0.5 foss4g-europe-2026 2026-06-29 2026-07-03 5 00:05 https://talks.osgeo.org https://talks.osgeo.org/media/foss4g-europe-2026/img/logo-alb-nobackground_1_YJhUXDr.png Europe/Bucharest Auditorium Full session 2026-06-29T09:00:00+03:00 09:00 01:00 Welcome to FOSS4G Europe 2026! foss4g-europe-2026-5884-opening-plenary Marian Neagul en false https://talks.osgeo.org/foss4g-europe-2026/talk/LAPHSL/ https://talks.osgeo.org/foss4g-europe-2026/talk/LAPHSL/feedback/ Auditorium Keynote 2026-06-29T10:00:00+03:00 10:00 01:00 Introduction to the OSGeo Foundation. foss4g-europe-2026-5886-the-open-source-for-geospatial-foundation-keynote Keynote Tom KralidisAngelos TzotsosJeroen TichelerCodrina Ilie en false https://talks.osgeo.org/foss4g-europe-2026/talk/GFFRS3/ https://talks.osgeo.org/foss4g-europe-2026/talk/GFFRS3/feedback/ Auditorium Talk 2026-06-29T11:30:00+03:00 11:30 00:30 We will give a status report on the GDAL software, focusing on recent developments and achievements in the 3.12 and 3.13 GDAL versions released during the last year. The discussed topics will be as various as the scope of GDAL is, covering among other things: - Enhancements in the "gdal" front-end command line interface: * raster functionality: as-features, blend, compare, neighbors, nodata-to-alpha, zonal-stats, etc. * vector functionality: check-coverage, check-geometry, clean-coverage, index, layer-algebra, make-point, partition * dataset management * mixed raster/vector pipelines * nested pipelines - GeoParquet, JSON Features and Geometries (JSON-FG), Zarr support enhancements - New VRT pixel functions - C/C++/Python API for raster band algebra - and more confidential topics, like CADRG NITF product generation, new E57 raster driver, IHO S-102/S-104/S-111 driver enhancements etc. foss4g-europe-2026-4899-state-of-gdal-what-s-new-in-3-12-and-3-13 State of software Even Rouault en false https://talks.osgeo.org/foss4g-europe-2026/talk/9AQJYA/ https://talks.osgeo.org/foss4g-europe-2026/talk/9AQJYA/feedback/ Auditorium Talk 2026-06-29T12:00:00+03:00 12:00 00:30 This is a special moment in the evolution of QGIS. Version 4.0 was released earlier this spring and shortly version 4 will become the next long-term release. It’s been 7 years since we’ve had a new major version of QGIS! This talk will focus on what users can expect when they upgrade to QGIS 4.x. There were also quite a few notable features released with the last QGIS 3.x release – QGIS 3.44 Solothurn. This talk will include features from 3.44 as they will be new to anyone migrating from the last LTR (3.40 Bratislava). Each highlighted feature will not simply be described but will be demonstrated with real data. If you want to learn about the latest features in QGIS, this talk is for you! Likely topics include GUI enhancements * Symbology * Annotations * Point cloud support * Data Providers * Processing * Model builder * 3D * Vector editing * Print compositions foss4g-europe-2026-5071-qgis-feature-frenzy-what-s-new-in-qgis-4-0 State of software Kurt Menke en false https://talks.osgeo.org/foss4g-europe-2026/talk/AFMBWP/ https://talks.osgeo.org/foss4g-europe-2026/talk/AFMBWP/feedback/ Auditorium Talk 2026-06-29T12:30:00+03:00 12:30 00:30 MapServer, a founding OSGeo project, is widely used for publishing spatial data and interactive web mapping applications. This talk provides an overview of the new 8.6 release and a preview of the upcoming 8.8 release, highlighting key enhancements and features. Integration with powerful new GDAL features, such as pipelines and cloud-native drivers, is demonstrated, along with results from the first-ever MapServer User Survey. This session is for both current MapServer users and anyone interested in exploring what MapServer can do. foss4g-europe-2026-4971-state-of-mapserver State of software Seth Girvin en false https://talks.osgeo.org/foss4g-europe-2026/talk/HEVHTQ/ https://talks.osgeo.org/foss4g-europe-2026/talk/HEVHTQ/feedback/ Auditorium Talk 2026-06-29T14:30:00+03:00 14:30 00:30 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. foss4g-europe-2026-5403-state-of-mapstore State of software Lorenzo NataliTobia Di PisaStefano Bovio en false https://talks.osgeo.org/foss4g-europe-2026/talk/QKFSSW/ https://talks.osgeo.org/foss4g-europe-2026/talk/QKFSSW/feedback/ Auditorium Talk 2026-06-29T15:00:00+03:00 15:00 00:30 The gap between desktop GIS and browser-based mapping is closing fast. WebGIS solutions are increasingly taking on tasks that previously required dedicated desktop environments, such as complex analytical processing, on-the-fly integration of massive datasets, and workflow automation. While proprietary systems have provided monolithic solutions for these tasks, the open-source ecosystem is now catching up with more flexible, modular approaches. Here, we introduce [GOAT](https://github.com/plan4better/goat), a refactored WebGIS platform built to bring analytical processing to the browser using modern open-source technologies. Traditionally, spatial analysis workflows are fragmented. Data is found on web portals, downloaded, processed locally, and eventually pushed back to a visualization tool. GOAT is designed to consolidate this cycle. Users can search and load data via built-in catalogs, direct uploads, or OGC services, and apply styling directly in the browser. Beyond basic mapping, the application provides spatial modules for travel-time calculations, geostatistics, and general geoprocessing. To support reproducibility, processing steps can be chained together in an automated workflow builder. This allows users to rerun complex analyses when new data arrives or parameters change, and includes support for custom SQL queries. These workflows can also be mapped to UI inputs on the map, allowing non-technical users to execute spatial pipelines by adjusting parameters. Once the analysis is done, results need to be shared. GOAT includes a layout engine for designing and exporting high-resolution map series and PDFs. A dashboard builder lets users combine maps with charts and metrics. Because the dashboards remain linked to the underlying data, they update automatically when the data changes, turning static reports into interactive tools. Under the hood, the project is freely available under the GPL-3.0 license and managed as a monorepo. The frontend relies on React.js, Next.js, and MapLibre GL JS, communicating over modern OGC protocols (Tiles, Features, Processes). All endpoints are built in Python with FastAPI, following both OGC standards for spatial services and the OpenAPI specification. For geoprocessing and data management, the FastAPI backend leverages Python and PostgreSQL/PostGIS. One of our recent architectural shifts is the implementation of DuckLake, a framework that combines the storage efficiency of Parquet files with the analytical speed of DuckDB, alongside PostgreSQL for metadata. Due to scaling issues with PostgreSQL/PostGIS on large datasets and growing volumes of user data, we adopted this hybrid approach. Parquet files allow us to store and query large spatial datasets efficiently, while DuckDB provides the processing speed needed for analytical workloads. This structure enables data to be stored in comparatively cheap volume storage and easily backed up in S3-compatible object storage. For serving data, we implemented a hybrid vector tile architecture. We rely on static vector tiles generated by Tippecanoe for maximum rendering speed, while dynamic vector tiles are created on the fly using DuckDB for filtered or edited datasets. In practice, this means we can render and interact with millions of building footprints or land-use parcels directly in the browser through pre-generated tiles, yet still maintain the flexibility to request dynamic tiles when users modify data or apply filters. To prevent large analytical queries from slowing down the application, we adopted an asynchronous architecture using Windmill to orchestrate Python jobs in the background. Long-running geospatial tasks are defined in a core library powered by Python and DuckDB. Each tool is wrapped as a standard OGC Process, making it accessible from both the frontend and external APIs. This design ensures that processes run in isolation, meaning individual tools can be scaled independently based on their specific resource demands. As a result, users can chain multiple processing steps into background workflows and continue interacting with the map uninterrupted. Finally, the entire platform is Dockerized and deployed via Kubernetes to ensure robust scaling in production. We have also built dedicated data pipelines to ingest base data, such as street networks from OpenStreetMap, spatial features from Overture Maps, and public transit schedules via GTFS. Active mobility is handled by custom algorithms, while public transport routing is powered by the open-source Nigiri engine (from MOTIS). This combination allows users to run complex, large-scale spatial queries like spatial intersections, identifying gaps in public transit networks, or evaluating nationwide accessibility. In this talk, we will share the technical challenges we faced and the architectural decisions we made while building GOAT. Although GOAT has been primarily developed by Plan4better, a core goal of this presentation is to encourage participation from the wider open-source geospatial community. Alongside a live demo, we will discuss practical use cases in Germany. Finally, we will touch on our latest technical experiments, such as integrating locally hosted open-weight LLMs to help users query spatial data via plain text, and outline our roadmap for deeper integration with existing open-source GIS tools like QGIS. foss4g-europe-2026-5537-presenting-the-novel-analytical-webgis-goat Elias Pajares en false https://talks.osgeo.org/foss4g-europe-2026/talk/FZELJW/ https://talks.osgeo.org/foss4g-europe-2026/talk/FZELJW/feedback/ Auditorium Talk 2026-06-29T15:30:00+03:00 15:30 00:30 GIS works really well for most geospatial workflows, but when it comes to real-time or time-series data, things are still not very smooth. The OGC SensorThings API offers a way to access this kind of data through an open standard, but bringing it into everyday GIS workflows is not always straightforward. In this talk, I’ll walk through a practical workflow for working with SensorThings data, from connecting to an API to exploring and visualising time-series data directly in QGIS using the OGC SensorThings plugin and the SensorThings Inspector. From there, I’ll show how the same data can be brought into a web environment using QWC, and what needs to be considered when moving from desktop to web. The focus is on the workflow itself: what steps are needed, what works well, what still feels a bit rough, and how real-time data can fit more naturally into open source GIS. foss4g-europe-2026-5705-exploring-real-time-geospatial-data-with-sensorthings-from-qgis-to-qwc Use cases & applications Mariano Salas en false https://talks.osgeo.org/foss4g-europe-2026/talk/ZLQF7J/ https://talks.osgeo.org/foss4g-europe-2026/talk/ZLQF7J/feedback/ Auditorium Keynote 2026-06-29T16:30:00+03:00 16:30 01:00 Stay tuned! More info coming soon! foss4g-europe-2026-5887-surprise-keynote Keynote Marian Neagul en false https://talks.osgeo.org/foss4g-europe-2026/talk/FZKUP3/ https://talks.osgeo.org/foss4g-europe-2026/talk/FZKUP3/feedback/ A02 Talk 2026-06-29T11:30:00+03:00 11:30 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. 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. foss4g-europe-2026-5469-geoserver-3-tour Andrea AimeJody Garnett en false https://talks.osgeo.org/foss4g-europe-2026/talk/JDGFCR/ https://talks.osgeo.org/foss4g-europe-2026/talk/JDGFCR/feedback/ A02 Talk 2026-06-29T12:00:00+03:00 12:00 00:30 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. foss4g-europe-2026-5468-geoserver-3-complete-final-update Andrea AimeJody Garnett en false https://talks.osgeo.org/foss4g-europe-2026/talk/FSZ39Q/ https://talks.osgeo.org/foss4g-europe-2026/talk/FSZ39Q/feedback/ A02 Talk 2026-06-29T12:30:00+03:00 12:30 00:30 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. foss4g-europe-2026-5470-ogc-apis-with-geoserver-3-implementation-avaialbility-and-next-steps Andrea Aime en false https://talks.osgeo.org/foss4g-europe-2026/talk/TDBVZY/ https://talks.osgeo.org/foss4g-europe-2026/talk/TDBVZY/feedback/ A02 Talk 2026-06-29T14:30:00+03:00 14:30 00:30 You assumed you'd spend your time creating GIS layers, but the computer said no.  Now you're spending your evenings reading logs.  There's got to be a better way?   Welcome to the story about how we rebuilt our 50 requests/s GeoServer setup,  and what we learned along the way.  We'll take a look from an  IT/Operations point of view, and describe how we automated GeoServer updates and built a cluster. What worked for us and what did not? foss4g-europe-2026-5354-lessons-from-running-geoserver-in-a-mid-size-production-environment Use cases & applications Hans YpermanLarissa Bonifacio en false https://talks.osgeo.org/foss4g-europe-2026/talk/NSNJVB/ https://talks.osgeo.org/foss4g-europe-2026/talk/NSNJVB/feedback/ A02 Talk 2026-06-29T15:00:00+03:00 15:00 00:30 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. foss4g-europe-2026-5475-operating-maritime-ais-at-enterprise-scale-with-geoserver Use cases & applications Andrea AimeNuno Oliveira en false https://talks.osgeo.org/foss4g-europe-2026/talk/L3YPNX/ https://talks.osgeo.org/foss4g-europe-2026/talk/L3YPNX/feedback/ A02 Talk 2026-06-29T15:30:00+03:00 15:30 00:30 Building a production-grade WMTS for satellite imagery sounds straightforward—until you hit cloud-native reality. This talk charts UP42’s journey from a 2024 hackathon to a live beta service, exposing the hard lessons learned while delivering STAC-catalogued imagery to professional GIS tools at scale. The Architecture Battle Why GeoServer? While commercial options were cost-prohibitive and emerging tools like TiTiler lacked maturity, GeoServer offered OGC-compliance and a proven REST API. However, "standard" setups quickly crumbled under B2B demands. We’ll dive into: The FUSE Trap How GCP Cloud Storage FUSE crippled tile-seeding performance, and why we pivoted to native GCP BlobStore plugins to unlock massive throughput. The Scaling Wall Why vanilla GeoServer’s clustering failed us, necessitating a strategic (and bumpy) migration to GeoServer Cloud’s microservices architecture. The Integration Tax Real-world troubleshooting of Helmfile fixes, CSI driver misconfigurations, and the "silent" bugs that haunt cloud-native geospatial stacks. Hard-Earned Takeaways We’re sharing our internal RFCs and benchmarks so you don’t have to learn the hard way. Learn why you must evaluate plugin maturity over hype, why FUSE is a bottleneck for tile writes, and why continuous load testing is the only way to survive the jump from "it works on my machine" to "it works for the world." foss4g-europe-2026-5449-scaling-the-sky-from-hackathon-to-production-on-scale-with-geoserver-cloud Matheus Pinheiro dos SantosJeremiah Dominguez Gorrin en false https://talks.osgeo.org/foss4g-europe-2026/talk/VLTRPL/ https://talks.osgeo.org/foss4g-europe-2026/talk/VLTRPL/feedback/ A11 Talk 2026-06-29T14:30:00+03:00 14:30 00:30 Open source and open geospatial data deliver clear technical benefits, yet their real impact is often difficult to communicate to public sector decision makers and governance stakeholders. Metrics familiar to the FOSS4G community-adoption, repositories, contributors-rarely align with how impact is assessed in policy, funding, or institutional contexts. This talk explores how open source impact can be framed in ways that resonate with public authorities and policymakers. Using examples from European open geospatial initiatives, it highlights how open source contributes to transparency, resilience, interoperability, cost efficiency, and long term public value-and how these outcomes can be communicated beyond technical audiences. The session offers a practical framing approach to help practitioners translate open source principles into outcomes that support informed decisions and sustainable public investment. Audience level: Beginner to intermediate Key takeaway: Open source impact is about outcomes and trust-not just technology. foss4g-europe-2026-5685-open-source-open-impacts-what-impact-means-when-talking-to-decision-makers Octavian Borcan en false https://talks.osgeo.org/foss4g-europe-2026/talk/RFHA7S/ https://talks.osgeo.org/foss4g-europe-2026/talk/RFHA7S/feedback/ A11 Talk 2026-06-29T15:00:00+03:00 15:00 00:30 Our climate is changing, and not necessarily for the better. We need to alter the landscape in our cities to cope with urban heat islands and flash floods and what not. This is up to the local governments, but they often-times do not know where to start. And should they make a change to the landscape, are they then on schedule, or is it not good enough? Big data can help them at that. With GIS and remote sensing, we can help urban policy makers make the right choices. And in most cases, we can do this open source! By saying open source I mean: open input data, open resulting data, open algorithms and open scripts. Using landscape metrics like the 3+30+300-rule, the EU Nature Restoration Law, the Human Pressure Index: we can quantify in an open manor where the biggest issues are, and if policy makers are on par. A true FOSS programme has been conducted in The Netherlands on this, and we would like to share our insights to other FOSS enthusiasts that would want to make their city a nicer place to live in. In our presentation we will go over the process, the applicability to policy, and the impact that has been made with FOSS in this field. Finally, we would love to cooperate with other fellow European FOSS-enthusiasts to make their cities greener and more liveable. A picture says more than a thousand words! Let’s make that picture, and convince your local mayor, journalists and citizens! foss4g-europe-2026-5488-making-cities-more-liveable-in-a-foss-way-open-data-to-the-rescue Prof. Hans van der Kwast en false https://talks.osgeo.org/foss4g-europe-2026/talk/8UL8BU/ https://talks.osgeo.org/foss4g-europe-2026/talk/8UL8BU/feedback/ A11 Talk 2026-06-29T15:30:00+03:00 15:30 00:30 As part of its data production workflows, the IGN (the French National Mapping Agency) has developped many QGIS plugins that can be of interest to the QGIS community and we have created QGIS Plugin to ease the access to services of the Geoplateforme * Géoservices : - The GPF Isochrone–Isodistance–Routing plugin brings Géoplateforme’s mobility services into QGIS, offering fast isochrone, distance, and routing calculations. IGN supervised its development with Oslandia. - The French Locator Filter adds high‑quality French geocoding to QGIS using the Géoplateforme API. IGN funded the feature and coordinated the subcontracting with Oslandia. * Data production tools : A first plugin, “Espace co,” has recently become available; its purpose is to allow users to populate specialized collaborative databases, such as BDTopo (the French national database). It is available here : https://plugins.qgis.org/plugins/ign_espace_collaboratif/ * Other plugins are currently under development, and we will strive to make them all available. A non-exhaustive list of what we plan to develop: - View plugin to have predefined styles based on data at your fingertips and to be able to share these styles among people working together. - Plugin for improved Z-axis management (visualization and correction) in QGIS - Plugin for managing lists of objects, saving them, and creating new ones… - Plugin for calculating the shortest path, - Plugin for digitization direction, We are offering this presentation to explain our approach: why we chose to develop QGIS plugins and why we chose to make them available. We will present examples of use cases based on our own needs. This presentation will allow us to gauge whether the QGIS community might be interested in these developments, potentially use them, and perhaps even contribute to them. foss4g-europe-2026-5442-boosting-qgis-what-france-s-mapping-agency-adds-to-the-toolbox lavenantRémi Ferrier en false https://talks.osgeo.org/foss4g-europe-2026/talk/VLFQFH/ https://talks.osgeo.org/foss4g-europe-2026/talk/VLFQFH/feedback/ A12 Talk 2026-06-29T11:30:00+03:00 11:30 00:30 Efficient management of the integrated water cycle requires advanced digital tools capable of centrally integrating, analyzing, and optimizing hydraulic, operational, and territorial information. In this context, Giswater 4 represents a qualitative leap in the digitalization of water services, incorporating substantial improvements in its architecture, interoperability, and analytical capabilities. This work presents the implementation and adaptation of Giswater 4 within the framework of the PERTE project (Strategic Projects for Economic Recovery and Transformation) for the digitalization of the water cycle at Aigues de Manresa. Version 4 introduces a more robust and intuitive interface, greater integration with other platforms through the development of a REST API, and enhanced capacity to work with hydraulic digital twins. Additionally, it includes new functionalities aimed at advanced management of water supply and sanitation networks, such as traceability analysis, scenario evaluation, leak detection, and prioritization of infrastructure investments. foss4g-europe-2026-5441-giswater-4-open-source-innovation-in-water-network-planning David Cano en false https://talks.osgeo.org/foss4g-europe-2026/talk/EVXR3K/ https://talks.osgeo.org/foss4g-europe-2026/talk/EVXR3K/feedback/ A12 Talk 2026-06-29T12:00:00+03:00 12:00 00:30 This presentation introduces an integrated GIS–AHP framework for the spatial assessment of heavy metal (Cd, Pb, Ni) pollution risk in Serbian surface waters. Using data from 54 monitoring sites over 2019–2023, four complementary risk indices—Total Pollution Index (TPI), Bioavailability Index (BI), Trend Index (IT), and Regulatory Risk Index (RRI)—were calculated and integrated via the Analytic Hierarchy Process (AHP). The study identifies five high-risk locations influenced by mining, industrial, and urban pressures, while 75.9% of sites remain in low-risk categories. Spatial statistics using Getis‑Ord Gi* reveal significant clusters, highlighting critical areas requiring targeted monitoring and management. This approach demonstrates the utility of combining GIS and multi-criteria decision analysis for environmental risk assessment and can serve as a model for other regions and contaminants. foss4g-europe-2026-5415-spatial-risk-assessment-of-surface-water-heavy-metal-pollution-using-gis-ahp-in-serbia Jelena Lukić en false https://talks.osgeo.org/foss4g-europe-2026/talk/NJBTKW/ https://talks.osgeo.org/foss4g-europe-2026/talk/NJBTKW/feedback/ A12 Talk 2026-06-29T12:30:00+03:00 12:30 00:30 Lounaistieto, the regional geoinformation and open data network in Southwest Finland, develops and maintains regional data services and supports municipalities, public organisations and research partners in producing high quality, interoperable datasets. Within this initiative, Lounaistieto leads data enrichment, municipal collaboration and the development of sustainable practices for recreational data management. The ongoing Digiretki project aims to improve the quality, accessibility and interoperability of recreational data in Southwest Finland. Its core objectives include integrating the regional recreational database with two national information services, Luontoon.fi map service and the Lipas system, and strengthening municipalities’ ability to produce and maintain accurate, up to date datasets. The work also supports nature tourism companies in enhancing their digital visibility through Visit Finland’s