CDSE is a cloud-native processing platform — through standardized APIs (Sentinel Hub, openEO, STAC, OGC), users get analysis-ready results directly from the API or browser, with custom algorithms called evalscripts running server-side on the full archive. The infrastructure runs on European soil, operated by European companies — a concrete example of data sovereignty that goes beyond policy documents. In the context of sovereign AI, authoritative geospatial data is a strategic asset. Nations investing in AI capabilities need not just compute infrastructure but trusted, authoritative data distributed through open, standardized APIs. CDSE delivers exactly this: the foundation layer for building geospatial AI applications on open, sovereign infrastructure. The talk covers: - CDSE architecture and the European consortium operating it - The free access tier: what's available and where the boundaries are - API-first design: why cloud-native processing changes everything - The sovereign AI angle: authoritative data as national infrastructure - Practical example: AI-assisted satellite data analysis showcase Whether you're a researcher, developer, or organization building geospatial applications, CDSE provides the open platform to do it on — backed by the full weight of the EU's Copernicus programme. false https://talks.osgeo.org/foss4g-2026/talk/MRSKCK/ Conference Management Room5 General session talk 2026-09-03T13:00:00+09:00 13:00 00:30 An idle-aware geospatial processing scheduler that auto-scales workers based on queued workloads. The system runs spatial processing tasks opportunistically using available resources and supports cloud-native deployment for scalable geospatial data processing pipelines. foss4g-2026-5265-idle-aware-geo-processing-scheduler-auto-scaling-workers-for-cloud-native-geospatial-processing Natpakal Maneerat en Large-scale geospatial processing tasks such as spatial analysis, spatial indexing, and vector processing often require significant computing resources. In many production environments, however, computing resources remain underutilized during periods of low activity. This talk introduces an Idle-Aware Geo-Processing Scheduler, a worker-based processing system designed to run geospatial workloads opportunistically by utilizing idle compute resources. The system operates as a distributed worker processing engine built around a task queue architecture. When new geospatial jobs enter the queue, the scheduler automatically scales workers based on workload demand. For example, if multiple jobs are submitted simultaneously, multiple workers can be spawned to process them in parallel. Conversely, when the workload decreases or the queue becomes empty, workers automatically scale down, allowing the system to return to minimal resource usage. This mechanism enables geospatial processing tasks to run as background pipelines without interfering with primary system workloads. The architecture follows cloud-native principles, allowing the system to be deployed on cloud environments where compute resources can scale elastically. By combining auto-scaling workers, task queues, and open-source geospatial tools, spatial processing pipelines can dynamically expand during high workloads and shrink during idle periods. This talk will cover: -The design of an idle-aware scheduler for geospatial workloads -Architecture of a worker-based geospatial processing engine -Applying auto-scaling and cloud-native deployment to spatial processing pipelines -Task queue orchestration for distributed geospatial jobs -Integrating open-source geospatial tools into scalable processing workflows This approach enables geospatial teams to build flexible spatial processing pipelines that adapt to workload demands while efficiently utilizing available computing resources. false https://talks.osgeo.org/foss4g-2026/talk/N87UAA/ Conference Management Room5 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 "Machi-Space" empowers diverse stakeholders to perform environmental simulations and share data effortlessly, lowering barriers to participation in urban development and providing a collaborative platform for evidence-based decision-making. foss4g-2026-5171-democratizing-urban-development-the-potential-of-the-urban-digital-twin-platform-machi-space Hirofumi HayashiKunihiko Hirosawa en To democratize urban development, it is essential for citizens to engage with a sense of ownership rather than deferring solely to expert judgment. We developed "Machi-Space", a digital twin platform as a cloud-based service designed to enable non-experts to conduct environmental numerical simulations and effortlessly share the results among stakeholders. By leveraging Project PLATEAU's 3D city models, this platform ensures high data reliability and accuracy. With features such as the integration of IFC files for planned buildings, “Machi-Space” is expected to play an increasingly vital role in urban planning workflows. Consequently, the user base continues to grow. false https://talks.osgeo.org/foss4g-2026/talk/SVJVFS/ Conference Management Room5 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 pygeoapi is one of the most popular open source solutions for deploying OGC compliant geospatial APIs. This session will explain best practices for deploying pygeoapi and scaling it within a cloud native, containerized, and horizontally scalable deployment. foss4g-2026-5227-running-pygeoapi-cloud-native-at-scale Benjamin WebbColton Loftus en pygeoapi provides an off the shelf solution for serving OGC APIs like Features, Maps, Tiles, or Environmental Data Retrieval from your organization’s existing data including GeoPackage, PostGIS, Zarr, GeoParquet, shapefiles, and many more. Pygeoapi can be deployed on its own or be integrated into existing API servers written with Flask, Starlette or Django. As such, pygeoapi offers many ways to tweak its performance depending on use case. We will begin the talk with a general introduction to pygeoapi and how it can be used to simplify OGC API deployment in your organization. We will then go over the high level internals of pygeoapi and best practices for scaling your deployment. Topics will include horizontal scaling in containerized environments, caching strategies, connection pooling, and best practices when creating your own custom provider extensions to pygeoapi. false https://talks.osgeo.org/foss4g-2026/talk/R7GEQB/ Conference Management Room5 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 Learn to overcome geospatial scaling bottlenecks by moving from monolithic designs to a composable architecture. We introduce Meros, an open-source feature data service, demonstrating how decoupling APIs—like OGC API - Features—from databases creates highly flexible, resilient, and easily deployable services. foss4g-2026-5244-rethinking-feature-data-services-a-composable-architecture-for-geospatial-apis Siriya Saenkhom-or en As geospatial applications increasingly move into cloud environments, the traditional monolithic approach to serving feature data is starting to show its limitations. Teams often encounter familiar challenges such as scaling bottlenecks, infrastructure lock-in, and increasing friction when integrating geospatial services with modern distributed databases and cloud platforms. This talk explores a practical approach to rethinking how feature data services can be designed and operated. By adopting a composable architecture, we decouple the API layer from the underlying database storage, allowing each component to evolve and scale independently. This architectural shift enables geospatial APIs—such as OGC API - Features—to become significantly more flexible, deployable as lightweight services, and adaptable to a variety of modern data stores. To demonstrate this approach, we introduce **Meros**, an open-source feature data service developed by our team. Meros implements a modular architecture that separates the API layer, feature service logic, and database adapters. This allows the system to seamlessly integrate with different databases while remaining simple to deploy and operate. In this session, we will share the journey of designing and building Meros from the ground up. We will explore the key architectural decisions behind the system, discuss the operational benefits of a decoupled design, and present practical considerations for deploying and scaling its components. By the end of the talk, developers and architects will walk away with a practical case study—giving them a tangible starting point to build their own resilient, adaptable feature data services. false https://talks.osgeo.org/foss4g-2026/talk/SZ7AKX/ Conference Management Room5 General session talk 2026-09-03T15:00:00+09:00 15:00 00:30 This presentation introduces an open-source Geospatial Observation Stack using OpenTelemetry, Prometheus, Grafana, and Loki. We demonstrate how end-to-end tracing and SLOs reduce MTTD and MTTR in cloud-native architectures, enabling developers to pinpoint bottlenecks across complex spatial data pipelines and move toward predictive maintenance. foss4g-2026-5361-elevating-geospatial-cloud-native-platforms-end-to-end-observability-with-geospatial-observation-stack Worrathep Somboonrungrod en As geospatial platforms transition toward cloud-native microservices, management complexity has surged across API data services, processing engines, and multi-format storage systems. Traditional monitoring is no longer sufficient for these distributed environments. To ensure high availability and rapid troubleshooting, a shift toward deep Observability is essential. This presentation introduces an open-source Geospatial Observation Stack—specifically designed for cloud-native geospatial architectures using OpenTelemetry, Prometheus, Grafana, and Loki. We demonstrate how to achieve end-to-end tracing and correlation, enabling administrators and developers to pinpoint bottlenecks across the entire spatial data pipeline. By implementing Service-Level Objectives (SLOs) and proactive alerting policies, organizations can move from reactive