DataHub database. The initiative advances open data availability and interoperability by redesigning the regional Virma data model to align with national standards and by implementing a technical transfer solution that allows enriched Virma data, including site descriptions, images, accessibility information and area based features, to flow automatically into the Lipas system. This ensures nationally compatible and openly accessible datasets and strengthens the foundation for coherent outdoor recreation information across Finland. Municipal engagement, data quality assessments and hands on training further improve the region’s capacity for producing interoperable open data. The project also connects directly to open source software development. The renewed Virma data model and associated documentation are openly published through Lounaistieto’s GitHub, enabling other regions and organisations to adopt, adapt and extend the technical solution. By following open source principles, the initiative promotes transparency, reusability and collaborative development and reinforces the broader mission to build sustainable, interoperable and openly available digital infrastructure for outdoor recreation, municipal service provision and nature tourism innovation. In the presentation, we will introduce the project’s outcomes and provide an overview of Lounaistieto’s work. foss4g-europe-2026-5364-towards-open-and-interoperable-recreational-data-infrastructure Use cases & applications Antti Vasanen en false https://talks.osgeo.org/foss4g-europe-2026/talk/XQUK9R/ https://talks.osgeo.org/foss4g-europe-2026/talk/XQUK9R/feedback/ A12 Talk 2026-06-29T14:30:00+03:00 14:30 00:30 This talk looks at what it means to teach geoinformatics today, in a world where geospatial data, tools, and platforms are already widely available. As geoinformatics becomes part of many different disciplines and FOSS4G solutions make access easier than ever, the real challenge is no longer getting students to use the technology, it’s helping them think spatially and critically. We will reflect on how the explosion of Earth observation data, open datasets, and user-friendly tools has changed both the field and what we expect from graduates. While this accessibility is empowering, it can also lead to shallow, tool-driven learning if we don’t deliberately focus on concepts, methods, and reasoning. A key idea in the talk is the shift from teaching tools to teaching thinking. We will discuss why spatial thinking, domain knowledge, and the ability to critically evaluate data and results matter more than ever, especially when automated workflows and ready-made solutions can hide important assumptions and limitations. The talk will also touch on how artificial intelligence is changing the classroom. As AI becomes part of everyday geospatial workflows, we need to rethink not only how we teach, but also how we assess students, placing more value on interpretation, transparency, and critical engagement with machine-generated outputs. Finally, I will share practical experiences from moving teaching from proprietary GIS software to open-source environments. This includes both the benefits, such as openness, reproducibility, and accessibility, and the challenges of redesigning courses and supporting students with different backgrounds. foss4g-europe-2026-5489-teaching-geoinformatics-when-tools-are-no-longer-the-challenge Evelyn Uuemaa en false https://talks.osgeo.org/foss4g-europe-2026/talk/XGUURN/ https://talks.osgeo.org/foss4g-europe-2026/talk/XGUURN/feedback/ A12 Talk 2026-06-29T15:00:00+03:00 15:00 00:30 The entry level for students to get started with “doing Geo” never has been so low. Open-source software has been enabling young professionals on small budgets to gain hands-on experience – creating a vibrant, young community of FOSS “aficionados” in and around university classrooms. Now, we are seeing a second wave of development: genAI makes open-source tools even more accessible, by tutoring students through the process of installation, analytical workflows, troubleshooting, and eventually code line commands. In this talk, I will show some impressive examples of the level at which students can arrive with the help of generative AI and vibe coding with the GAMA modelling software for agent-based models. In a deeper dive into this topic, I further ask: which competences are left behind with vibe coding? And more fundamentally: if “doing Geo” has become so simple, what is the role of formal Geoinformatics education? When “humans in the lead” turn to “humans in the loop” and beneficiaries of “smart agentic systems”, what will this do to the productive work with FOSS GIS and decision making? How can we humans determine the stages, at which human validation is needed. And how can one validate the outcomes, who wasn’t capable of producing them? By relating to examples from the Spatial Simulation class, I will finish off the talk with some lessons learned in terms of highlights and challenges when integrating (Geo)AI into courses and curricula, and what this implies for future learning, teaching and “doing” Geoinformatics in and beyond Higher Education. foss4g-europe-2026-5559-classroom-vibes-ai-supported-coding-for-doing-geo Gudrun Wallentin en false https://talks.osgeo.org/foss4g-europe-2026/talk/8U7RAB/ https://talks.osgeo.org/foss4g-europe-2026/talk/8U7RAB/feedback/ A12 Talk 2026-06-29T15:30:00+03:00 15:30 00:30 Like WMS and WFS, MapServer mapfiles are an essential component for publishing geospatial data via Open Geospatial Consortium (OGC) services. However, the standard workflow for managing these files can be a confusing experience, where analysts often find themselves toggling between text editors and server environments. MapFile Preview is an evolving, browser-based development environment designed to simplify this process, bringing the creation, management, and testing of MapServer configuration files into a single, intuitive interface. In this abstract, we present the current progress of this tool, which aims to transform MapServer administration from a high-touch technical chore into a visual, validated, and efficient process. Currently a work in progress, the application bridges the gap between raw code and live services through several core, modules: • Integrated Workspace Management: The tool provides a centralized UI for navigating workspace directories. Analysts can open existing files via a system of aliases, which replaces the need to manage long, complex file paths during the preview process. The "Quick New" and guided form features allow for the rapid generation of starter templates. • Real-Time Validation and Formatting: To avoid publishing an invalid Mapfile after submission, the application uses a local MapServer binary to perform instant syntax validation. This identifies errors or warnings before a file is ever published to a production environment. Furthermore, an automated formatting engine "pretty-prints" the code, enforcing consistent indentation that simplifies peer review and long-term maintenance. • Service Previewing: The platform comes with specialized modules for WMS and WFS service testing. GIS Analysts can visualize map layers, legends, and capabilities within the application. The WFS preview includes a layer picker, enabling users to isolate specific data layers to verify that geometry and attribute tables are rendering as intended. Another tool, “CGI Smoke Test,” helps determine whether an issue comes from network connectivity or from the mapfile configuration. • Auto Metadata and AI Assistance: Understanding the complexity of OGC standards and the potential for manual entry errors, the "Auto Metadata" tool of the application generates metadata blocks for WMS, WFS, and WCS services automatically. To further support the user, the "Mapfile Teacher" tool integrates the Gemini AI model with a conversational interface for technical guidance. This AI model uses the official MapServer documentation, so the tool can offer a context-specific LLM for troubleshooting complex logic or learning new syntax. As an ongoing development project, MapFile Preview can be a tool for a more accessible GIS administration. By combining mapfile editing, validation, and preview within a single environment, Mapfile Preview reduces time spent identifying syntax errors and supports the faster and finer publication of spatial data services. foss4g-europe-2026-5327-mapfile-preview-a-browser-based-tool-for-editing-and-testing-mapserver-mapfiles Use cases & applications Stathis PetridisDimosthenis ParadeisisAndreas Gkaravelis en false https://talks.osgeo.org/foss4g-europe-2026/talk/7LNC9E/ https://talks.osgeo.org/foss4g-europe-2026/talk/7LNC9E/feedback/ A13 Talk 2026-06-29T11:30:00+03:00 11:30 00:30 The European Space Agency (ESA) is these days developing a new unified Zarr-based file format for Sentinel (1, 2, and 3) mission products under the Earth Observation Processing Framework (EOPF) initiative. EOPF Zarr enables scalable, cloud-native access to Earth Observation data and represents a significant shift in how EO products are distributed by ESA. This talk introduces the EOPF Zarr format: its design, current status, and practical capabilities. We'll also cover the growing ecosystem of open-source tools being built around it, from plugins for GDAL, xarray, QGIS, R, and Julia, to interactive browser-based exploration of Sentinel imagery with no downloads or preprocessing required. Whether you're new to cloud-based EO workflows or want to learn about the new format, this session will give you a clear picture of where EOPF stands today and how to get started with it. foss4g-europe-2026-5409-the-new-format-for-all-sentinel-products-eopf-zarr Felix Delattre en false https://talks.osgeo.org/foss4g-europe-2026/talk/UKQM9W/ https://talks.osgeo.org/foss4g-europe-2026/talk/UKQM9W/feedback/ A13 Talk 2026-06-29T12:00:00+03:00 12:00 00:30 Building analysis-ready Earth observation products starts well before any algorithm runs. Source data need to be accessible, complete, up to date. That sounds obvious, but doing it reliably across multiple satellite missions while backfilling years of historical archives is not an easy task. This talk is about how we built that foundation. The starting point is a simple Argo CronWorkflow that queries a STAC API and downloads one day of data to S3. Nothing impressive, but Argo already gives you things a cron job doesn't: built-in retries, a web UI showing exactly which step failed, and the full log. Your Python script doesn't change, you're just not the (only) one watching it anymore. This talk follows what happened when we scaled this up across various satellite products. Each problem we ran into pushed us to add something: fan-out parallelism when sequential backfills were taking days, STAC as a logbook of what had already been ingested and what is missing, and eventually an observability layer when we needed to understand periods of higher error rate. The combination of autonomous backfill and automated monitoring creates a system that self-corrects at two levels: individual failed items are retried via STAC gap detection, while systemic issues surface in daily reports for human intervention. All the tools are open source: Argo Workflows, STAC API, Python, Kubernetes, CI pipelines Attendees will leave with a concrete understanding of what Argo Workflows gives you at each stage of complexity, from replacing a cron job to running a system you can trust "unsupervised". foss4g-europe-2026-5420-from-cron-job-to-self-healing-pipeline-using-argo-and-stac-for-eo-data-ingestion Remote Sensing Loïc Houpert en false https://talks.osgeo.org/foss4g-europe-2026/talk/JFCDW9/ https://talks.osgeo.org/foss4g-europe-2026/talk/JFCDW9/feedback/ A13 Talk 2026-06-29T12:30:00+03:00 12:30 00:30 Over the past years, mapchete has evolved from a tile-based raster and vector processing library into a modular ecosystem for building and operating large-scale geospatial data processing pipelines. Previous presentations at FOSS4G focused on the core package and touching scalable processing patterns using dask and mapchete. This talk presents the next step: the open source publication of additional components developed in a production context, including mapchete EO, mapchete Hub, and mapchete Hub CLI. These packages extend mapchete's core processing model towards Earth Observation (EO) use cases and distributed execution, with a focus on reproducibility, scalability, and a variety of (pre)processesing capabilities relevant for EO. mapchete EO provides higher-level primitives for working with satellite imagery (primarily Sentinel-2), including typical preprocessing steps such as cloud masking, BRDF correction, and temporal compositing. These components are derived from operational pipelines used in the EOxCloudles (cloudless.eox.at) product line, where consistent large-scale processing and data quality constraints are critical. mapchete Hub introduces a service layer for orchestrating distributed processing of mapchete tasks. Processing jobs can be submitted, scheduled, and monitored via an API. The API design is oriented towards the OGC API - Processes standard, aligning mapchete-based workflows with emerging interoperable interfaces in the geospatial ecosystem. The accompanying CLI (mapchete hub CLI) provides a minimal interface for interacting with this system without requiring custom integration. In addition to the software components, the talk covers recent changes in packaging and distribution. All packages are now published via both PyPI and Conda, and container images are provided through GitHub Container Registry. All packages were moved to a dedicated mapchete organization. foss4g-europe-2026-5527-from-tile-based-processing-to-distributed-execution-extending-the-mapchete-stack Joachim Ungar en false https://talks.osgeo.org/foss4g-europe-2026/talk/PXCHPM/ https://talks.osgeo.org/foss4g-europe-2026/talk/PXCHPM/feedback/ A13 Talk 2026-06-29T14:30:00+03:00 14:30 00:30 EOEPCA (Earth Observation Exploitation Platform Common Architecture) is a European Space Agency (ESA) funded project led by Telespazio UK that defines a reusable exploitation platform architecture using open standard interfaces. Its goal is to encourage interoperation and federation between operational exploitation platforms, facilitating easier access and more efficient exploitation of the rapidly growing body of Earth Observation (EO) and other data. Users are beginning to appreciate the advantages of exploitation platforms. However, the market now offers a plethora of platforms with various added value services and data access capabilities. This ever-increasing offer is rather intimidating and confusing for most users. Users often face challenges such as inconsistent interfaces, proprietary software and limited interoperability. To fully exploit the potential of these complementary platform resources we anticipate the need to encourage interoperation amongst the platforms, such that users of one platform may consume the services of another directly platform-to-platform. The EOEPCA system architecture is designed to meet a set of defined use cases for various levels of user, from expert application developers to data analysts and end users. The architecture is defined as a set of Building Blocks (BBs), exposing well-defined open-standard interfaces. These include Identity and Access Management, Resource Discovery, Data Access, Processing Workflows, Datacube Access, Machine Learning Operations and more. Each of these BBs are containerised for Kubernetes deployment, which provides an infrastructure-agnostic deployment target. The recent stable release of EOEPCA+ 2.0 delivers 11 production-ready building blocks, each with deployment scripts, documentation and interactive tutorials. Work is progressing towards version 2.1. All EOEPCA+ source code is public on GitHub under open-source licences. We will outline how individuals and organisations can get involved, and discuss how EOEPCA+ connects to the broader Open Science community and the future direction of the Common Architecture. foss4g-europe-2026-5450-eoepca-open-source-building-blocks-for-eo-exploitation-platforms-architecture-community-and-the-road-ahead Remote Sensing Richard ConwayJames Hinton en false https://talks.osgeo.org/foss4g-europe-2026/talk/BW3SWP/ https://talks.osgeo.org/foss4g-europe-2026/talk/BW3SWP/feedback/ A13 Talk 2026-06-29T15:00:00+03:00 15:00 00:30 As large repositories of EO data become increasingly available, the ability to search across these repositories and archives of data is paramount importance to address the geospatial data and metadata explosion. Distributed search requires standard for metadata encodings, and behaviour of how to delegate requests against remote catalogues. In addition, metadata mappings and crosswalks are critical when attempting to harmonize across metadata standards as part of real-time distributed search. This presentation will provide an overview of recent work in pycsw in the context of the ESA EOEPCA project, as well as updates to the OGC API - Records standard to add formal specification of this behaviour. foss4g-europe-2026-4946-federated-search-using-pycsw Remote Sensing Tom KralidisAngelos Tzotsos en false https://talks.osgeo.org/foss4g-europe-2026/talk/MPLR3B/ https://talks.osgeo.org/foss4g-europe-2026/talk/MPLR3B/feedback/ A13 Talk 2026-06-29T15:30:00+03:00 15:30 00:30 ROCS is building Romania’s Earth Observation infrastructure using FOSS4G tools like STAC, COG, Zarr, ~~MinIO~~ SeaweedFS and Kubernetes but also leveraging components from ESAs EOEPCA+ stack. The platform enables scalable, cloud-native data access, processing and analytics. Real-world use cases include crop monitoring, forest compliance, EO education, all developed in an open and reusable manner. The ROCS project represents a national effort to build a scalable, cloud-native infrastructure for EO data management and analysis in Romania, fully based on FOSS4G tools. The presentation will cover the project’s architectural design, technical stack (including STAC, COG, Zarr, OGC APIs and Kubernetes) and implementation of federated data centers that ensure efficient, distributed EO processing. A key focus is on real-world applications, including automated crop monitoring, forestry compliance (e.g., EUDR), and the integration of EO tools in education. ROCS is committed to open development, reproducibility and interoperability, aiming to contribute back to the FOSS4G community. The session will be valuable for developers, platform builders and public sector stakeholders interested in building sustainable, cloud-ready EO platforms with open-source technologies. The talk will also reflect on practical challenges encountered, including the growing concern over license volatility in widely used open-source projects and how this affects long-term sustainability. foss4g-europe-2026-5697-rocs-extending-romania-s-national-infrastructure-within-the-european-collaborative-ground-segment-with-foss4g-solutions Use cases & applications Marian Neagul en false https://talks.osgeo.org/foss4g-europe-2026/talk/EWDVAY/ https://talks.osgeo.org/foss4g-europe-2026/talk/EWDVAY/feedback/ A01 Talk 2026-06-29T14:30:00+03:00 14:30 00:30 The two papers by Cannata et al. (2023) and Collombin et al. (2024) address a central paradox in open geospatial research: while geospatial web services (e.g. OGC-based services) foster data sharing in line with Open Science and FAIR principles, they simultaneously challenge reproducibility. This core issue concerns particularly dynamic geospatial data. Unlike static datasets stored in repositories with persistent identifiers (e.g. DOIs), data accessed through web services are continuously updated, corrected, or reprocessed. As a result, the exact dataset used in a study may no longer be retrievable in its original state, making it difficult or impossible to reproduce results. This issue is particularly critical for time-varying datasets such as environmental monitoring, sensor observations, or cadastral data. Even when workflows and computational environments are reproducible, reproducibility ultimately fails if the underlying data cannot be accessed in the same version used in the original analysis. Both works highlight that current geospatial infrastructures lack key mechanisms such as data versioning, persistent identification, and temporal querying capabilities (“system-time”). Without these, web services cannot guarantee access to historical data states. Addressing this limitation requires moving beyond interoperability toward infrastructures that explicitly manage data evolution over time, enabling retrieval of past states and supporting transparent and verifiable research. In this context, istSOS4Things (https://github.com/istSOS/istSOS4) is introduced as a SensorThings API compliant solution that tackles these challenges by integrating mechanisms for temporal versioning, traceability, and controlled access directly into the data service layer. Rather than acting as a simple interface to mutable data, the system is designed as a version-aware and policy-enabled service, capable of preserving and exposing the evolution of geospatial data streams. A core element of this approach is the implementation of system-time versioning at the database level, where each observation is associated with temporal attributes capturing both its validity and its transaction history. This enables reconstruction of the dataset as it existed at a specific point in time, effectively introducing a “time-travel” capability. Users can therefore query not only the current state of the data, but also past states, addressing the reproducibility gap identified in the literature. From an architectural perspective, istSOS4Things adopts a container-based, microservice-oriented design, where each component is deployed as an independent service and orchestrated through Docker. The core of the system is a PostgreSQL database extended with PostGIS. On top of the database, the API layer is implemented using SQLAlchemy ORM with asyncpg as query engine and FastAPI for routing logic, and served through Uvicorn as an ASGI server. To support performance and scalability, the architecture integrates Redis as an in-memory data store, used for caching request to query conversion workload. This combination of FOSS ensures high performance, asynchronous request handling, and scalability of the SensorThings API endpoints. In istSOS4Things, the “time-travel” capability is exposed through an extension of the SensorThings API query model that introduces explicit temporal navigation parameters. In particular, an as_of parameter allows retrieving the state of the data at a specific timestamp, while a from_to parameter enables exploration of how data evolved over a defined time interval. These parameters extend standard OData-based filtering mechanisms and bring system-versioned data concepts, commonly found in temporal databases, into web-based geospatial services. A key innovation is the introduction of a commit-based versioning model. Each modification to the dataset is recorded as a Commit entity, representing a discrete change event that groups one or more operations. Each commit is associated with metadata such as timestamp, description, and context, and is linked to a User entity, capturing the identity of the actor responsible for the change. This explicit association enables tracking of who performed what modification and when, introducing accountability and traceability into the data lifecycle. Observations are therefore not only versioned in time, but also logically grouped into commits, forming a structured history of changes. This allows navigation across dataset evolution both by timestamp (system-time) and by discrete change events. In practice, this enables reconstruction of the dataset at a given point or commit, inspection of differences between versions, and understanding of the sequence of transformations applied to the data. The combination of temporal versioning and commit-based tracking provides a comprehensive provenance model that goes beyond simple versioning. Importantly, the combination of service endpoint, query definition, and temporal reference (e.g. via as_of) effectively defines a persistent and reproducible view of the dataset, supporting reproducible data citation without requiring static dataset snapshots. Building on this, the system supports reproducible data access through fully specified queries. By combining spatial, temporal, and thematic filters with temporal parameters, users can re-execute the same query over a well-defined data state. This shifts reproducibility from static data publication toward reproducible data access patterns, where both the query and the temporal context define the dataset. Another key aspect is the integration of fine-grained access control and policy enforcement mechanisms directly at the data layer. Access to data is regulated through a Role-Based Access Control (RBAC) model implemented using PostgreSQL roles combined with Row-Level Security (RLS) policies. This enables permissions to be enforced not only at the table level, but also at the level of individual records, allowing selective visibility and editing of observations based on the querying user (e.g. restricting updates to specific sensor networks). Overall, this work promotes a shift in how reproducibility is approached in geospatial research. Rather than relying on static data publication, it embraces the dynamic nature of data and provides mechanisms to reconstruct past states, document their evolution, and control access over time. By combining temporal versioning, commit-based change tracking, extended query capabilities, provenance metadata, and policy-based access control, istSOS4Things transforms geospatial web services into reproducible, auditable, and governable data infrastructures, directly addressing the limitations identified in prior research. foss4g-europe-2026-5919-istsos4things-a-reproducible-auditable-and-governable-sensor-data-infrastructures Academic track Massimiliano Cannata en false https://talks.osgeo.org/foss4g-europe-2026/talk/MTBKC7/ https://talks.osgeo.org/foss4g-europe-2026/talk/MTBKC7/feedback/ A01 Talk 2026-06-29T15:00:00+03:00 15:00 00:30 Metadata are a foundational component of any data infrastructure, as they enable dataset discovery, evaluation, and reuse. Metadata creation and maintenance, however, is typically a costly, inconsistent, and largely manual process. In the European context, this bottleneck is visible in major data sharing initiatives such as the INSPIRE Directive and the Common European Data Spaces, where high-quality, interoperable metadata are a legal requirement as well as a precondition for data market participation. Two main challenges affect the creation and maintenance of geospatial metadata. The first is schema heterogeneity, since existing schemas (including ISO 19115, DCAT, GeoDCAT-AP, Dublin Core, INSPIRE profiles) show partially overlapping semantics but incompatible serialisations. Migration between schemas is typically performed through hand-crafted transformations encoding explicit field-to-field mappings: brittle, schema-specific artefacts requiring specialist maintenance. The second challenge is content inconsistency: even within a single schema, records produced by different operators (including within the same organisation) may exhibit inconsistent terminology, structure, and level of detail in free-text fields – which undermines catalogue-level discoverability. Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in particular offer promising capabilities to address these challenges. AI-assisted metadata management (acquisition, cleaning, verification and maintenance) has been already explored in literature [1], but to our knowledge not in the case of geospatial datasets. Existing work includes for example AI support for digitizing libraries, classifying research resources, and create website content. Whatever the purpose, research has focused on how to instruct LLMs to produce structured outputs for high-quality metadata [2] or to address the limitations of metadata schemas, particularly free-text attributes whose definitions are often self-referential or provide insufficient contextual meaning for GenAI to process [3]. This work extends such literature by proposing an open-source, AI-powered geospatial metadata editor. The core design principle is schema agnosticism: output schemas are defined as declarative configuration files, and any new schema can be registered without modifying the application code. Field extraction and generation are driven by attribute type classification, independently of field names or schema-specific logic, with GeoDCAT-AP 3.0.0 as the current default. The tool recognises three different types of metadata attributes and tackles them differently. Type A attributes, such as title, keywords, and description, are characterised by a free-text nature and as such, they are suitable for automatic generation using LLMs and prompts with contextual information. Type B are technical attributes such as spatial resolution, bounding box, file extension, etc. that can be automatically extracted from the dataset by standard functions. Finally, Type C are organisational attributes, such as publisher, contact point and licensing: these can be usually reused across several records within the same organisation. Input handling is multimodal. For extraction of Type B attributes, the tool accepts native geospatial datasets processed via GDAL/OGR, or the URL to the OGC service serving the dataset. As a third option, useful for large datasets, the textual output of the gdalinfo or ogrinfo inspection utilities can be provided directly. Type A attributes are generated using accompanying documentation on the relevant dataset, such as technical reports and scientific publications, which are indexed as corpus content. The generation phase implements Retrieval-Augmented Generation (RAG): semantically oriented queries are issued against the vector corpus for each free-text attribute, results are ranked by relevance, and a context passage is assembled to inform generation. The LLM component is any OpenAI-compatible inference endpoint, supporting both cloud-hosted and fully local deployments. The tool supports three modes of operation through a unified processing pipeline, with differences arising solely from the inputs provided. In the creation mode, the user supplies a geospatial dataset and supporting documentation. Technical (Type B) attributes are populated deterministically from the dataset; publisher’s information (Type C attributes) is parsed from the structured publisher document provided as input. Finally, by querying the assembled corpus and including the Type B and C metadata attributes, the LLM generates free-text fields (Type A) such as title, description, keywords, and provenance. A shared, versioned prompt template encodes conventions – expected content sections, field order, and level of specificity – applied consistently across all generated metadata records. This template functions as a content harmonisation instrument: metadata produced by different operators for different datasets converge on a common descriptive structure, improving catalogue-level discoverability without requiring ad-hoc normalisation. The use of a template and of different prompting techniques to reduce model hallucination and improve harmonisation across datasets has been investigated in an article currently under review [4]. In the schema migration mode, the user additionally supplies an existing metadata record in any supported structured format. The legacy record is parsed into field candidates and indexed in the retrieval corpus. During generation, the LLM receives the legacy metadata as contextual input; the mapping from source schema to target schema fields emerges from the model semantic knowledge of both standards rather than from explicit transformation rules, generalising to schema pairs for which no hand-crafted converter exists. Finally, in the enrichment mode, the user supplies a partial record or publisher documentation declaring organisational fields such as publisher, license, and contact point; these are incorporated at the extraction time, while remaining free-text fields are generated and harmonised through the prompt template. In all three modes, the LLM is invoked only during the generation phase and only upon explicit user action, preserving a human-in-the-loop validation step before the export. Source code was submitted for intellectual property and security screening for release under the open-source European Union Public License (EUPL) according to the organisation policy. Schema definitions and prompt templates are expressed as human-readable declarative files, versioned and customisable without modifying application code. The empirical evaluation in [4] is currently bounded by the institutional homogeneity of JRC-produced metadata; future research will address this limitation by incorporating records from Member State authorities engaged in the INSPIRE GeoDCAT-AP Pilot [5], enabling a more representative assessment of the tool's harmonisation capacity across heterogeneous producer communities. foss4g-europe-2026-5915-an-open-source-ai-powered-geospatial-metadata-editor-for-schema-agnostic-generation-migration-and-content-harmonisation Academic track Margherita Di Leo en false https://talks.osgeo.org/foss4g-europe-2026/talk/SAADFE/ https://talks.osgeo.org/foss4g-europe-2026/talk/SAADFE/feedback/ A01 Academic lightening talk 2026-06-29T15:30:00+03:00 15:30 00:05 The reproducibility and transparency of scientific research are fundamental prerequisites for building trust in knowledge production and for addressing global challenges such as those outlined in the United Nations Sustainable Development Goals (SDGs). However, many scientific publications still lack the necessary information to reproduce results, particularly with regard to underlying software, data, and computational workflows. This situation, commonly referred to as the replication crisis, undermines the reliability and sustainability of scientific practice (Baker, 2016). To address these challenges, the paradigms of Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) have emerged as guiding frameworks. Open Science promotes transparency through open access publications, open data, and open source software, while the FAIR principles define minimum standards for the structured availability and reuse of research outputs. A critical but often underrepresented component in this ecosystem is the proper citation and recognition of research software, including free and open source geospatial (FOSS4G) tools (Smith et al., 2016). The Open Source Geospatial Foundation (OSGeo) represents a mature ecosystem of approximately 50 open source geospatial software projects, supported by a global community. All OSGeo projects undergo a formal incubation process, ensuring adherence to best practices such as open licensing, publicly accessible repositories, and transparent governance. These practices inherently support several FAIR principles, particularly Findability and Accessibility (Tzotsos et al., 2016). However, gaps remain in long-term preservation, persistent identification, and formal recognition through standardized citation mechanisms. Software is commonly referenced using URLs pointing to code repositories. While suitable for short-term access, URLs lack long-term reliability, as they may become invalid due to infrastructure changes or resource removal. This creates challenges for reproducibility, as references in scientific publications may no longer resolve to the original artifacts (Fenner et al., 2019). Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), provide a robust solution. DOIs enable stable referencing of digital objects independent of their location, supported by infrastructures such as CrossRef and DataCite. Platforms such as Zenodo, operated by CERN and supported by the European Union, facilitate DOI assignment for software and datasets, enabling long-term archiving and citation. The importance of software citation has been formalized by initiatives such as FORCE11, which defined community principles for software citation (Smith et al., 2016), and the Research Data Alliance, which promotes global standards for data and software interoperability. An increasing number of OSGeo projects have adopted DOI-based citation practices. Currently, more than 20 projects provide DOIs, allowing both version-specific citation and project-level referencing. This development aligns with broader trends in geospatial research, where open source GIS has become a central component of scientific workflows (Brovelli et al., 2020). However, implementation depth and automation vary across projects, and the scientific publishing ecosystem has not yet fully adapted to consistently support DOI-based software citation. A key challenge lies in the heterogeneity of publisher workflows. While some journals encourage or require DOI-based software citation, the integration of these references into metadata systems such as CrossRef is not always reliable. As a result, even correctly implemented FAIR practices may fail to produce visible and citable references. This lack of transparency creates uncertainty for researchers, developers, and reviewers and may lead to what can be described as an “information catastrophe,” where contributions remain effectively invisible within the scientific record (Fenner et al., 2019). The FAIR4G project (www.fair4g.org) addresses this challenge by introducing a transparent, data-driven approach to monitoring and documenting FAIR software citation practices in the open geospatial domain. Launched in 2025 as a volunteer-driven initiative, FAIR4G provides continuously updated analyses of DOI-based citations for OSGeo projects, based on CrossRef metadata from scientific journals and books. The FAIR4G web portal serves as a centralized, low-barrier information resource for stakeholders across the Open Science ecosystem. For each participating software project, it offers tabular overviews of DOI-based citations, including publication date, publication type, publisher, journal title, and the DOI of the citing work. Additional contextual information, such as links to project websites and Zenodo landing pages, enhances transparency and usability. These data provide significant added value for multiple stakeholder groups. OSGeo projects can track the scientific reuse of their software across disciplines and optimize citation guidelines. Individual contributors gain visibility into how their work is reused in scientific and societal contexts. Researchers can identify journals that successfully implement FAIR software citation practices, enabling more informed publication strategies. Publishers and journals can use FAIR4G data to benchmark and improve their workflows, supporting their transition towards Open and FAIR practices. The Geospatial Data Abstraction Library (GDAL) illustrates these dynamics. Since registering a DOI in 2022 and archiving releases via Zenodo, GDAL has seen increasing adoption in both downloads and DOI-based citations. FAIR4G analyses show a steady growth in citations across a wide range of scientific disciplines, highlighting the central role of open geospatial software in contemporary research. FAIR4G is an evolving project that aims to expand its analytical capabilities and data services. Planned developments include temporal and thematic analyses of DOI adoption across publishers and journals, as well as the provision of FAIR-compliant, machine-readable datasets. These efforts are intended to foster dialogue among stakeholders and support the continuous improvement of standards and infrastructures for software citation. In conclusion, FAIR4G addresses a critical gap at the intersection of Open Science, FAIR principles, and open geospatial software. By increasing transparency and providing actionable insights into DOI-based software citation practices, it supports reproducibility, recognition, and sustainability in scientific research. As such, FAIR4G contributes both a practical tool and a conceptual framework for strengthening the role of FOSS4G within a more open and sustainable scientific ecosystem. foss4g-europe-2026-5911-fair4g-advancing-fair-software-citation-and-transparency-for-open-geospatial-science Academic track Peter Löwe en false https://talks.osgeo.org/foss4g-europe-2026/talk/E9HEQB/ https://talks.osgeo.org/foss4g-europe-2026/talk/E9HEQB/feedback/ A01 Academic lightening talk 2026-06-29T15:35:00+03:00 15:35 00:05 Urban Heat Islands (UHI) have emerged as a critical environmental and socio-spatial challenge in rapidly urbanizing regions worldwide. The phenomenon is primarily driven by land-use transformations, increasing building density, and the progressive reduction of vegetation cover, all of which contribute to elevated land surface and air temperatures in urban areas compared to their rural surroundings. The UHI effect has far-reaching implications, significantly affecting public health, increasing energy consumption for cooling, and reducing overall urban livability. In recent years, it has also been increasingly recognized as an issue linked to fundamental human rights, including the right to health, adequate housing, and a sustainable and safe environment. Urban populations are particularly vulnerable to climate change impacts such as extreme heat events due to high population densities, sealed surfaces, and limited access to green spaces. Consequently, international policy frameworks emphasize the urgent need for climate-resilient urban planning and the adoption of nature-based solutions to mitigate these risks. The city of Tirana represents a compelling case for investigating the dynamics of the Urban Heat Island effect. Over the past three decades, Tirana has undergone rapid and often unregulated urban expansion, characterized by intensive construction activity, densification, and significant land-cover changes. These processes have substantially altered the urban morphology and have intensified the UHI effect, particularly in densely built central areas. While recent studies have documented temperature variations across the city and identified key drivers such as reduced vegetation, increased impervious surfaces, and urban density, there remains a lack of systematic, spatially explicit, and reproducible analyses. Furthermore, limited attention has been paid to linking these environmental patterns with urban planning policies and the existing legal framework, creating a gap between technical assessments and policy-oriented applications. Within the Albanian legal context, the Urban Heat Island phenomenon is not explicitly defined as a standalone concept. However, it is indirectly addressed through a set of interrelated legal instruments, including legislation on territorial planning and development, environmental protection, climate change mitigation and adaptation, energy efficiency, and the energy performance of buildings. These laws collectively promote principles of sustainable development, encourage the integration of green infrastructure, and support measures aimed at enhancing climate resilience. At the local level, the Tirana General Local Plan (TR030) incorporates provisions related to ecological corridors, natural systems, and the expansion of public green spaces. Although these instruments do not directly target UHI, they establish an institutional and regulatory framework that contributes to mitigating urban heat, highlighting an implicit obligation for public authorities to address the phenomenon more explicitly in future planning processes. This study aims to investigate the spatial and temporal dynamics of the Urban Heat Island effect in Tirana over a ten-year period. The research adopts a fully open and reproducible approach by relying exclusively on Free and Open-Source Software (FOSS) and openly available Earth Observation data. The primary objectives are to identify UHI hotspots, analyze their relationship with vegetation cover and built-up expansion, and evaluate how these spatial patterns correspond to existing planning and legal instruments. By doing so, the study seeks to bridge the gap between geospatial analysis and policy-making, offering insights that can inform evidence-based urban planning. The methodological framework is based on satellite imagery obtained from Landsat 8 and Landsat 9 missions. Land Surface Temperature (LST) is derived using established radiometric calibration and atmospheric correction techniques to ensure accuracy and comparability over time. Vegetation dynamics are assessed through the Normalized Difference Vegetation Index (NDVI), which is further used to calculate the Proportion of Vegetation (PV), providing a more detailed understanding of vegetative cover distribution. Urban expansion and built-up intensity are analyzed using the Normalized Difference Built-up Index (NDBI), enabling the identification of areas experiencing significant urban growth. All data processing, analysis, and visualization are conducted using open-source tools, primarily QGIS, alongside Python-based libraries such as GDAL, rasterio, and NumPy. This approach ensures that the entire workflow is transparent, reproducible, and accessible, aligning with the principles of open science and the FOSS community. Moreover, it demonstrates that advanced geospatial analysis can be conducted without reliance on proprietary software, making it particularly relevant for researchers and institutions with limited resources. Preliminary findings reveal a strong spatial correlation between elevated Land Surface Temperatures and densely built areas with limited vegetation cover, particularly in the central zones of Tirana. In contrast, areas characterized by higher NDVI values, including parks, green corridors, and peri-urban zones, consistently exhibit lower temperatures, confirming the cooling effect of vegetation and green infrastructure. Additionally, areas undergoing rapid urban development show a noticeable increase in thermal intensity over time, suggesting that current planning measures may be insufficient to counterbalance the thermal impacts of urbanization. The study contributes to the broader geospatial and FOSS community by presenting a comprehensive, open, and transferable methodology for analyzing Urban Heat Islands in medium-sized cities. It highlights the value of integrating geospatial technologies with legal and planning analysis, thereby bridging the divide between technical research and policy implementation. The findings support the need for more proactive and climate-sensitive urban planning strategies, including the expansion of green spaces, the use of permeable and reflective materials, and stricter regulation of building density and land use. Ultimately, this research underscores the critical role of open-source tools in advancing sustainable, climate-resilient, and human-centered urban development. By providing a robust evidence base and a reproducible analytical framework, it aims to support decision-makers, planners, and researchers in addressing the growing challenges posed by the Urban Heat Island effect in Tirana and beyond. foss4g-europe-2026-5905-urban-heat-island-dynamics-in-tirana-a-foss-based-analysis-within-the-urban-planning-and-legal-framework Academic track Leonora Haxhiu en false