fixes to predictive maintenance. Our results show that this integrated observability approach significantly reduces Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR), ensuring robust, high-performance geospatial services in a cloud-native world. false https://talks.osgeo.org/foss4g-2026/talk/TFUZ8K/ Conference Management Room6 General session talk 2026-09-03T11:00:00+09:00 11:00 00:30 Geospatial analytics and Geospatial queries can be a challenge for Geospatial backend it's can be slow and or might clause a problems. This is architecture design use case on forest and land monitoring platform (LANDX) by GISTDA Thailand. how i deal with large geospatial data and big geospatial query. foss4g-2026-5382-architecture-design-and-api-development-for-geospatial-backend-in-large-scale-forest-and-land-monitoring-platforms Thana Wannasang en Managing and processing large-scale spatial data is one of the major challenges in developing Geospatial Analytics API particularly on the geospatial backend, which must handle complex, resource-intensive spatial queries. Large concurrent spatial queries can degrade system performance, causing slow responses or even service outages. This session presents a case study of the architecture for the LANDX website, developed by the Geo-Informatics and Space Technology Development Agency (GISTDA). LANDX is a platform for monitoring and analyzing land-related spatial data, including LandTrendr-based change detection, land-use monitoring, disaster monitoring, and forest-change detection. The system also supports analyses to help verify compliance with regulations such as the EU Deforestation Regulation, which require integration of multiple data sources such as crop boundary data, land-use maps, satellite imagery, and other large geospatial datasets. Querying across these diverse sources simultaneously creates significant design challenges in terms of performance, resource usage, and system stability. To address these challenges, the system stores data in multiple formats according to usage patterns. A key approach we use is storing geometry data as geometry types inside DuckDB files, which enables faster spatial queries, lower resource consumption, and reduced network latency. Large-volume datasets are also stored as Apache Parquet files on S3 Storage, with Hive-style partitioning to reduce the amount of data read per query. With this architectural approach and the chosen technology stack, the system reduces query costs, increases flexibility for geospatial data handling, and can efficiently support queries over millions of records. This architecture has been deployed in the LANDX project to support land-change monitoring, land-use change detection, disaster monitoring, and compliance checks for environmental supply-chain requirements. The session concludes with benchmark results from load testing that simulates large numbers of concurrent users, demonstrating query latency, resource consumption, and the overall performance characteristics of the LANDX architecture. false https://talks.osgeo.org/foss4g-2026/talk/UDKZY3/ Conference Management Room6 General session talk 2026-09-03T11:30:00+09:00 11:30 00:30 OGC API and modern standards have made spatial data integration seamless — like a "super expressway" breaking traditional barriers. Yet this ease lures organizations into a "Convenience Trap," where unchecked connections breed API Sprawl, traffic bottlenecks, and untraceable security vulnerabilities. foss4g-2026-5252-the-paradox-of-convenience-decoding-the-hidden-risks-of-ogc-api-and-architecting-for-sustainable-control Nuttawoot Rungsirirak en Technologies like the OGC API and modern connectivity standards have revolutionized data exchange—including the handling of heavy spatial data payloads—making integration seamless and highly accessible. It is akin to opening a "super expressway" that completely shatters traditional communication barriers. However, this effortless integration often lures organizations into the "Convenience Trap." When systems are allowed to connect freely without centralized oversight, hidden risks begin to accumulate under the surface. These include tangled data pathways (API Sprawl), severe traffic bottlenecks when simultaneous requests for map services overwhelm backend spatial databases, and decentralized security vulnerabilities that are nearly impossible to trace. This session decodes the hidden risks behind this newfound convenience. It explores why the ultimate solution is not to restrict connectivity, but to elevate the IT infrastructure through an "Intelligent Command Center" (API Gateway Architecture). By acting as a traffic controller and security checkpoint, this architecture restores holistic visibility, manages heavy data flows (Rate Limiting), and empowers organizations to scale their enterprise and geospatial innovations securely and sustainably. false https://talks.osgeo.org/foss4g-2026/talk/UDRTGC/ Conference Management Room6 General session talk 2026-09-03T13:00:00+09:00 13:00 00:30 Watershed delineation