https://talks.osgeo.org/foss4g-europe-2026/talk/YPABRR/ https://talks.osgeo.org/foss4g-europe-2026/talk/YPABRR/feedback/ A01 Academic lightening talk 2026-06-29T15:40:00+03:00 15:40 00:05 *Introduction* European landscapes have experienced drastic and accelerating changes in recent decades, particularly since World War II. Understanding landscape change from a historical perspective is essential in assessing the impact of previous land management choices on current-day landscapes, habitats and biodiversity. While conservation policies typically consider current situations, the lack of historical context might lead to shifting baselines to fit a deteriorating trend. The Habitat Map of Switzerland is a high-resolution thematic product mapping the different Swiss habitats [1]. The classification is based on TypoCH, a Swiss-specific hierarchical habitat typology, which can be translated into the pan-European EUNIS classification system. TypoCH contains land cover classes on the first level and becomes increasingly more detailed, with plant communities and species on the lower levels [2]. Recently declassified Swiss-wide imagery from 1946 with 1m spatial resolution, in conjunction with regularly updated aerial imagery since the 1980s, offers an unprecedented opportunity to map landscape and habitat change in the past century. In the perspective of multi-temporal classification, a proof-of-concept study was performed on selected study areas in Switzerland, using grayscale 1946 aerial imagery, and object-oriented analysis and classification [3]. The gap bridged by this project is creating consistent multi-temporal mapping for Switzerland, with potential to apply the methodology in other countries with similar historical data. The inherent inconsistencies in historical aerial image quality, the limited spectral information (grayscale) and habitat heterogeneity and complexity are the most challenging. Consistent mapping is important for robust change detection and comparison between time steps. Therefore, a flexible habitat typology and scalable method should be determined. This study aims to map habitat status and explore habitat change in Switzerland over 5 times steps (1946 – present) using deep learning image segmentation methods. Given the varying quality and spectral resolution of imagery over time, the first aim of this project is to determine which habitats can be consistently mapped from 1946 to the present. This will be done in a data-driven approach, developing a hierarchical, modular and flexible open-source deep learning methodology to check habitat mapping feasibility. The further aims are to develop a method to consistently classify habitats across this time-series, detect landscape change, and relate results to the current biodiversity status. *Methodology* To determine which habitats can be mapped from grayscale imagery, a data-driven approach will be used, starting from current-day aerial imagery and Habitat Map of Switzerland, integrated into a preliminary architecture based on a hierarchical U-Net structure. The current-day aerial imagery will be degraded to simulate historical aerial imagery, using state-of-the-art algorithms to add noise, scratches, distortions and blurring [4]. The Habitat Map of Switzerland will be used as training, validation and test data. The first level of the U-Net architecture will segment the first level of the habitat typology, which mainly corresponds to land-cover classes. The current Habitat Map of Switzerland includes habitats up to the third level of the TypoCH typology. The U-Net will be trained to first separate the TypoCH classes, which correspond to the first level of the TypoCH typology. For each class, a new U-Net will be trained to separate the groups within the class, which correspond to the second level of the TypoCH typology. Then, for each group, new U-Nets will be trained to separate types within each group, which correspond to the third level of the TypoCH typology. This approach will inform about habitats which are more difficult to map, as well as habitats which might be easily confused with other habitats. This in turn will inform on class-specific or group-specific variables which would need to be added to address uncertainty and the limited spectral information. In conjunction with a previous ecological priority review, adjacent data will be researched for habitats which are more difficult to map but are of high ecological importance in terms of long-term landscape change. Potential auxiliary sources include digitized data [5] or even automated feature extraction from topographical maps (Siegfried maps). The data for the model was chosen using a stratified random sampling technique. The extent of Switzerland was gridded into 512x512px tiles. The choice of the tiles was stratified in a first phase using the 12 biogeographical regions of Switzerland. Upon preliminary results, additional stratification on habitat composition, certain habitat coverage percentage or elevation might be considered. 5% of tiles of each biogeographical region were randomly selected, resulting in 7356 tile-mask pairs. Per region, the tile-mask pairs were separated into 70% for training, 20% for validation and 10% for testing. The lack of validation data for historical imagery is one of the biggest challenges of the project. Therefore, in the first step, the model will be trained and tested on current aerial imagery converted to grayscale and degraded with artifacts common to historical imagery. This way, the feasibility of mapping the wide range of TypoCH habitats will be tested with robust validation based on the current Habitat Map of Switzerland. In further steps, the model will be strategically applied on historical imagery and potentially active learning and/or zero-shot segmentation algorithms will be used to generate historical training data, aided by time-series comparisons. *Expected results, implications and conclusions* The first part of the project will show which habitats can be mapped from historical imagery, providing a methodology which can be transferred in other areas with historical data availability. Then the method will be scaled Swiss-wide on multiple time steps, showing types and rates of habitat change and link the results to management practice and the current biodiversity status. The results will have broad implications on future conservation measures, land management policies, and restoration actions. Given the amount of data available for Switzerland, training a geofoundation model specialized on historical grayscale imagery and object change detection would be an idea to be explored as a future part of this project using the knowledge and data obtained from preliminary model testing. foss4g-europe-2026-5918-habitat-change-mapping-using-historical-aerial-imagery-and-deep-learning Academic track Francesca Drăguț en false https://talks.osgeo.org/foss4g-europe-2026/talk/BGWUCE/ https://talks.osgeo.org/foss4g-europe-2026/talk/BGWUCE/feedback/ A01 Academic lightening talk 2026-06-29T15:45:00+03:00 15:45 00:05 Earth system science is increasingly driven by an unprecedented influx of heterogeneous Earth observation and model data, but these data typically arrive as disparate products, tiles, and collections rather than as uniform analysis-ready cubes. In response, a growing set of data cube frameworks aims to integrate heterogeneous datasets into common, interoperable spatio-temporal structures. Earth System Data Cubes (ESDCs) are one such framework (Mahecha et al., 2020), and can be understood as labelled, multi-dimensional arrays of Earth system data that organize variables consistently across space and time (or any other dimension), enabling uniform operations across common grids. Concretely, ESDCs comprise (1) labelled dimensions defining the data cube axes, (2) one or more grids with coordinate values distributed along these dimensions, (3) univariate values associated with each grid cell, and (4) a suite of attributes that characterise the data variables, the dimensions, and the cube entity as a whole. In practice, however, building such data cubes still requires significant engineering to discover datasets, harmonize metadata, and create consistent arrays that Artificial Intelligence (AI) models can consume (Montero et al., 2024a). In recent years, the SpatioTemporal Asset Catalog (STAC) specification has become a widely adopted way to describe and access cloud-hosted geospatial assets, enabling programmatic discovery and standardized links to imagery and other derived products. Building on this ecosystem, we developed cubo (Montero et al., 2024b), an open-source Python tool for creating AI-focused ESDCs from STAC catalogues, producing data cubes (as xarray objects) on regular spatial grids with consistent array shapes (e.g. matching pixel counts along x and y or longitude and latitude). Yet a large portion of routinely used Earth observation data is accessed through Google Earth Engine (GEE), a cloud-based platform that hosts a large, curated catalogue of geospatial datasets and provides scalable, planetary-scale analysis via both JavaScript and Python APIs (Gorelick et al., 2017). The catalogue spans long optical and radar satellite archives (e.g. Landsat and Sentinel-1 and Sentinel-2), widely used global products (e.g. MODIS, ERA5 reanalysis, SRTM), and thematic layers and derived datasets such as land cover and vegetation indices. As a result, users face a fragmentation problem: cubo can readily create ESDCs from STAC catalogues, but datasets that are primarily accessed via GEE remain out of reach for the same data cube specification and output conventions. Here we present a Google Earth Engine (GEE) backend for cubo that generates on-demand AI-focused Earth System Data Cubes (ESDCs) directly from GEE, using the same data cube specification concept developed initially for STAC catalogues and returning consistent xarray outputs (Hoyer and Hamman, 2017). The optional GEE backend mirrors the STAC workflow in cubo: users specify cube centre coordinates (longitude and latitude), a temporal window, bands, cube edge size (pixels), and a target spatial resolution, and cubo derives the corresponding bounding box in the local Universal Transverse Mercator (UTM) Coordinate Reference System (CRS). This keeps the data cube definition explicit and comparable across studies, and it makes the data preparation step a parameterized part of the workflow. The key difference is the data access layer: instead of retrieving assets via STAC (using stackstac: https://github.com/gjoseph92/stackstac), cubo queries GEE collections through xee (https://github.com/google/Xee), an xarray interface to Earth Engine that returns the result directly as xarray objects. From the user perspective, the same cube specification is reused, with the collection identifier now pointing to a GEE collection. The only additional argument in the main cubo function is selecting the GEE backend (via a boolean flag). This keeps data cube construction consistent across backends while leveraging GEE as a scalable data access and processing environment. By aligning GEE-based cube creation with an existing STAC-based cube workflow, the GEE backend lowers the practical barrier to switching between catalogues and platforms without rewriting entire pipelines. It also opens up access to datasets that are primarily available through GEE (e.g. CloudScore+, Dynamic World, or the novel AlphaEarth Embeddings) while still adhering to the same cube specification and output conventions. Retrieving data cubes from GEE and from STAC catalogues using the same cube specification also enables users to merge data cubes across backends with minimal effort, since they share consistent dimensions and coordinates. This is particularly relevant for open geospatial ecosystems, where interoperability and transparent data preparation are prerequisites for comparable results across studies. We release the Earth Engine support as an optional backend in cubo (installable via the extra cubo[ee]), which is free and open source, hosted on GitHub (https://github.com/ESDS-Leipzig/cubo), and distributed through common Python channels (PyPI and conda-forge). We expect users to benefit from this update since they can now retrieve data from both STAC catalogues and GEE in the same way for their scientific workflows, using consistent cube specifications across backends. Looking forward, we plan to extend cubo so that multiple datasets can be retrieved and organised directly into a single data cube without rerunning the full workflow for each collection, regardless of the backend they come from. We also plan to broaden the set of supported backends to additional widely used packages in the open geospatial ecosystem, such as odc-stac. foss4g-europe-2026-5904-a-unified-framework-for-building-ai-focused-earth-system-data-cubes-across-stac-and-google-earth-engine Academic track David Montero Loaiza en false https://talks.osgeo.org/foss4g-europe-2026/talk/BVBPNG/ https://talks.osgeo.org/foss4g-europe-2026/talk/BVBPNG/feedback/ Auditorium Talk 2026-06-30T10:30:00+03:00 10:30 00:30 As a public agency, IGN has an obligation to publish in open format the data it produces and some of the code it develops. But our commitment to Open Source goes beyond giving access to the code produced and for more than ten years, IGN (the French map agency) has been engaged in creating Digital Commons, both in terms of data and in terms of code. In addition to contributing to well-known open-source libraries (pdal, gdal, etc.), our developers push a large part of their code on public repository on github. And, for some, we are working to build and animate a community of contributors and users. In this conference, we will explain why a national mapping agency has chosen to adopt such an approach and why we believe Digital Commons are strategic to build a sustainable and collaborative future. We will give a quick overview of the code we are making open source and the organization that this involves (in particular, the creation of an Open Source Program Office - OSPO). Finally, we will present our recent activities, organizational (internal drafting of guides, FAQs, etc.) and technical (new project and ongoing developments) and also contributions we can make with other organizations and companies involved in open source initiatives (notably participation in Tosit, a gathering of French companies focused on open source). foss4g-europe-2026-5100-national-map-agency-how-to-build-digital-commons Open community lavenantRémi Ferrier en false https://talks.osgeo.org/foss4g-europe-2026/talk/GW3YWD/ https://talks.osgeo.org/foss4g-europe-2026/talk/GW3YWD/feedback/ Auditorium Talk 2026-06-30T11:30:00+03:00 11:30 00:30 Europe is entering a new geopolitical and economic phase. Resilience, competitiveness, and digital sovereignty are moving to the centre of public debate. In that context, open source is no longer seen only as a software development model or a community ethos. It is becoming part of how Europe thinks about digital capacity and sovereignty. For the geospatial community, this shift matters deeply. Open-source geospatial software forms part of the operational backbone through which data is processed, interpreted, and turned into public value. Despite Europe’s strong communities, mature projects, and world-class technical leadership, cybersecurity, scaling and long-term sustainability remains fragile. This is now being recognised more explicitly at European level, including through the European Open Digital Ecosystem Strategy and the Horizon 2026 Work Programme. The former is a component of the upcoming European Commission’s Technological Sovereignty package, whose adoption is expected for Q2 2026; the latter offers an opportunity for the community to address sustainability considerations through economic leadership. This talk reflects on that changing landscape through the lens of research, innovation, and Europe’s evolving strategic context. It explores how emerging debates on digital sovereignty, AI, and open digital ecosystems are beginning to reshape the wider landscape for FOSS4G in Europe. Together, we will discuss what digital sovereignty may mean in practice for geospatial open source, what kinds of support structures are needed to move from individual project success to durable ecosystem capacity, and how developers, communities, institutions, and companies can help define models of sustainability that preserve openness while strengthening European capacity. The aim is to look beyond policy slogans and consider the deeper shift now underway. If geospatial open source is becoming part of Europe’s strategic future, would the community be ready to respond, and how? What are the existing gaps to fill, challenges to address, and opportunities to be aware of? And what kind of European digital future does it want to help build? * Call for Evidence on the European Open Digital Ecosystem Strategy: https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/16213-European-Open-Digital-Ecosystems_en * A services and business incubator for geospatial open-source developments: https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/HORIZON-CL6-2026-03-GOVERNANCE-06?keywords=incubator&isExactMatch=true&status=31094501,31094502,31094503&programmePeriod=2021%20-%202027&frameworkProgramme=43108390&order=DESC&pageNumber=1&pageSize=50&sortBy=relevance * Di Marco D., Thabit S., Kotsev A., Christensen A., Minghini M. et al., Open but Not Powerless: Towards a Common Understanding of EU Digital Sovereignty, European Commission Ispra, 2025, JRC144908: https://publications.jrc.ec.europa.eu/repository/handle/JRC144908 foss4g-europe-2026-5678-open-source-digital-sovereignty-and-europe-s-geospatial-future FOSS4G ‘Made in Europe’ Marco MinghiniStefanie Lumnitz en false https://talks.osgeo.org/foss4g-europe-2026/talk/GDZ9SQ/ https://talks.osgeo.org/foss4g-europe-2026/talk/GDZ9SQ/feedback/ Auditorium Talk 2026-06-30T12:00:00+03:00 12:00 00:30 Amid escalating geopolitical tensions and growing dependence on foreign digital infrastructure, digital sovereignty has become an urgent priority for governments, public institutions, and scientific organizations in Europe. Despite marketing terms like *sovereign cloud*, legal frameworks such as the *U.S. CLOUD Act* and *FISA 702* continue to expose European data to extraterritorial access, revealing a structural mismatch between political ambitions, geopolitical threats, and technological reality. This talk explores how open‑source geospatial ecosystems offer a practical, scalable pathway toward genuine digital autonomy. Open source provides transparency, auditability, and the ability to self‑host and adapt tools to local needs and jurisdiction. It enables reproducible analytics, secure handling of sensitive geodata, and long‑term independence from vendor lock‑in. The presentation also discusses viable business models for commercial open source, showing how companies can sustainably build services, consulting, hosting, and innovation on top of open foundations without compromising user sovereignty and by contributing to open source communities. However, achieving sovereignty is not only a technical challenge, it is also a procurement and governance challenge. Current public‑sector tendering practices often unintentionally exclude open‑source solutions through over‑specification, popularity bias, certification requirements, and tight timelines. These structural barriers limit innovation and reinforce dependency on proprietary ecosystems or vendors who market themselves as “open” without adhering to open‑source principles. To address this, the talk argues for the development of a certification or trustmark for genuine open‑source companies, helping public institutions distinguish between truly open providers and those using “open‑washing” to lock customers into non–big‑tech proprietary ecosystems. Organizations such as OSGeo, national QGIS and OSGeo user groups, and broader open‑source communities can play a crucial role in defining such standards, raising awareness among procurement officers, and supporting fair, transparent, sovereignty‑aligned procurement processes. Ultimately, the talk argues that sovereignty is not a checkbox but a long‑term commitment, and that open source is not merely an alternative, but a strategic necessity for Europe’s digital future. foss4g-europe-2026-5682-open-source-for-digital-sovereignty-business-models-trustmarks-and-procurement-reform Hans van der Kwast en false https://talks.osgeo.org/foss4g-europe-2026/talk/LSNDUH/ https://talks.osgeo.org/foss4g-europe-2026/talk/LSNDUH/feedback/ Auditorium Talk 2026-06-30T12:30:00+03:00 12:30 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. 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. foss4g-europe-2026-5648-sustaining-open-source-real-models-real-lessons Building a business with FOSS4G Jeroen Ticheler en false https://talks.osgeo.org/foss4g-europe-2026/talk/VAE9UK/ https://talks.osgeo.org/foss4g-europe-2026/talk/VAE9UK/feedback/ Auditorium Talk 2026-06-30T14:30:00+03:00 14:30 00:30 Europe is undergoing a significant transformation in its policy landscape, driven by the rapid pace of technological advancements, increasing geopolitical pressures, and growing concerns regarding digital sovereignty. Concurrently, the Competitiveness Compass has introduced a simplification agenda aimed at streamlining the regulatory environment and reducing the administrative burden on both businesses and public administrations. The Data Union Strategy, adopted in late 2025, prioritises initiatives that facilitate data access, enhance data reuse for artificial intelligence, and safeguard EU data sovereignty. Within this complex context, the European Commission has proposed a major revision of the INSPIRE Directive, which has been in force since 2007, as part of a broader package of initiatives known as the Environmental Omnibus, designed to simplify environmental legislation. Over the years, INSPIRE has established a unique and comprehensive framework for public sector geospatial data sharing in the EU, serving as a global benchmark for similar Spatial Data Infrastructure initiatives. The proposed revision, currently under negotiation with the Parliament and Council, seeks to modernise and streamline the Directive, aligning it with today’s political and technological landscape that has undergone considerable changes since the Directive's inception two decades ago. This modernisation effort involves simplifying various legal requirements and aligning them with those outlined in the Open Data Directive and its Implementing Act on high-value datasets, which establish an open data regime for several public sector datasets, including geospatial data. The talk will provide a retrospective analysis of INSPIRE's achievements and lessons learned to date, offer a comprehensive overview of the current European policy framework governing geospatial data sharing, and examine the evolution of INSPIRE, covering its past, present, and future developments, while highlighting specific opportunities for stakeholders and identifying key challenges that need to be addressed. foss4g-europe-2026-5681-the-future-of-european-geospatial-data-sharing-in-a-new-policy-landscape FOSS4G ‘Made in Europe’ Marco Minghini en false https://talks.osgeo.org/foss4g-europe-2026/talk/FFVTYV/ https://talks.osgeo.org/foss4g-europe-2026/talk/FFVTYV/feedback/ Auditorium Talk 2026-06-30T15:00:00+03:00 15:00 00:30 Across Europe, open geospatial data is increasingly published through INSPIRE aligned infrastructures, National Access Points (NAPs), and national open data portals. While access has improved significantly, licensing has become one of the most common sources of friction and risk when data is reused across borders, institutions, and use cases. This talk focuses on practical licensing challenges encountered in real European projects. It examines common pitfalls such as mixing share alike and permissive licenses, using non commercial data in public private or downstream contexts, and dealing with national constraints related to redistribution or export control. Particular attention is given to differences in licensing approaches across INSPIRE datasets and NAPs, and how these differences impact interoperability and reuse. Rather than legal theory, the session presents a hands on framework to help practitioners recognize high risk patterns early and design data workflows that remain compliant as datasets evolve. Audience level: Beginner to intermediate Key takeaway: In Europe’s open data ecosystem, licensing is a technical constraint—and should be treated as part of system design, not an afterthought. foss4g-europe-2026-5684-licenses-in-the-real-world-avoiding-share-alike-non-commercial-and-export-control-pitfalls-in-europe Octavian Borcan en false https://talks.osgeo.org/foss4g-europe-2026/talk/N3D8SN/ https://talks.osgeo.org/foss4g-europe-2026/talk/N3D8SN/feedback/ Auditorium Keynote 2026-06-30T16:30:00+03:00 16:30 01:00 "GIS in the Cloud" details the convergence of GIS with Cloud Computing and AI, focusing on Quarticle's AI-ready geospatial solutions. Quarticle provides high-performance geoprocessing and intelligent risk analysis tools, emphasizing operational efficiency, scalability, and cloud independence. The core technical contribution is the use of a Kubernetes (K8s) Operator for automated GeoServer management, transforming it into a cloud-native, self-regenerative service. This Declarative and Reliable architecture uses Custom Resource Definitions (CRDs) and Helm templates for consistency. The operational flow employs GitOps via ArgoCD: changes committed to Git automatically trigger the Operator to manage GeoServer resources through its REST API, ensuring continuous deployment and zero downtime. The keynote highlights the ongoing technological progress in K8s and open-source Foundation Models. foss4g-europe-2026-5891-gis-in-the-cloud Keynote Octavian Iercan en false https://talks.osgeo.org/foss4g-europe-2026/talk/BJENWS/ https://talks.osgeo.org/foss4g-europe-2026/talk/BJENWS/feedback/ Auditorium Talk 2026-06-30T17:30:00+03:00 17:30 00:30 In 2025, the Spanish Supreme Court ruled that source code for a piece of software developed for a Ministry (the BOSCO tool) needed to have its source code released. Nowadays laws are being implemented as computer code; and for a democratic society to function as such, the public needs to be able to know both the letter of the law and the source code of the software used to enforce that law. The BOSCO ruling sets an European precedent for algorithmic transparency and digital sovereignty. foss4g-europe-2026-4904-the-bosco-ruling-government-software-must-be-explainable FOSS4G ‘Made in Europe’ Iván Sánchez Ortega en false https://talks.osgeo.org/foss4g-europe-2026/talk/XRE39X/ https://talks.osgeo.org/foss4g-europe-2026/talk/XRE39X/feedback/ A02 Talk 2026-06-30T10:30:00+03:00 10:30 00:30 For some of us, terminal windows are ubiquitous. Whether we love them or just find them convenient, there's always one open. However, we best know them as grids of text. Can we escape this 80x25 prison and build something that resembles an interactive GIS application? During this talk, we'll take a look at some terminal protocol extensions and libraries in the Rust ecosystem. foss4g-europe-2026-5416-escaping-the-cell-grid-terminal-gis State of software Laurențiu Nicola en false https://talks.osgeo.org/foss4g-europe-2026/talk/DAUWH8/ https://talks.osgeo.org/foss4g-europe-2026/talk/DAUWH8/feedback/ A02 Talk 2026-06-30T11:30:00+03:00 11:30 00:30 **This introductory talk aims at giving a conceptual overview and application examples for using Discrete Global Grid Systems in daily GIS analysis and visualisation tasks.** Near-real-time data, time series data and other spatio-temporal event data are often subject to the need for cartographic visualisation sooner or later, regardless of their intended purpose of analysis. If a web visualisation is planned, data preparation is a challenging task, as a prototypical quick-and-dirty web presentation in the browser quickly reaches its performance limits with large amounts of data. Depending on the application, the raw data volumes can be huge. The provision of raw data via OGC web services also scales poorly with increasing data volumes. This is where aggregation or generalisation of the data becomes necessary. Self-defined grids, official or national grid systems and discrete global grid systems (DGGS) are very well suited for this. Some variants such as the INSPIRE-based [geographical grid for Germany](https://gdz.bkg.bund.de/index.php/default/inspire/sonstige-inspire-themen/geographische-gitter-fur-deutschland-in-lambert-projektion-geogitter-inspire.html), [Uber's hexagonal grid system H3](https://h3geo.org/) and [Google's hierarchical grid system S2](http://s2geometry.io/) will be briefly compared in the presentation. Although web mapping frameworks such as OpenLayers already offer practical methods such as [HexBin](https://viglino.github.io/ol-ext/doc/doc-pages/ol.source.HexBin.html) for creating a hexagon grid for a spatial aggregation of source data, all source data must first be transferred to the client's browser, which — experience has shown — quickly leads to performance problems. Two project examples are giving a glimpse on more effective approaches for efficiently aggregating time series data in the hexagonal grid system H3: - Via post-processing at database level using the PostgreSQL extension [h3-pg](https://github.com/postgis/h3-pg) and - in real time during streaming data processing with Apache Flink by using the [H3 implementation in Java](https://github.com/uber/h3-java). The ultimate goal in both cases is always a high-performance provision of the aggregated result data via OGC web interfaces such as WMS/WFS or API – Maps/API – Features. In this way, results are easily exchangeable and can be used flexibly in analysis tools of various players. Finally, [DGGAL](https://dggal.org/) and [Vgrid](https://vgrid.gishub.vn/) with bindings for Python and QGIS will be recommended as handy open-source tools. We may also take a look at the DGGS working group of the OGC and the OGC standard [API – DGGS](https://www.ogc.org/de/standards/dggs/). The talk aims to present an approach to grid aggregation that is as generic as possible, so that the transferability of this practical methodology to numerous other statistical use cases is conveyed. In particular, traffic data, movement data and sensor data can be elegantly visualised with little effort and prepared for further analysis applications. foss4g-europe-2026-5664-discrete-global-grid-systems-for-spatio-temporal-aggregation-and-visualisation Remote Sensing Michael Scholz en false https://talks.osgeo.org/foss4g-europe-2026/talk/JZNM93/ https://talks.osgeo.org/foss4g-europe-2026/talk/JZNM93/feedback/ A02 Talk 2026-06-30T12:00:00+03:00 12:00 00:30 The PROJ library (https://proj.org) is widely used in GIS and surveying software. Most of the people know it because of its ability to preform projections (v.g. Transverse Mercator or Spilhaus). However PROJ is able to do more things in addition to projections. This talk will go through some of these features, like datum transformations, CRS catalogs (like EPSG, ESRI, IGNF, ...), grid files, geodetic computation, projection distortion factors, transformation pipelines, etc. The presentation will use the page https://jjimenezshaw.github.io/wasm-proj/ to show some of these features. foss4g-europe-2026-5203-proj-is-not-only-about-projections State of software Javier Jimenez Shaw en false https://talks.osgeo.org/foss4g-europe-2026/talk/SSCMFH/ https://talks.osgeo.org/foss4g-europe-2026/talk/SSCMFH/feedback/ A02 Talk 2026-06-30T12:30:00+03:00 12:30 00:30 There's a lot of data out there that is mapped in HEALPix format, like the ERA5 global climate and weather data. And while DGGS formats are nothing new, visualizing them has traditionally required desktop tools and offline workflows. Taking advantage of the latest web technologies (namely deck.gl) we decided to explore this uncharted territory and build an interactive HEALPix viewer that runs entirely in the browser. This opens the door to craft unique and engaging experiences for exploring HEALPix data and perform analysis. Visualizing temporal data can become as simple as hitting a play button, allowing users to discover and study patterns previously only accessible to sophisticated tools. In this talk I'll share our journey into bringing HEALPix to the web while showing the potential this new technology has to change how HEALPix data is accessed and explored. foss4g-europe-2026-5365-tiling-the-earth-interactive-healpix-in-the-browser State of software Daniel da Silva en false https://talks.osgeo.org/foss4g-europe-2026/talk/9XMLKP/ https://talks.osgeo.org/foss4g-europe-2026/talk/9XMLKP/feedback/ A02 Talk 2026-06-30T14:30:00+03:00 14:30 00:30 Subsea cable damage accounts for over 80% of insurance claims in offshore wind, making cable burial design a critical and high-risk decision. Despite this, traditional 2-dimensional cable burial risk assessment (CBRA) methods were largely non-spatial, relying on spreadsheets, manual interpolation, and expert judgement. These approaches are difficult to reproduce, error-prone, and inefficient when route changes occur. This talk presents a fully geospatial CBRA workflow built using PostGIS, transforming a historically linear and manual process into a scalable, data-driven pipeline. We demonstrate how Automatic Identification System (AIS) vessel data is processed from raw point observations into tracklines and density rasters, and how post-construction rerouting scenarios can be modelled to estimate shifts in marine traffic patterns. These spatial outputs are integrated with geological ground models and seabed levels to determine cable burial depth requirements along subsea cable routes. Using core PostGIS capabilities such as spatial indexing (GiST), relational predicates (ST_Intersects) and geometric analysis (e.g. ST_HausdorffDistance), we create a reproducible workflow that supports rapid iteration as new data becomes available. The approach reduces duplicated effort, improves consistency, and enables more transparent decision-making in a high-cost engineering context. It has already been applied to multiple offshore wind developments, including the floating offshore wind farm, Green Volt. The talk will explore how this workflow extends into 3-dimensional modelling through voxel generation, highlighting opportunities for further integration with netCDF outputs and open geospatial ecosystems. Attendees will gain insight into how PostGIS can be used to modernise risk modelling workflows, where data availability can be variable. We conclude by addressing that while the analytical pipeline contains as many open source elements as practicable, the results are currently shared using the Esri JavaScript software development kit (SDK) to support wider stakeholder engagement. Both the benefits and remaining challenges in transitioning fully to open-source solutions will be discussed, with invitation to collaboration on bridging these gaps. foss4g-europe-2026-5477-reducing-subsea-cable-risk-with-postgis-a-cbra-workflow-for-offshore-wind Hannah Jukes en false https://talks.osgeo.org/foss4g-europe-2026/talk/RPPKRR/ https://talks.osgeo.org/foss4g-europe-2026/talk/RPPKRR/feedback/ A02 Talk 2026-06-30T15:00:00+03:00 15:00 00:30 The Province of South Holland manages one of the most densely packed, data-rich regions in Europe. Housing pressure, nitrogen deposition, flooding risk, ageing infrastructure, biodiversity loss — the questions are urgent, and the data to answer them mostly already exists. It's public, it's free, and almost none of it talks to anything else. We built something to fix that. Over the past two years, we've assembled a spatial data warehouse for Zuid-Holland: an H3 geo-datacube at resolution 9, combining six years of Dutch open datasets — demographics and housing (CBS), land use and nature (LGN), water quality (IHW/KRW), air quality (RIVM), ground height (AHN), noise exposure, and accessibility distances. Every dataset on the same hexagonal grid. Every hexagon its own small story about a patch of land. Then we gave it a voice. A natural-language assistant — built entirely on open source tools — lets policy advisors, planners, and analysts ask questions in plain Dutch and get a map back in seconds. No SQL. No data wrangling. No waiting for a colleague who knows which table holds what. "Where did nature expand while population shrank between 2018 and 2023?" becomes a map. "Which areas combine flooding risk with high housing pressure and poor accessibility?" becomes a map. Decisions that used to take a week of preparation start with a conversation. The stack is fully open: LangGraph for the AI workflow, DuckDB for query execution, FastAPI for the backend, Deck.gl and MapLibre GL JS for rendering. The warehouse itself is built on Delta Lake — a living system, not a static file — designed to grow as new provincial datasets are onboarded. This talk covers the architecture, the hard-won lessons (LLM hallucinations hitting production queries is a fun problem to debug), and a live demo against the actual provincial data warehouse. We'll show what the hexagons reveal about Zuid-Holland that spreadsheets never could — and what it looks like when a province can finally ask itself the right questions. All code is open source. The pattern is replicable. The data is already yours. Stack: H3 · DuckDB · LangGraph · FastAPI · Deck.gl · PDOK · Delta Lake foss4g-europe-2026-5689-asking-a-province-a-question-llms-h3-and-open-dutch-geodata Use cases & applications Thijs Oosterhuis en false https://talks.osgeo.org/foss4g-europe-2026/talk/QZN7Q3/ https://talks.osgeo.org/foss4g-europe-2026/talk/QZN7Q3/feedback/ A02 Talk 2026-06-30T15:30:00+03:00 15:30 00:30 The rise of large language models is reshaping how we interact with information, and geographic data should be no exception. In this exploratory talk, we present IGN’s early work on a Geo‑Context MCP, a new interface designed to make France’s sovereign geodata directly accessible to AI agents. The goal is simple but ambitious: allow intelligent systems to query, understand, and reason over geographical data and OGC services—just as easily as humans do today. We will walk through the first experiments that connect AI agents to WFS endpoints, structured geographic datasets, and other key services from the Géoplateforme. By exposing geodata through a machine‑native contextual layer, IGN aims to lower the barrier between spatial information and AI‑driven analysis. We hope this can open the door to new forms of automated geoprocessing, enriched decision‑making, and dynamic geospatial exploration. Finally, this talk invites the community to imagine what comes next. IGN’s initiative is intentionally open, experimental, and collaborative—an invitation to researchers, developers, and public institutions to help prototype the future of Geo‑AI interaction. How can we make geodata more “intelligible” to agents ? Which tools, standards, or abstractions should we build together? This session is a first step toward that shared exploration. foss4g-europe-2026-5438-beyond-maps-prototyping-a-geo-context-layer-for-the-ai-driven-future lavenantRémi Ferrier en false https://talks.osgeo.org/foss4g-europe-2026/talk/SM7QL9/ https://talks.osgeo.org/foss4g-europe-2026/talk/SM7QL9/feedback/ A11 Talk 2026-06-30T10:30:00+03:00 10:30 00:30 PostgreSQL and PostGIS remain the default choice for geospatial data management. Their strict ACID compliance and extensive spatial capabilities cover most standard use cases. However, scaling a dynamic, multi-tenant WebGIS platform, where users continuously upload custom datasets, reveals the scalability boundaries of traditional relational architectures. During the development of the WebGIS platform, [GOAT](https://github.com/plan4better/goat), we encountered these constraints directly. Initially, all spatial data was stored in a monolithic PostgreSQL database. To prevent the system catalogs from overloading due to creating thousands of user-specific tables, we consolidated datasets into multi-tenant tables grouped by geometry type. This stabilized the table count, but as platform adoption increased, individual tables rapidly exceeded 50GB. The resulting volume of data and indexes degraded query performance and complicated maintenance. Attempting to resolve this computationally, we implemented Citus to shard and distribute the data. While this approach provided vertical scaling, managing a distributed PostgreSQL cluster introduced significant operational complexity. Furthermore, the application logic required extensive refactoring to accurately route queries using distribution columns. When we evaluated horizontal scaling through read replicas, the infrastructure cost of duplicating a multi-terabyte database proved prohibitive for a small team. Even with a strong preference for the PostGIS ecosystem, we were maintaining a fragile system that was slow to back up and difficult to sustainably host. A common structural response to this challenge is separating storage from compute via a lakehouse architecture. We evaluated managed solutions like BigQuery and Databricks, which offer expanding spatial support. Yet, for an open-source project, these platforms present distinct disadvantages: high costs, vendor lock-in, and an inability to be self-hosted, bypassing data sovereignty requirements for certain clients. Focusing on open-source frameworks, Apache Iceberg stood out as a mature, production-ready standard. However, we ultimately opted to test a novel framework still in its early stages: DuckLake. DuckLake provides a strict separation of concerns. It manages metadata within a lightweight relational database while storing the actual data in highly compressed Parquet files. Built around DuckDB, it allows direct access to DuckDB's native spatial functions, including vector tile generation. We integrated DuckLake despite its beta status, primarily to test DuckDB's minimal storage requirements, strong analytical capabilities, and open-source foundation. The infrastructural shift was measurable. Transitioning large-scale system layers—such as nationwide street networks—and user data from PostGIS to Parquet reduced our total storage footprint by at least 90%. This was achieved through a combination of Parquet's columnar compression and the elimination of traditional database indexes, which previously accounted for a massive portion of our storage. Currently, our data is stored on scalable volumes and backed up to S3-compatible object storage. Analytical compute has been decoupled and is managed by Windmill, orchestrating background Python jobs to execute DuckDB queries on demand. While a minor subset of highly specific queries show slight performance regressions compared to PostGIS, the vast majority execute significantly faster. This shift is particularly noticeable during large-scale spatial analytics, as columnar storage outperforms row-based continuous reading when scanning and aggregating massive datasets. Because Parquet files are highly compressed and immutable, they cannot be edited in place. To support data mutations, DuckLake writes edits to separate delta files, which are dynamically merged during query execution. Furthermore, DuckLake can save small edits directly to PostgreSQL using inlining. The underlying metadata layer, responsible for tracking these file shifts, is managed by PostgreSQL. Since PostgreSQL is optimized for rapid transactions, this hybrid approach allows us to retain the metadata management of a relational database while utilizing the storage efficiency of Parquet for massive spatial datasets. Additionally, DuckLake provides built-in support for versioning and time travel, maintaining a history of data mutations. While analytical workloads benefited from this architecture, we encountered distinct challenges regarding the high-frequency queries required by web mapping. Although Parquet files can be optimized through sorting and partitioning, they are not naturally designed for the low-latency, point-lookup access patterns required by vector tile or feature services. Notably, standard Parquet implementations do not inherently support spatial indexes, meaning spatial queries often trigger full file scans rather than targeted reads. Specifically, generating dynamic vector tiles requires evaluating and filtering entire Parquet files for every incoming user request, creating a severe performance bottleneck on larger datasets. Additionally, maintaining continuous access to the PostgreSQL metadata layer via DuckDB frequently exhausted database connection limits, which introduced noticeable latency during initial query execution. To mitigate these issues, we introduced connection pooling using PgBouncer and caching strategies via Redis. However, these solutions only masked the underlying problem for large datasets. Ultimately, we adopted a hybrid vector tile architecture to bypass these limitations: we rely on static vector tiles generated by Tippecanoe for base layers, reserving DuckDB's dynamic, on-the-fly vector tile generation strictly for volatile, filtered, or actively edited datasets. During the development of GOAT, testing DuckLake demonstrated both its current utility and its limitations. While the framework is still in its early stages and requires architectural workarounds—like our hybrid vector tile setup—to handle low-latency web mapping, it functions as a practical complement to PostgreSQL and PostGIS for heavy analytical workloads. The upcoming native support for GeoParquet is expected to address several of the current performance bottlenecks by introducing geometry as a native Parquet type, which should improve execution times for spatial bounding box and intersection queries. For analytics-heavy platforms managing multi-tenant data, the combination of DuckLake and DuckDB offers an alternative approach to scaling spatial infrastructure while maintaining manageable server costs. foss4g-europe-2026-5538-ducklake-a-scalable-data-lakehouse-for-web-mapping Majk Shkurti en false https://talks.osgeo.org/foss4g-europe-2026/talk/33YRC7/ https://talks.osgeo.org/foss4g-europe-2026/talk/33YRC7/feedback/ A11 Talk 2026-06-30T11:30:00+03:00 11:30 00:30 An overview of the osm2pgsql commandline tool. The talk shows the basic features, the latest developments and demonstrates how to use it for importing OpenStreetMap data into a PostgreSQL / PostGIS database. foss4g-europe-2026-5693-osm2pgsql-processing-openstreetmap-data-with-postgis State of software Jakob Miksch en false https://talks.osgeo.org/foss4g-europe-2026/talk/XWGPXA/ https://talks.osgeo.org/foss4g-europe-2026/talk/XWGPXA/feedback/ A11 Talk 2026-06-30T12:00:00+03:00 12:00 00:30 Modern Enterprise GIS is nowadays built with FOSS4G softwares. Many organizations are in the middle of transition to build EntrepriseGIS and some organizations are still planning to move from old fashioned closed source software implementations to open source solutions. FOSS4G based Enterprise GIS is commonly built with PostGIS based GIS database, QGIS desktop software, mobile applications (like QField and Mergin Maps) and web applications servers (like QGIS Server, Geoserver). The most crucial and enduring component of an Enterprise GIS solution is the database. This presentation will cover how