is essential for flood hazard mapping to minimize disaster impacts. Traditional approaches require time-consuming DEM preparation and preprocessing. This presentation introduces a serverless function that extracts watersheds from nationwide flow direction data hosted on cloud storage, comparing performance between COG and Zarr formats. foss4g-2026-5432-serverless-watershed-extraction-benchmarking-zarr-vs-cog-for-large-scale-flow-direction-data /media/foss4g-2026/submissions/UUF3NZ/%E5%9B%B31_YYXgbO0.png Shinsuke Nakamori en ### Background Watershed delineation is essential for flood hazard mapping and disaster risk assessment. Traditional workflows require downloading large DEM datasets, performing preprocessing, and running computationally intensive algorithms—a process taking hours or days. This creates barriers for rapid assessment and limits accessibility for practitioners without specialized GIS infrastructure. ### Approach This presentation introduces a serverless watershed extraction system using AWS Lambda. Users click a point on a web map, triggering upstream cell tracing on cloud-hosted J-FlwDir (Japan's nationwide flow direction dataset). The system generates GeoTIFF and transparent PNG outputs, returning presigned S3 URLs within seconds. ### Performance Comparison This presentation compares COG and Zarr performance using real-world test cases on AWS Lambda. Initial benchmarks on large basins (millions of cells) demonstrate both formats process watersheds interactively, with measurements of total time and phase breakdown (extraction, GeoTIFF generation, PNG rendering, S3 uploads). Analysis identifies bottlenecks and discusses optimization opportunities such as chunk size configuration. false https://talks.osgeo.org/foss4g-2026/talk/UUF3NZ/ Conference Management Room6 General session talk 2026-09-03T13:30:00+09:00 13:30 00:30 The greatest gap in geospatial technology is not data-to-code — it is complexity-to-usability. Through two real-world platforms, this session explores how to turn powerful open-source spatial tools into decisions anyone can act on — by giving users less to think about, not more data to interpret. foss4g-2026-5359-less-to-think-about-bridging-the-usability-gap-in-geospatial-platforms Siraya en We have better satellite imagery than ever before. We have more open-source tools than ever before. We have more spatial data than ever before. But if the only people who can act on it are the ones who already understand it — then powerful tools remain just that: powerful, but out of reach. This is the defining challenge in delivering geospatial technology: a disconnect between technical power and user reality. The greatest gap in technology is not data-to-code — it is complexity-to-usability. This presentation examines the Project Manager's role as a bridge in the FOSS ecosystem, sharing lessons from delivering a "Spatial Agriculture Platform" and an "Advanced Spatiotemporal Analytics Platform" — and how to translate complex open-source processes into a user-centric experience that serves actual business logic. The core of this session is Simplifying — reducing analytical complexity so that powerful spatial data becomes a decision that anyone can act on: Spatial Agriculture Platform: Farmers managing large plots faced overwhelming fragmented information — soil, weather, crop health — with no structured way to act on it. The platform's role was not to give farmers more data, but to give them less to think about — consolidating complex spatial inputs into a single mobile interface where crop health, fertilizer guidance, and disease risks are actionable without ever interpreting the data behind it. Advanced Spatiotemporal Analytics Platform: Verifying whether a supply chain is deforestation-free once required specialists. The platform collapsed this complexity into two powerful capabilities: the ability to explore decades of forest change at any location instantly, and the ability to verify EUDR compliance with nothing more than a plot boundary. The science stays hidden. The answer stays clear. In both cases, the richness of the FOSS ecosystem made it possible — but only when complexity was deliberately kept out of the user's way. This session offers a practical framework on how a PM can bridge the gap between technical complexity and user-centric outcomes, and what it truly takes to let the power of open source disappear into simplicity. false https://talks.osgeo.org/foss4g-2026/talk/VQHSHX/ Conference Management Room6 General session talk 2026-09-03T14:00:00+09:00 14:00 00:30 How JR West Democratized Geospatial Data to Tackle the Technical Succession Crisis foss4g-2026-4940-digital-transformation-in-railway-infrastructure /media/foss4g-2026/submissions/X83SEA/DIGIMAP_e4bGLuB.png Nguyen Van ThienHirofumi HayashiTatsuya NakaYayoi OdaTomohiro SHIMIZU en *The Crisis Facing a critical labor shortage and the retirement of veteran staff, JR West needed to preserve 'tribal knowledge' regarding their vast infrastructure and maintain safety with a shrinking