to design, build and maintain a GIS database for Enterprise usage. The presentation also includes best practices and recommendation of tools for Enterprise GIS database management. Enterprise GIS is an organization-wide suite of interoperable GIS software used to manage and process geospatial information. Following the basic principles of enterprise architecture, its structure is based on three distinct layers: the User Interface, the Application Server, and Data Storage. This presentation will focus on how to build a robust and efficient Data Storage layer with PostGIS database. This presentation will cover the following topics: - GIS database design and modelling with open source solutions - Best practices for GIS data modelling and versioning of data models - Organise and manage the Enterprise GIS database in PostgreSQL cluster - Best practices for access privileges in Enterprise GIS database - QGIS related data storage to PostGIS - Manage database connections and credentials in QGIS This presentation is essential for GIS database managers, GIS managers, and all users of Enterprise GIS solutions. Attendees will be equipped with the fundamental knowledge and practical best practices — including valuable tips-and-tricks — required to effectively build and maintain a robust Enterprise GIS database. foss4g-europe-2026-5515-enterprise-gis-building-and-maintaining-the-database Pekka Sarkola en false https://talks.osgeo.org/foss4g-europe-2026/talk/Z7QGGL/ https://talks.osgeo.org/foss4g-europe-2026/talk/Z7QGGL/feedback/ A11 Talk 2026-06-30T14:30:00+03:00 14:30 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-europe-2026-5398-state-of-geonode State of software Giovanni AllegriMattia Giupponi en false https://talks.osgeo.org/foss4g-europe-2026/talk/HPFGGP/ https://talks.osgeo.org/foss4g-europe-2026/talk/HPFGGP/feedback/ A11 Talk 2026-06-30T15:00:00+03:00 15:00 00:30 Panoramic images are a great way for users to explore a location without having to be there in person. They give a detailed view of the surroundings and can be used for various purposes such as site inspections and urban planning. By leveraging the GIS capabilities of the free and open-source VC Map framework and the 3D visualization power of CesiumJS, we can create an open-source tool that goes beyond simply viewing panoramic images. Our goal is to provide a seamless experience for users to explore, analyze and navigate their 3D, 2D and panoramic data within a single application. Part of this work included developing a tiled data specification with the goal of minimizing the need for file transfers and boosting performance. This specification is built on cloud-ready OGC standards - Cloud Optimized GeoTIFF (COG) and FlatGeobuf, allowing an easy creation using GDAL. The images are rendered directly into the 3D scene, which allows to blend in visualizations of GIS data from the 3D scene. To enable analytical tools, such as measurements, we use additional depth information. With this development we aim to demonstrate the power of using and developing open source tools with open data standards. foss4g-europe-2026-5410-vc-map-panorama-high-resolution-panoramic-images Use cases & applications Ben Kuster en false https://talks.osgeo.org/foss4g-europe-2026/talk/TJKEE8/ https://talks.osgeo.org/foss4g-europe-2026/talk/TJKEE8/feedback/ A11 Talk 2026-06-30T15:30:00+03:00 15:30 00:30 The amount and the diversity of Earth Observation data and the multiple ways to get it, make accessibility to the data quite difficult. EODAG offers an Open-Source Python or Command Line Interface client that federates and unifies access to cross-providers Earth Observation data. It offers a solution that simplifies access to heterogeneous data from various providers with different APIs, through a unique interface based on STAC. The design and open-source approach of EODAG allow for a balanced eased extensibility toward other data sources and data types. EODAG capabilities can be extended through the following related projects: - EODAG-Labextension, a Jupyterlab extension allowing users to search and browse for remote sensed imagery directly from JupyterLab. - EODAG-Cube for having direct access to data as Xarray datasets. - STAC-FastAPI-EODAG, EODAG backend for stac-fastapi, which combines the capabilities of EODAG and STAC FastAPI to provide a powerful, unified API for accessing Earth observation data from various providers. In this talk, we will present the main functionalities of EODAG and related projects. We will focus on updates since our previous FOSS4G workshop, particularly the use of STAC for API parameters and results properties. We will also describe the latest features and upcoming developments. foss4g-europe-2026-5425-eodag-earth-observation-data-access-gateway Remote Sensing Sylvain BrunatoAubin Lambaré en false https://talks.osgeo.org/foss4g-europe-2026/talk/UV8LCP/ https://talks.osgeo.org/foss4g-europe-2026/talk/UV8LCP/feedback/ A12 Talk 2026-06-30T11:30:00+03:00 11:30 00:30 mapchete EO extends mapchete with abstractions and utilities to read from Earth Observation (EO) archives, with a primary focus on Sentinel-2 data. While mapchete provides a tile-based execution model for raster and vector processing, mapchete EO enables reading multidimensional arrays (time series) from well known data archives. Class-based abstractions for handling Sentinel-2 products were engineered to also enable usage outside of the mapchete context. They provide a unified interface to various data and metadata archives to automatically mask data using all available metadata masks (SCL, L1C, etc.) as well as to apply BRDF correction while reading the datza. The second part of the talk focuses on operational experience from processing Sentinel-2 data at global scale for the EOxCloudless product line. At this scale, the system has to have multiple layers of fallbacks and retries in order to accomodate I/O related and temporary failures. Additional challenges arise when processing data across the antimeridian, where data coverage is not consistent between various archives. These edge cases expose limitations that are not apparent in smaller-scale workflows and require careful handling within global processing pipelines. The presentation will outline these challenges and discuss their implications for the design of robust, large-scale Sentinel-2 processing pipelines within an open source framework. foss4g-europe-2026-5528-mapchete-eo-abstractions-for-sentinel-2-data-access-and-processing Joachim Ungar en false https://talks.osgeo.org/foss4g-europe-2026/talk/FZYGGU/ https://talks.osgeo.org/foss4g-europe-2026/talk/FZYGGU/feedback/ A12 Talk 2026-06-30T12:00:00+03:00 12:00 00:30 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. foss4g-europe-2026-5473-serving-earth-observation-data-with-geoserver-addressing-real-world-requirements Andrea AimeSimone Giannecchini en false https://talks.osgeo.org/foss4g-europe-2026/talk/YSDMSM/ https://talks.osgeo.org/foss4g-europe-2026/talk/YSDMSM/feedback/ A12 Talk 2026-06-30T12:30:00+03:00 12:30 00:30 The European Space Agency (ESA) has adopted a variety of open-source software tools to manage, visualize, and distribute planetary data, with a particular emphasis on Mars. These tools are essential for both internal operations and for providing crucial data access to the global scientific community. Below, we detail the use of these technologies, collaboration on open-source projects, and the underlying GIS architecture developed by the Planetary Science Archive (PSA). [Link](https://psa.esa.int/psa) ## Tools Used 1. **OpenLayers**: - **Functionality**: A JavaScript library for creating interactive maps in web browsers. - **Application**: Used to build web user interfaces that allow scientists to visualize geospatial data of Mars and other planets, offering an intuitive and accessible platform for the exploration and analysis of planetary data. 2. **GeoServer**: - **Functionality**: An open-source map server that enables the sharing and editing of geospatial data. - **Application**: Used to serve spatial data via standard protocols like WMS (Web Map Service). This facilitates the visualization of footprints with different base maps. 3. **Three.js**: - **Functionality**: A JavaScript library for creating 3D graphics in web browsers. - **Application**: It is employed to generate three-dimensional visualizations of the Rosetta comet. 4. **PostgreSQL and PostGIS**: - **Functionality**: PostgreSQL is an open-source relational database management system, and PostGIS is an extension that adds support for geographic objects. - **Application**: Are used to store and manage complex geospatial data. PostGIS allows for advanced spatial queries, facilitating the analysis of large volumes of geospatial data and its integration with other GIS tools like GeoServer. ## Collaborative Projects and Data Access 1. **Astroquery**: - **Description**: A Python library that facilitates access to online astronomical databases. - **Collaboration**: ESA contributes to Astroquery to ensure that planetary data is easily accessible to researchers. This includes data from planetary exploration missions and astronomical observations, integrating these data into scientific analyses efficiently. 2. **Antimeridian**: - **Description**: Tool for processing spatial data crossing the antimeridian (the 180° line of longitude).. - **Collaboration**: Open Source project, and the PSA plans to collaborate with the project by contributing code. This tool is crucial for planetary data where coordinates can be extended beyond the traditional range of 0° to 180° longitude, allowing for continuous and accurate representation of planetary maps.. ## New Interface and GIS Architecture ESA has developed a new interface for the Planetary Science Archive, integrating the aforementioned tools into a cohesive and user-friendly platform. This interface allows scientists to: - **Explore Interactive Data**: Navigate through interactive maps of Mars, Phobos and other planets, applying filters and visualizing different layers of geospatial data. Users can overlay geological, topographical, and spectral data layers to gain a more comprehensive view of the terrain and use the different functionalities, such as changing the projection (polar, equirectangular), extracting information by region of interest. - **3D Visualization**: Thanks to Three.js, users can explore the the 67P(Churyumov-Gerasimenko) comet in 3D for the Rosetta mission, rotate, and zoom into features for more detailed analysis. Ultimately, we use Three.js to represent irregular bodies such as comets, asteroids, and asteroids. - **Real-Time Data Access**: Researchers can access the latest information and perform real-time queries to obtain specific data according to their needs. - **Data Download**: Scientists can download datasets directly from the interface for use in their own analyses and studies, selecting and downloading specific subsets of data based on defined search criteria. ## GIS Architecture The GIS architecture behind this new interface relies on a robust combination of open-source technologies: - **GeoServer Base Maps**: Acts as the distributor of base maps of Mars, Phobos, Cassis. They are cached using GWC to optimize access in all available projections. - **Frontend with OpenLayers and Three.js**: Provides 2D and 3D visualization capabilities, offering a rich and interactive user experience. OpenLayers is used for 2D interactive map visualization, while Three.js is employed to generate three-dimensional visualizations of planetary surfaces. - **Database with PostgreSQL and PostGIS**: Used to store and manage complex geospatial data. PostgreSQL and PostGIS enable advanced spatial queries, facilitating the analysis of large volumes of geospatial data and its integration with other GIS tools. - **Integration with Data Access Tools**: Projects like Astroquery and Antimeridian are integrated to facilitate the access and manipulation of specific data, solving complex issues like the management of data crossing the antimeridian. This integration allows scientists to access and analyze planetary data more efficiently and accurately. ## Benefits for the Scientific Community The use of advanced technologies and a robust GIS architecture developed by ESA offers several significant benefits for planetary research: - **Open and Transparent Access**: Although the code is not public, ESA uses open-source tools that ensure data and resources are available to the entire scientific community. This promotes collaboration and knowledge sharing, allowing researchers to access information without restrictions and work together more efficiently. Another benefit for the scientific community is to be able to cross different instruments/missions in a single interface, e.g., give me all the CaSSIS and HRSC data of this particular crater. For more information about ESA projects, you can visit their [GitHub repository](https://github.com/esa). - **Solutions to Specific Problems**: Tools like Antimeridian [Antimeridian GitHub](https://github.com/gadomski/antimeridian) address unique technical challenges, ensuring precise and continuous representation of planetary data. This facilitates the analysis and interpretation of geospatial data, ensuring that visualizations and maps are accurate and reliable. ## Conclusion The adoption of open-source software and the development of an advanced GIS architecture enable ESA to offer a powerful and accessible platform for planetary research. This benefits not only its own scientists but also the global scientific community, promoting knowledge sharing and collaboration in the exploration of the Solar System. Tools such as OpenLayers, GeoServer, Three.js, PostgreSQL, and PostGIS, along with collaborative projects like Astroquery and Antimeridian, are fundamental for the efficient management and precise visualization of planetary data. With all this, the summary of the talk is to show how free software is used in the PSA for planetary data and more specifically in Mars data. foss4g-europe-2026-5004-use-of-open-source-software-in-the-esa-planetary-science-archive Use cases & applications Fran Raga en false https://talks.osgeo.org/foss4g-europe-2026/talk/KJ3MWT/ https://talks.osgeo.org/foss4g-europe-2026/talk/KJ3MWT/feedback/ A12 Talk 2026-06-30T14:30:00+03:00 14:30 00:30 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. foss4g-europe-2026-5569-lessons-from-running-geoserver-at-scale Andrea AimeSimone Giannecchini en false https://talks.osgeo.org/foss4g-europe-2026/talk/NCJR9L/ https://talks.osgeo.org/foss4g-europe-2026/talk/NCJR9L/feedback/ A12 Talk 2026-06-30T15:30:00+03:00 15:30 00:30 For any geospatial platform, the ability to serve imagery at scale is the ultimate "stress test." When our team at UP42 began building a production-grade WMTS service, we quickly realized that moving from a functional setup to a high-performance one requires more than just adding more hardware. This talk shares our iterative journey of migrating from a "Vanilla" GeoServer architecture to a microservices-based GeoServer Cloud environment, and the systematic load testing that guided every decision along the way. We will walk through our "detective-style" approach to performance tuning. Using Apache JMeter to simulate heavy production loads, we treated our infrastructure as a series of integration points where bottlenecks could hide. Rather than a smooth transition, the move to a cloud-native architecture revealed a new landscape of challenges that required us to look deeper into the system than we had ever anticipated. Throughout our testing phases, we uncovered a variety of hidden performance killers, including: - Storage Hurdles: How standard cloud-mount solutions struggled with tile-writing workloads and why native cloud storage plugins became essential. - Concurrency Caps: The realization that default configurations for thread limits and traffic control are often too conservative for modern cloud environments. - The Proxy Trap: How internal communication between services can become a bottleneck even when individual components are performing well. - Resource Optimization: The relationship between CPU/Memory allocation and the ability to handle parallel tasks like simultaneous seeding and streaming. This presentation is a practical guide for anyone looking to push GeoServer beyond its default limits. We will share our "battle map" for isolating bottlenecks, bypassing load balancers for diagnostic testing, and the critical importance of keeping load testing continuous as your architecture evolves. Key Takeaways - The Migration Reality: GeoServer Cloud offers the foundation for horizontal scaling, but it requires a specialized tuning strategy compared to standalone instances. - Systematic Isolation: Learn how to test individual microservices (GWC, Gateway, etc.) in isolation to pinpoint exactly where latency is introduced. - Visibility Matters: The importance of combining log analysis, thread dumps, and performance metrics to solve "silent" performance issues. - End-to-End Testing: Why you must test the full integration path early to find bottlenecks that only appear under high concurrency. foss4g-europe-2026-5447-scaling-geoserver-from-vanilla-architecture-to-cloud-performance-optimization Jan ChristianMatheus Pinheiro dos Santos en false https://talks.osgeo.org/foss4g-europe-2026/talk/MLDRHM/ https://talks.osgeo.org/foss4g-europe-2026/talk/MLDRHM/feedback/ A13 Talk 2026-06-30T10:30:00+03:00 10:30 00:30 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 foss4g-europe-2026-5571-mastering-security-with-geoserver-geofence-and-openid Andrea Aime en false https://talks.osgeo.org/foss4g-europe-2026/talk/SPFHGR/ https://talks.osgeo.org/foss4g-europe-2026/talk/SPFHGR/feedback/ A13 Talk 2026-06-30T12:00:00+03:00 12:00 00:30 The FOSS4G Observatory is an initiative that started 10 years ago in quite a different world. In 2016, we were setting out to better understand what are the open source solutions in the geospatial realm, following the technological progress to answer the intimidating challenges on the horizon. Stable and operational stacks for serving impossible amounts of satellite data to thousands of users simultaneously, allowing complex processing and fast visualisations, integration of numerous types of data and state of the art cartographic digital representations become vital. Years have passed and the FOSS4G observatory increased, following the vast and fast expansion of the FOSS4G ecosystem, documenting almost 600 open source projects and providing the international community (not only the geospatial one) a service that would allow a better, clearer understanding of the open source software available for a specific task, its licenses, standard compliances, dependencies, allowing objective comparisons based on git-related information, and more. Today, our world is quite different than the one a decade ago. Challenges, of all kinds have significantly increased, but so did the potential of tools and data to answer them. In this talk, the presenter will walk you through the vastness of the FOSS4G ecosystem, seen though the objective view of the Observatory, describing the various indicators and providing a potential answer on why and how the open source model has contributed to the development of geospatial technologies underpinning major European initiatives, such as INSPIRE or the Copernicus Program. foss4g-europe-2026-5696-charting-the-geospatial-commons-a-decade-of-the-foss4g-observatory Open community Codrina Ilie en false https://talks.osgeo.org/foss4g-europe-2026/talk/LS7QCJ/ https://talks.osgeo.org/foss4g-europe-2026/talk/LS7QCJ/feedback/ A13 Talk 2026-06-30T12:30:00+03:00 12:30 00:30 The Géoplateforme is the new national infrastructure for geographic data in France, designed to offer administrations a unified, scalable, and sovereign environment for storing, distributing, and visualising geospatial information. It provides a full suite of mutualized services—from secure hosting and high‑performance data distribution to ready‑to‑use visualization tools—that allow public bodies to focus on their missions rather than on infrastructure. On top of these core capabilities, the platform also delivers reference geocoding and reverse‑geocoding, altimetry services, and route and itinerary computation, making it a comprehensive ecosystem for producing and consuming geodata at national scale. This infrastructure guarantees sovereign and secure access to geographical data and maps without relying on Gafam services. To build this platform, we relied heavily on open‑source technologies, combining mature, community‑driven components with tools specifically developed at IGN to meet national requirements. Finally, we are committed to open‑sourcing the code behind our infrastructure and progressively sharing the building blocks that power the Géoplateforme and cartes.gouv.fr. Our strategy is not only to publish code, but to build a real community around it, inviting administrations, researchers, companies, and contributors to shape its evolution. By opening the doors to collaborative development, we aim to create a sustainable and transparent geospatial commons for France—one that grows through shared expertise, pooled investment, and a collective ambition to strengthen national geodata services through open innovation. foss4g-europe-2026-5436-powering-france-s-maps-the-open-tech-behind-geoplateforme-cartes-gouv-fr FOSS4G ‘Made in Europe’ lavenantRémi Ferrier en false https://talks.osgeo.org/foss4g-europe-2026/talk/VKNKM3/ https://talks.osgeo.org/foss4g-europe-2026/talk/VKNKM3/feedback/ A13 Talk 2026-06-30T14:30:00+03:00 14:30 00:30 Managing water supply networks requires coordinating between infrastructure data, hydraulic analysis, and operational records. When GIS data, hydraulic models, and maintenance attributes are stored in separate and different systems without interoperability between them, it becomes difficult to handle a consistent overview of network conditions and operations. To address this, Consortis Geospatial introduces MAPtheYA: an advanced geospatial platform that bridges these gaps by providing an end-to-end solution for unified water network management. MAPtheYA, a name derived from the acronym of the Greek Water Utility Services (DEYA), is an online information system for managing water supply networks. It visualizes the hydraulic network and the technical characteristics of each asset. MAPtheYA provides search capabilities for network elements and allows multiple users to edit the network. Thus, the utility’s technical staff can supervise, expand, and modify the hydraulic network. The platform integrates EPANET hydraulic modeling to allow technical teams to move beyond static geometry into simulation. By calculating flows, pressures, and tank levels over time, MAPtheYA supports complex hypothetical scenarios. The presentation will explain how operators can simulate valve closures, pipe failures, or changes in pressure zones to assess their impact on their wider water network. Results are then presented through thematic maps and graphs for both daily operations and long-term strategic planning. The next set of capabilities planned is for MAPtheYA to manage operational data such as the recording of technical inspections, fault history, interventions, and ongoing construction projects. This will create a traceable digital record for every component of the infrastructure and allow organizations to have a full view of their network. The platform will offer a full set of RESTful APIs allowing interoperability with existing systems such as SCADA platforms, enterprise resource planning (ERP) systems, consumer management systems (CMS), or IoT devices, through its open architecture. This integration enables managers to access information such as consumption data or sensor alerts directly within the GIS environment, supporting more coordinated monitoring and informed decision-making across the water network. The current work-in-progress interface will be introduced, presenting the main workflows and the general direction of the user interface, which can be divided into three pillars. Three main aspects will be explained: the editing and expansion of the hydraulic network, the modeling capabilities based on EPANET simulation and the monitoring of the hydraulic network’s state using sample data from IoT and hydrometric stations. foss4g-europe-2026-5324-maptheya-a-unified-gis-ecosystem-for-the-smart-water-network-management Use cases & applications Stathis PetridisDimosthenis ParadeisisAndreas Gkaravelis en false https://talks.osgeo.org/foss4g-europe-2026/talk/NLX383/ https://talks.osgeo.org/foss4g-europe-2026/talk/NLX383/feedback/ A13 Talk 2026-06-30T15:00:00+03:00 15:00 00:30 The IBERGIS platform integrates the 1D/2D hydrodynamic model of IBER and the SWMM urban drainage model into a single GIS environment inside QGIS, enabling an efficient workflow for urban flood analysis. Using a coupled 1D/2D approach, both the drainage network and the surface runoff are modelled, with depths, velocity, water elevation and extents obtained as outputs, which can be visualised directly from the GIS interface. This integration enables rapid interpretation of critical areas and automated post-processing without external tools. These results demonstrate how GIS-based flood modelling can support urban planning and climate adaptation decisions. The applied case of Manresa provides a replicable workflow for municipalities seeking to enhance urban resilience through spatially integrated hydraulic modelling. foss4g-europe-2026-5440-gis-based-1d-2d-flood-modelling-with-ibergis-a-replicable-workflow-for-urban-climate-resilience David Cano en false https://talks.osgeo.org/foss4g-europe-2026/talk/ZSUR83/ https://talks.osgeo.org/foss4g-europe-2026/talk/ZSUR83/feedback/ A13 Talk 2026-06-30T15:30:00+03:00 15:30 00:30 Snowpack plays a fundamental role in alpine and periglacial environments, acting as a key regulator of surface and subsurface processes. Beyond its well-known hydrological importance as a seasonal water reservoir, snow exerts a strong control on ground thermal regimes by functioning as an insulating layer that decouples near-surface ground temperatures from atmospheric forcing. This insulation effect influences permafrost occurrence, stability, and degradation, particularly in marginal periglacial environments such as those found in the Southern Carpathians. At the same time, snow cover modulates biological activity by controlling soil temperature, moisture availability, and the duration of the growing season, thereby shaping alpine ecosystem dynamics. Accurately characterizing snowpack properties, such as depth, density, and persistence is therefore essential for understanding coupled cryospheric, hydrological, and ecological processes in mountain regions. However, capturing snow variability in complex terrain remains challenging due to strong spatial heterogeneity driven by topography, wind redistribution, and micro-scale surface conditions. These challenges are further exacerbated in regions such as the Romanian Carpathians, where the availability of in situ meteorological observations is limited, particularly at high elevations and in remote alpine environments. The lack of dense and continuous meteorological measurements constrains the direct characterization of snow–climate interactions and limits the applicability of traditional observation-based approaches. While climate reanalysis products provide continuous large-scale atmospheric forcing, their coarse spatial resolution limits their direct use in mountainous environments. Conversely, field observations and high-resolution surveys, such as UAV-based measurements, provide detailed local information but are spatially limited and episodic. Bridging these scales requires reproducible workflows that integrate climate data, physically based modeling, and high-resolution observations within a coherent geospatial framework. This contribution presents an open geospatial workflow for climate-driven snow modeling in alpine terrain, linking climate downscaling, physically based snowpack simulation, and UAV-based observations. The workflow integrates freely available hourly climate reanalysis data from the Copernicus Climate Data Store (ERA5), including both single-level and pressure-level variables, with topography-aware downscaling using the open-source TopoPyScale tool. Implemented in a reproducible environment using Python and Ubuntu via Windows Subsystem for Linux (WSL), the workflow transforms coarse-resolution atmospheric forcing (~31 km) into terrain-informed local-scale inputs by incorporating high-resolution digital elevation models (DEMs) and its derived morphometric parameters such as elevation, slope, aspect, and sky-view factor, as well as horizon-based radiation corrections. The downscaled climate forcing is subsequently used to drive snowpack simulations using the SURFEX–Crocus model developed by Météo-France. While the model is distributed under an open-source license with controlled access, it can be readily obtained for research purposes. Within this workflow, SURFEX–Crocus is employed to simulate detailed snowpack evolution at both point-based locations and clustered terrain representations. The model provides a comprehensive set of snowpack variables, including snow depth, snow water equivalent (SWE), snow temperature profiles, density, stratigraphy, hardness, and snow microstructural properties such as grain size and shape. These outputs enable a process-based representation of snow accumulation, metamorphism, and melt, offering insights into both seasonal dynamics and interannual variability. To demonstrate the integration of model outputs with observational data, UAV-derived snow depth is used as a high-resolution reference dataset. Repeated UAV surveys conducted over an alpine site in the Retezat Mountains (Southern Carpathians) across four winter seasons are processed using open-source photogrammetric tools, such as OpenDroneMap, to generate digital surface models (DSMs) under snow-covered and snow-free conditions. Snow depth is then derived through a DEM of Difference (DoD) approach. The resulting high-resolution snow depth maps are spatially aggregated to match the resolution of the model outputs, enabling direct comparison between simulated and observed snow conditions for selected time periods. This study emphasizes the design of a transferable and reproducible workflow that enables the comparison of climate-driven snow simulations with user-collected high-resolution observations. The integration highlights how physically based models capture broad-scale snow dynamics, while UAV data reveal fine-scale variability associated with terrain-driven redistribution processes that remain unresolved at the model scale. The presented workflow relies primarily on open data and open geospatial tools, including ERA5 reanalysis, TopoPyScale, Python-based processing libraries (GDAL, rasterio, xarray, pandas, numpy, netcdf4), and open photogrammetric solutions. By combining these components within a coherent processing chain, the approach demonstrates how complex cryospheric analyses can be conducted in a reproducible and adaptable manner. The proposed framework provides a practical pathway for integrating climate reanalysis, terrain-aware downscaling, snow modeling, and UAV observations in alpine environments. It can be readily adapted to other mountain regions and applications, supporting improved understanding of snowpack dynamics and their implications for hydrology, permafrost, and ecosystem processes under changing climatic conditions. foss4g-europe-2026-5695-linking-climate-downscaling-and-uav-observations-an-open-workflow-for-snow-modeling-in-alpine-terrain Use cases & applications Andrei Ioniță en false https://talks.osgeo.org/foss4g-europe-2026/talk/SJEX8J/ https://talks.osgeo.org/foss4g-europe-2026/talk/SJEX8J/feedback/ A01 Talk 2026-06-30T11:30:00+03:00 11:30 00:30 Discrete Global Grid Systems (DGGS) are increasingly adopted as a unified spatial reference framework for organising and analysing multi-source geospatial data at global scale. Among hexagonal DGGS configurations, refinement ratio 7 systems exhibit particularly desirable properties: they preserve hexagonal symmetry across refinement levels and produce unambiguous indexing hierarchies where each cell maps to exactly one parent (Sahr, 2011). The recently introduced IGEO7 system and its associated Z7 hierarchical integer indexing scheme (Kmoch et al., 2025) provide a pure aperture 7 equal-area hexagonal DGGS implemented in the open-source DGGRID software. While IGEO7/Z7 offers significant theoretical advantages over systems such as H3, such as true equal-area cells versus H3’s ±50% cell size variation, practical challenges remain in translating these advantages into efficient, scalable computational workflows. This paper addresses three interconnected challenges: (1) the algorithmic foundations of Z7 neighbourhood computation using Generalised Balanced Ternary (GBT) arithmetic on the int64 bit-packed index, (2) the alignment of Z7’s hierarchical index structure with cloud-native storage layouts in Zarr via monotonic parent-based range indexing, and (3) a practical demonstration through slope gradient computation as a representative focal operation on hexagonal DGGS. A Z7 index is a 64-bit unsigned integer where the first 4 bits encode the base cell number (0–11, corresponding to the 12 pentagonal cells at the icosahedron vertices), and the remaining 60 bits encode up to 20 resolution digits at 3 bits each (values 0–6, with 7 marking digits beyond the cell’s resolution). This compact bit-packed representation enables efficient hierarchical operations through bitwise manipulation: parent extraction requires only masking and setting trailing digit groups to 7, while resolution determination reduces to scanning for the first occurrence of the sentinel value 7. The encoding ensures that all children of a given parent cell share a common bit prefix, a property that is fundamental to both neighbourhood computation and storage optimisation. Neighbourhood finding in Z7 leverages GBT arithmetic, which is a generalisation of balanced ternary to the three axes of a hexagonal grid (Sahr, 2019). Each of the six neighbours of a cell is computed by performing digit-wise addition of a direction vector (digits 1–6) to the cell’s index implemented via bitshifting, starting at the finest resolution digit and propagating carries toward coarser levels. Because the aperture 7 grid alternates orientation between successive resolutions (approximately ±19.1° rotation), the addition tables alternate between clockwise and counter-clockwise variants at odd and even resolutions respectively. Each per-digit operation involves a table lookup (a 7×7 matrix yielding both the result digit and a carry digit), making the overall algorithm O(r) in complexity where r is the resolution. When the carry propagates beyond the first resolution digit, the neighbour crosses into an adjacent base cell, requiring a lookup in the icosahedral adjacency table and potential rotational corrections at polar base cells. We present a Python/Numba implementation of this algorithm that operates directly on arrays of uint64 Z7 indices, achieving vectorised batch neighbourhood computation suitable for large-scale raster-style analysis. The hierarchical prefix property of Z7 indices, where all children of a parent share a common bit prefix, directly enables an efficient storage layout for cloud-native Zarr archives. When Z7 indices at a given resolution are sorted numerically, cells within the same parent region are stored contiguously. This creates a monotonic range index where any parent zone’s children can be retrieved through a simple range query on the sorted 1-D index dimension. We describe how xarray-xdggs (XDGGS, Kmoch et al, 2024) exploits this property: DGGS-indexed data is encoded as 1-D Zarr arrays with the Z7 cell ID as the coordinate dimension (but stored only as the start and end IDs), and chunking boundaries are aligned with coarser-resolution parent boundaries (e.g., chunking at resolution N-4 or N-5 while storing data at resolution N). This alignment ensures that hierarchical queries, i.e. aggregation from index children to logical parents, or drill-down from parents to children, traverse contiguous storage blocks, minimising I/O operations in cloud-object-storage environments. The approach mirrors the storage optimisation patterns known from quad-trees overviews or the HEALPix nested indexing but extends naturally to aperture 7 hierarchies. We also discuss how Zarr’s metadata attributes are used to record DGGS parameters (grid type, indexing scheme, refinement level), enabling self-describing archives that can be leveraged for large scale computations. As a practical demonstration, we implement a slope gradient computation, a classical focal GIS operation in terrain analysis, operating entirely within the Z7 index space. For each cell, the algorithm retrieves the six neighbours via GBT arithmetic, obtains the elevation values and their relative directions to each other, calculates the elevation differences along the three hexagonal axes onto two orthogonal components, and computes the slope from the combined partial derivatives. This finite-difference approach, adapted from Li et al. (2022), benefits from the uniform adjacency and equal weighting inherent to hexagonal cells, eliminating the directional bias present in rectangular grid slope computations. We implement this using Xarray for data management, Numba-accelerated functions for batch Z7 neighbour lookups on uint64 arrays, and demonstrate the end-to-end workflow from Zarr-stored elevation data through to a Zarr-stored slope product. All data is indexed by Z7 cell IDs without any intermediate coordinate transformations. Our results show that the combination of Z7’s bit-packed int64 representation, GBT-based neighbourhood arithmetic, and parent-aligned Zarr chunking provides a coherent, performant stack for DGGS-native geospatial analysis. The approach is implemented using open-source Python tools (Numba, Xarray, XDGGS, Zarr) and is designed to integrate with emerging DGGS standards and cloud-native data infrastructure. foss4g-europe-2026-5906-efficient-neighbourhood-computation-and-cloud-native-storage-for-the-igeo7-dggs-using-the-z7-gbt-indexing Academic track Alexander Kmoch en false https://talks.osgeo.org/foss4g-europe-2026/talk/HSFXR3/ https://talks.osgeo.org/foss4g-europe-2026/talk/HSFXR3/feedback/ A01 Talk 2026-06-30T12:00:00+03:00 12:00 00:30 The subdivision control check (udstykningskontrol, UKS) is the process of verifying that a land subdivision complies with planning and land-use regulations. In Denmark, chartered land surveyors are legally obligated to complete this check for every cadastral case submitted to the Danish Geodata Agency (Geodatastyrelsen). The UKS requires the surveyor to manually gather data from 11 different legal themes, ranging from protected nature areas and coastal protection lines to soil contamination, road building lines, and local plans (resulting in querying 17 different geospatial data themes), before verifying whether a proposed cadastral change is legally permissible. Each theme is governed by its own sector legislation, and the surveyor must cross-reference open government datasets from multiple national portals including data themes such as nature, environmental, planning, cadastral, coastal zones, heritage sites, agricultural etc. In current practice this process is time-consuming, fragmented, and prone to human error [Hosseini et al., 2025b], yet it remains a mandatory prerequisite before any cadastral case can be registered. Research Question This paper presents a web-based, AI-enabled PostGIS engine that automates the UKS workflow. The system accepts WFS links, performs dynamic geospatial analysis, and structures its output specifically to enable a generative AI model to interpret the geographic properties of each GIS analysis. The central research question is: how can AI be utilised as a tool to streamline the subdivision control check, and to what degree can a locally hosted, open-source LLM produce legally grounded, evidence-based answers when provided with deterministic geospatial results as context? Notably, while the cadastral use case drives the design, the core contribution is a generalisable architecture: the combination of WFS ingestion, PostGIS analysis, and AI interpretation can be applied to any regulatory compliance workflow where spatial evidence must be matched against legal requirements. System Architecture and Open-Source Stack The system is built entirely on free and open-source components. The web application is developed in Next.js, which exposes the APIs that connect the user interface to the geospatial and AI backend. The data layer is managed through Supabase, which provides three databases built on PostgreSQL: a primary database storing case information and parcel geometries; a results database holding PostGIS outputs in structured JSON; a vector database for the CAG embeddings. At the analytical core is PostGIS, which performs all 17 geospatial analyses deterministically against the parcel geometry retrieved from the Danish cadastral register. The system accepts WFS endpoints, reads GetFeature responses, and constructs a bounding box envelope around the selected parcel. This envelope is used to query each WFS service, and the returned features are parsed and stored. Spatial operations include within-polygon tests, line intersections and distance calculations. The outputs are structured to serve as precise inputs for the AI interpretation phase, since the quality of the LLM response is only as good as the spatial evidence it receives. For natural language interpretation, the system uses Ollama, an open-source platform for running LLMs locally, serving the Meta Llama 3.1 8B model [Ollama, u.d.a]. The relevant legal texts are embedded using the Nomic-embed-text model and stored in the vector database. This constitutes a Cache-Augmented Generation (CAG) architecture [Chan et al., 2025]: rather than expecting the model to recall Danish land law from its training data, the system caches the legislation and injects it as context at inference time. This constrains the model to a closed legal knowledge space, which reduces the risk of hallucination. Pipeline Phases and Case Demonstration The processing pipeline consists of four phases. Phase 1 accepts a parcel identifier via the web interface, retrieves the cadastral geometry, and initialises the case. Phase 2 runs the orchestrator, which queries all WFS endpoints in parallel and populates the database. Phase 3 executes the PostGIS analyses, producing a structured result record per theme with a preliminary decision flag, spatial evidence, and an agent log. Phase 4 passes these results alongside the embedded legislative context to the LLM, which produces a completed draft of the UKS form. The paper includes a case-oriented walkthrough demonstrating the system on a real cadastral parcel, showing the analysis outputs for each of the 17 themes and the corresponding AI-generated interpretations. Crucially, the geospatial results are themselves meaningful and verifiable independently of the AI layer: in many themes, the spatial finding is already the answer, and the AI provides the legal framing and documentation around it. Results The system was evaluated against real cadastral cases and the generated UKS drafts were compared to manually prepared versions. The PostGIS layer correctly identified overlaps and distances across all 17 themes. The LLM layer produced coherent, legislation-referenced responses in the majority of test cases, with output quality closely tied to the specificity of the spatial evidence provided. Beyond the cadastral domain, the architecture is directly applicable to other land-use compliance workflows where spatial data must be checked against regulatory thresholds. Obvious examples include wind turbine siting (setback distances to dwellings and nature areas), solar farm permitting, and environmental impact screening. Relevance for FOSS4G All geospatial data originates from Danish national open data infrastructures operating under INSPIRE-compliant WFS standards. The full stack, Next.js, PostGIS, Supabase, Ollama, Llama 3.1, and Nomic-embed-text, is open source. The architecture is generalisable to any jurisdiction exposing land-restriction data through WFS services. The system code and data schema will be made publicly available under an open-source licence and available on GitHub. The study contributes to a broader discussion on responsible LLM integration into professional legal-technical workflows [Hosseini et al., 2025a], specifically the role of deterministic spatial evidence as a grounding mechanism that makes AI output traceable and verifiable. Conclusion The presented system demonstrates that a web-based open-source GIS and LLM pipeline can automate complex, legislation-bound cadastral workflows in a robust and practically useful way. Human oversight is preserved throughout, as the surveyor reviews and approves all outputs. foss4g-europe-2026-5913-automating-the-subdivision-control-check-an-open-source-gis-and-llm-pipeline-for-cadastral-case-preparation Academic track Lasse Hedegaard HansenNicklas Nordhaug en false https://talks.osgeo.org/foss4g-europe-2026/talk/RKPEN9/ https://talks.osgeo.org/foss4g-europe-2026/talk/RKPEN9/feedback/ A01 Academic lightening talk 2026-06-30T12:30:00+03:00 12:30 00:05 Temporal and spatial monitoring of geomorphic features associated with natural hazards are important for disaster prevention, helping to identify vulnerable areas and anticipate potential risks. Remote sensing data has become a cornerstone for natural hazard monitoring, allowing regular mapping of remote areas and larger regions with reduced time and costs. The unprecedented and continuously growing volume of Earth Observation (EO) data has prompted the use of EO data cubes (EODCs) for efficient storage, management, and analysis (Sudmanns et al., 2023). EODCs are mostly focused on raster and array structures due to the gridded nature of EO data. However, previous work (Abad et al 2022) has shown how raster data cubes are limited by their gridded representation in geomorphic feature detection. While pixel-level analysis is valuable for long time series EO datasets, it often disregards the spatial information (Sudmanns et al., 2020) essential for geomorphic feature analysis, treating pixels in isolation rather than as meaningful objects. Segmenting pixels into objects, or object-based image analysis (OBIA), is an established concept that allows better representation of natural phenomena with diverse characteristics and appearances, such as different types of glacial lakes, landslides, or lava flows (Hölbling, 2022). Individual geomorphic features are treated as aggregates of pixels and are grouped into objects, providing additional information on topological relations. Advances in object detection and image segmentation have opened new opportunities for tracking evolving features over time. Segmented results can be represented as a time series of evolving vectors within EODCs, taking advantage of vector data cube infrastructure. Vector data cubes work well with stationary objects, but the varying extent and shape of geomorphic features pose a challenge for existing data