workforce. *The Strategy Moving away from specialist-only GIS tools, the company developed the 'Digital Rail Map'—a mobile-first, search-centric platform accessible to all employees, designed to mimic consumer map apps. *The Result The tool achieved mass adoption with over 10,000 Monthly Active Users, transforming how field teams navigate, coordinate, and manage safety in real-time. false https://talks.osgeo.org/foss4g-2026/talk/X83SEA/ Conference Management Room6 General session talk 2026-09-03T14:30:00+09:00 14:30 00:30 Digital Earth Canada (DEC) is a national Earth Observation platform that brings together satellite data, cloud computing, and AI-powered analytics powered by open source technologies. DEC gives researchers, developers, policy makers, and the public a single, open gateway to understand and monitor our planet. foss4g-2026-5554-digital-earth-canada-unlocking-the-power-of-earth-observation-across-canada-and-abroad Christopher Rampersad en From tracking Arctic ice loss to monitoring crop health and detecting urban flood risk, satellite-based Earth Observation (EO) data holds enormous potential to inform decisions that affect all Canadians. Yet for most users — whether a government analyst, a university researcher, or a small environmental tech company — accessing, processing, and making sense of that data remains a significant challenge. Digital Earth Canada (DEC) is a Canadian Space Agency-led initiative developed by EarthDaily Analytics built to solve that problem. DEC is a centralized, open, cloud and high-performance computing platform that provides a single point of access to authoritative Canadian and international EO data, paired with powerful tools for analysis, visualization, and application development — including AI and machine learning capabilities. This session will offer a broad overview of DEC's vision, progress, and potential: - What DEC is: a "one-stop-shop" for EO data and analytics, co-designed with users across government, academia, and industry to meet the real needs of Canada's diverse EO community. - Why it matters: comparable platforms in Australia, Europe, Africa, and the United States have already demonstrated how open, shared EO infrastructure accelerates scientific discovery, supports environmental monitoring, and drives downstream economic activity. Canada now has the opportunity to build its own. - What it can do: from near-real-time lake water quality monitoring and wildlife habitat mapping to annual crop tracking and automated disaster response — DEC's use cases illustrate the breadth of applications it can support. - Who it's for: researchers, application developers, service engineers, policy analysts, and general users alike — DEC is designed to be accessible regardless of technical background, with user-friendly interfaces alongside advanced programmatic tools. - The road ahead: DEC is progressing from prototype toward a Minimum Viable Product, with a roadmap that includes expanded data federation, enhanced AI/ML support, and compliance with Government of Canada standards for security and accessibility. DEC is designed to be more than a technical platform - it is national infrastructure for the data-driven era. This session invites anyone curious about the future of Earth Observation to learn how DEC can serve their work and community. false https://talks.osgeo.org/foss4g-2026/talk/XGXT7T/ Conference Management Room6 General session talk 2026-09-03T15:00:00+09:00 15:00 00:30 Geoconnex links hydrologic data across many government agencies in the United States as a knowledge graph, published in accordance with W3C Spatial Data on the Web best practices. This session will provide an overview of Geoconnex and how to access its data using either SPARQL, OGC API Features, or GeoParquet. foss4g-2026-5232-geoconnex-standardizing-water-data-in-the-united-states-through-a-unified-graph-database Benjamin WebbColton Loftus en The United States, like many other countries, has many water data providers at both the federal and local levels. These agencies have diverse APIs with a variety of access patterns, making it difficult for researchers to synthesize insights across agencies. Geoconnex, a software initiative funded by the United States Geological Survey, aims to solve this data access challenge by linking hydrologic features into a knowledge graph. Instead of needing to access dozens of specific agency APIs, a user can quickly find all monitoring locations and datasets associated with a river by running a graph database query or accessing the Geoconnex Explorer web UI. This talk will go over how Geoconnex leverages standards like RDF and OGC API Features, how the graph is created via the Geoconnex crawler and data pipeline, and the various ways you can access data from the graph including SPARQL, OGC API Features, or GeoParquet. Attendees will learn a variety of W3C Spatial Data on the Web best practices that are broadly applicable to other geospatial data integration tasks. false https://talks.osgeo.org/foss4g-2026/talk/ZKUQYS/