cube structures. Abad et al. (2024) addressed this by introducing summary geometries to define a constant spatial dimension while storing changing geometries as data cube elements, assigning each feature a unique ID based on the centroid of the union and dissolve of all corresponding polygons over time. While effective, this approach only accounted for spatial extent, leaving open how to handle other potential spatiotemporal dynamics, such as merging, splitting, disappearing, or reappearing. The method may face difficulties in such cases where feature grouping may differ according to interpretations. For example, when two nearby glacial lakes expand over time and merge, should they be considered as one lake before they merge? Or if a lake dries out and a new one appears over time, should both lakes have their own unique ID? In the case of several shape-evolving features, whether of the same type (e.g., glacial lakes), or different (e.g., landslides and landslide-dammed lakes), such questions become important when quantifying geomorphological dynamics. In this study we aim to investigate the implementation of grouping algorithms with features experiencing different spatiotemporal dynamics. To investigate different grouping algorithms, we first built a vector data cube with a spatiotemporal polygon dataset of a geomorphic feature. As study area, we selected the glacial lakes at the southern margin of the Vatnajökull ice cap in southeast Iceland, particularly Jökulsárlón, Breiðárlón, and Fjallsárlón, due to the lake’s constant evolution. We acquired Landsat 4-8 data from 1985 to 2015 and Sentinel-2 data from 2016 to 2025 from OpenEO and Google Earth Engine. Annual summer composites were created to minimise ice cover and fill gaps caused by frequent cloud cover, proximity to satellite scene edges (Sentinel-2), and stripe errors (Landsat-7), which partly influenced mapping accuracy, though exact lake delineation was not essential for this study. The OBIA classification used spectral indices and k-means segmentation to map annual lake extents. The annual glacial lake polygons were used to build a vector data cube based on the notebook by Abad et al (2024). Different feature grouping methods were investigated, including the spatial overlap or proximity within a threshold over time, the centroid and the bounding box of the union and dissolve operation of all polygons over time, as well as a representative point of a feature set. An advantage of the vector data cube over raster representations is the ability to attach attribute information (such as lake area) to individual geometries, making it easier to visualise and query the temporal dynamics. Results highlighted the importance of feature grouping selection, as different approaches can lead to meaningfully different interpretations of lake evolution. Some methods treated lakes that would later merge as a single waterbody from the beginning, producing a smooth, continuous growth curve. Others assigned separate IDs until the moment of merging, resulting in an abrupt jump in area for one lake as it absorbed the other. The latter approach, however, is more logically consistent. For example, small lakes currently forming above Breiðárlón are clearly distinct features today, regardless of whether they will eventually merge with the larger lake. Treating them as one lake at their current state would be unreasonable. However, when dealing with larger datasets, we might face difficulties with the number of geometries and scalability. When lakes merge, the other unique ID is still present in the data cube with empty geometries. In a large dataset, handling such lack of data could become an issue as highlighted by Abad et al (2024). The scalability issue requires further exploration in the future. Inconsistent segmentation was another limitation, especially in the 1990s and early 2000s. This prevented reliable detection of disappearing and reappearing lakes, as the algorithm would have assigned new IDs to the same lake over time. Temporally consistent input data is therefore a prerequisite for accurately capturing the full range of geomorphic dynamics. Our work directly addresses the gap of how to structure and analyse evolving vector features over time within data cubes in open-source geospatial workflows. The methods were built on open-source tools (OpenEO, Python) and data (Sentinel and Landsat) when possible and extends on previous work of the community making our work relevant to the FOSS4G conference. foss4g-europe-2026-5909-representing-spatiotemporal-dynamics-of-glacial-lakes-with-vector-data-cubes Academic track Julia Engblom en false https://talks.osgeo.org/foss4g-europe-2026/talk/BLRKM9/ https://talks.osgeo.org/foss4g-europe-2026/talk/BLRKM9/feedback/ A01 Academic lightening talk 2026-06-30T12:35:00+03:00 12:35 00:05 The increasing availability of geospatial data and the growing maturity of open-source technologies have created new opportunities for addressing complex challenges across domains such as maritime surveillance and urban mobility. However, despite significant progress, the geospatial community continues to face limitations in accessing high-quality, interoperable, multimodal, and semantically enriched open datasets. This work addresses this gap by presenting four open-access geospatial datasets developed within a unified vision of openness, interoperability, and reproducibility: two datasets targeting vessel monitoring and two focusing on urban mobility. These datasets are part of the MUltiSensor Inferred Trajectories (MUSIT) project, an international, interdisciplinary initiative funded by the European Union's Horizon Europe program. MUSIT aims to transform heterogeneous tracking sensor data into complete, semantically enriched trajectories, opening new perspectives in mobility monitoring and fostering collaboration among academia, industry, and innovators. The first dataset, namely Multimodal Maritime Dataset on the English Channel [1] (MMDEC), provides a comprehensive multi-source view of maritime activity within a defined Area of Interest covering the western Celtic Sea, the English Channel, and part of the North Sea. Spanning a three-month period from July to October 2023, MMDEC integrates heterogeneous data streams including Automatic Identification System (AIS) signals, satellite imagery, meteorological and oceanographic data, port locations, and marine protected areas. By combining these diverse sources into a single, harmonized dataset, MMDEC enables advanced analysis of maritime behavior, anomaly detection, and environmental monitoring. Its multi-layered structure reflects real-world operational complexity and supports a wide range of use cases, from maritime safety to ecological impact assessment. Within the MUSIT framework, MMDEC represents a concrete realization of the project's data collection and integration pillar, contributing a rich, multi-sensor foundation for subsequent trajectory reconstruction and analysis. Complementing this dataset, AegeaNET [2] introduces a real-time dimension to maritime monitoring through an open sensor network deployed across the Aegean Sea. AegeaNET comprises strategically positioned AIS and ADS-B receivers that capture maritime traffic, providing continuous streams of positioning data to facilitate real-time tracking and situational awareness. As an academic and open initiative, AegeaNET exemplifies how distributed, community-driven sensor networks can enhance transparency and data availability in critical domains such as navigation safety and border monitoring. In alignment with MUSIT's core vision, AegeaNET directly addresses the challenge of incomplete or fragmented tracking data by offering persistent, sensor-based observations that feed trajectory inference and fusion pipelines. Together, MMDEC and AegeaNET demonstrate complementary approaches to maritime data collection: one focused on multi-source historical integration, and the other on real-time, sensor-based observation. In the domain of urban mobility, we present two semantically enriched trajectory datasets generated for the metropolitan areas of Paris and New York City [3]. The raw trajectory data underpinning both datasets consists of publicly available GPS traces voluntarily shared by users through OpenStreetMap, retrieved via the OSM API over geographic bounding boxes covering each city. This choice of source ensures full openness and compliance with the Open Database License, while avoiding the privacy issues that typically hinder the release of mobility data. These trajectories are then semantically enriched with multiple contextual layers drawn from heterogeneous open sources. Spatial context is provided through Points of Interest, also extracted from OSM, while weather conditions are integrated from meteorological data services. Additional inferred attributes - including detected stops, movement segments, and transportation modes - are derived through spatio-temporal analysis of the raw GPS signal. A particularly novel contribution is the inclusion of synthetic yet realistic social media posts, generated by a Large Language Model carefully instructed to simulate user-generated content associated with observed movements. This multimodal enrichment opens new possibilities for research at the intersection of mobility analysis and natural language processing. Consistent with MUSIT's emphasis on cross-domain representation and information fusion, the datasets are released in both tabular and Resource Description Framework formats, supporting semantic reasoning, knowledge graph construction, and compliance with the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Together, these design choices make the datasets valuable resources for a wide range of tasks, including behavior modeling, mobility prediction, and LLM-based applications. A key contribution of this work lies not only in the datasets themselves but also in the reproducible and extensible processes used to generate them. By openly sharing both the data and the underlying pipelines, we aim to empower the community to replicate, adapt, and extend our approach to other geographic regions and application domains. This is particularly important in the context of semantically enriched mobility data, where the combination of heterogeneous contextual information remains a significant barrier to entry for many researchers and practitioners. The MUSIT project, through its training and mobility programs and its commitment to open knowledge exchange, actively encourages reproducibility and community-driven engagement. From a broader perspective, these four datasets illustrate the potential of open geospatial data to bridge domain gaps and foster cross-disciplinary innovation. The maritime datasets highlight the importance of integrating heterogeneous environmental and operational data sources, while the urban mobility datasets demonstrate how semantic enrichment can unlock insights into human movement patterns. Both cases emphasize the role of open standards, open-source tools, and collaborative infrastructures in advancing the state of the art - values that are central to MUSIT's mission of building a dynamic community capable of turning research into tangible societal value. Finally, this work aligns closely with the principles of the open geospatial ecosystem by promoting transparency, accessibility, and reuse. By contributing these datasets to the community under the MUSIT project, we seek to support ongoing research, policy-making, and industry applications, while also encouraging further contributions and collaborations within and beyond the consortium. foss4g-europe-2026-5917-advancing-open-geospatial-data-multi-source-maritime-monitoring-and-semantically-enriched-urban-mobility-datasets Academic track Jelena PanagiotakouIoannis Kontopoulos en false https://talks.osgeo.org/foss4g-europe-2026/talk/MX3ZAN/ https://talks.osgeo.org/foss4g-europe-2026/talk/MX3ZAN/feedback/ A01 Academic lightening talk 2026-06-30T12:40:00+03:00 12:40 00:05 The integration of WebGIS platforms into energy planning processes and territorial governance has become a widely recognized approach at the international level, owing to the ability of these tools to support complex analyses and strategic decision‑making in the fields of sustainable development. In this context, spatial analyses are increasingly required to assess renewable energy potentials, evaluate land‑use and environmental constraints, support multi‑energy system modelling and inform strategic decision‑making. By enhancing data sharing, accessibility and active engagement of multiple stakeholders, WebGIS platforms significantly broaden the opportunities for collaborative planning. The distinctive features of these tools - namely spatial and temporal analytical capabilities, management of large volumes of heterogeneous data and interactive visualization - make them particularly well suited to supporting complex and multidisciplinary decision‑making processes, such as those characterizing the governance of the decarbonization process across different administrative levels. Over the past decade, RSE has developed and maintained a broad ecosystem of WebGIS tools and processing services to support energy planning processes in Italy [1]. This ecosystem includes several thematic atlases dedicated to renewable energy resources, as well as an integrated national energy atlas to assess energy sources integration in the territory and a centralized geospatial database collecting datasets at multiple spatial and temporal resolutions. These platforms have been designed following open data principles and are freely accessible online, primarily relying on free and open-source software geospatial components and standards. Access to data is currently provided through web-based visualization interfaces, standard OGC services, and, where possible, data downloads in common GIS formats. While this approach has proven effective in supporting data dissemination and exploratory spatial analysis, it increasingly shows limitations in responding to evolving user needs. In particular, public administrations, researchers and technical users are progressively moving beyond map-centric usage patterns, requiring more flexible, programmatic and automated access to geospatial energy-related data. Users often need to integrate datasets from multiple sources into custom workflows, advanced modelling environments, dashboards or decision-support systems. From a technical perspective, the current architecture is characterized by a strong emphasis on visualization and bulk data access, which limits the effective reuse of open geospatial data in more advanced and automated contexts. While users can explore datasets through WebGIS interfaces and, in some cases, download them, more targeted operations, such as obtaining the value of a dataset at a specific location, computing an aggregate over an area of interest (AOI) or extracting a time series, are not directly supported as machine-accessible services. At the same time, data extraction is currently implemented through multiple heterogeneous portals and ad hoc services, each with its own interaction model. Users may be required to browse large catalogs, generate scripts for local execution or submit requests that are processed asynchronously through separate systems. As a result, even simple analytical needs often require downloading entire datasets and performing local processing, which disrupts automated and reproducible workflows, while fragmentation across ad-hoc data extraction services leads to inconsistent user experiences, duplicated logic and greater access complexity. Thus, traditional WebGIS interfaces and view-oriented services alone are no longer sufficient. Overcoming these limitations requires a rethinking of the existing infrastructure, shifting from a model centered on visualization and bulk downloads to one based on programmatic, query‑driven access [2]. This contribution presents the conceptual design and early implementation of a centralized and modular REST API intended to act as a unified access layer across the entire RSE geospatial data ecosystem. The API is designed to decouple data access and analytical capabilities from specific user interfaces, enabling consistent and programmatic interaction with geospatial datasets and services. The proposed API addresses the previously detailed limitations by introducing a unified, query-driven access layer that complements existing WebGIS interfaces. Users can issue parameterized requests to retrieve only the specific information they need, including point-based queries, nearest-feature searches, spatial aggregations, time series extraction and filtered data subsets. These requests can be executed both interactively and programmatically, enabling direct integration into automated workflows, modelling pipelines and external applications. In addition, the API extends the capabilities of WebGIS clients beyond standard OGC-based interactions. While WMS and GetFeatureInfo remain available for visualization and basic inspection, the API enables richer server-side operations triggered by user interactions, returning structured results suitable for advanced visualizations such as charts, indicators and dynamic summaries. A further key aspect is the integration of existing domain-specific tools and processing services. Currently exposed as standalone web applications or custom WebGIS components, these tools are preserved and made accessible through the API as part of a consistent access model. In this configuration, the API acts as an orchestration layer, routing requests to the appropriate internal service, harmonizing inputs and outputs and enforcing common policies for authentication, authorization, and usage control. Overall, the API would establish a single, consistent entry point for querying, extracting and processing geospatial data, shifting the ecosystem to a more flexible, query-driven model. This transition would significantly improve the accessibility, usability, and interoperability of open geospatial data, enabling more efficient, reproducible and scalable applications across a wide range of use cases. Moreover, by serving as a unified access layer, the API acts as an encapsulation boundary: it hides internal changes to data structures, storage systems, or processing workflows behind a stable interface, so client applications can remain unaffected as the system evolves. Within the FOSS4G context, the proposed architecture demonstrates how mature open-source geospatial components can be enhanced with modern API-driven paradigms to support more dynamic and interoperable data ecosystems. In the specific case of the RSE tool ecosystem, this approach enables the incremental evolution of existing infrastructures, preserving consolidated tools while improving data accessibility, coherence and interoperability both among datasets and with external information systems. Overall, it represents a practical and transferable example of how open geospatial platforms can increase the value and usability of information assets without requiring disruptive redesigns. foss4g-europe-2026-5910-towards-a-modular-and-open-api-for-interoperable-energy-webgis-platforms Academic track Matteo Gobbi Frattini en false https://talks.osgeo.org/foss4g-europe-2026/talk/UGKGSH/ https://talks.osgeo.org/foss4g-europe-2026/talk/UGKGSH/feedback/ A01 Academic lightening talk 2026-06-30T12:45:00+03:00 12:45 00:05 Spatially explicit biogas potential assessment requires integrating land cover dynamics, agricultural census records, livestock inventories, and municipal waste generation data into a unified analytical framework. In most national contexts, each of these dimensions is maintained by a distinct government agency operating at incompatible spatial resolutions, under divergent update schedules, and without a shared classification ontology. The result is not data absence but institutional fragmentation, and fragmentation is a structurally different problem from scarcity: the data exists, is publicly available, and is updated regularly, but no coordinating infrastructure connects it into an analysis-ready form. This distinction has direct consequences for platform design. A system built to address data scarcity aggregates and estimates; a system built to address institutional fragmentation must integrate, reconcile, and make the reconciliation process itself transparent and reproducible. PILAR-2b (Plataforma Inteligente de Localização e Aproveitamento de Resíduos para Biogás e Bioprodutos) is an open-source spatial decision-support platform developed at CP2b/NIPE-Unicamp, Brazil, to produce municipally disaggregated biogas potential estimates for São Paulo State's 645 municipalities through a reproducible pipeline that treats data integration as a primary computational challenge rather than a preprocessing step. The platform integrates five heterogeneous government datasets: IBGE agricultural census records, SNIS municipal waste inventories, MapBiomas 30-metre land cover rasters, ANEEL energy infrastructure layers, and CETESB environmental licensing records. These sources present four structurally distinct incompatibilities: administrative unit definitions differ across agencies; coordinate reference systems require transformation to SIRGAS 2000 UTM Zone 22S; update cycles range from annual to decennial; and feedstock classification schemes lack a common ontology across sources. The data integration pipeline resolves each incompatibility through a fully scripted, version-controlled transformation chain implemented in Python with GeoPandas, Shapely, and Fiona, with no manual reconciliation steps at any stage. Every transformation from raw institutional input to analysis-ready geospatial layer is traceable, independently executable, and documented in a public repository under GPL 3.0. The platform architecture follows a three-tier microservices model, separating presentation, application logic, and data persistence into independently deployable cloud components. The presentation layer is implemented in Next.js 15 with Mapbox GL JS vector tile rendering, enabling simultaneous display of all 645 municipal polygons with sub-second pan-and-zoom response without requiring a client-side GIS installation. The application layer is built on FastAPI 0.104.1 with an asynchronous Python runtime; NumPy vectorization across all municipalities reduced per-request computation time from approximately 8.2 seconds with sequential iteration to 0.9 seconds with vectorised batch processing. The persistence layer operates on PostgreSQL 15 with PostGIS 3.4, hosted on Supabase, with full operational costs ranging from zero to fifty US dollars per month, depending on demand, a range considered accessible for public sector and academic deployment without proprietary licensing. Integrated feedstock inventories enter a correction factor methodology that decomposes theoretical biomass availability into practical mobilisable potential through four sequential, feedstock-dependent factors: collection efficiency (FC), competing uses (FCo), seasonal availability (FS), and logistical constraints (FL). Each factor is independently parameterised per feedstock category across 30 feedstock types in four sectors: agriculture, industry, livestock, and urban waste. The FC times FCo times FS times FL multiplication produces a transparent audit trail from gross theoretical potential to practically actionable municipal estimates, enabling factor-specific sensitivity analysis that is structurally unavailable in single-factor yield approaches, where corrections are embedded rather than decomposed. Applied to São Paulo State's 645 municipalities, PILAR-2b quantifies a theoretical biogas potential of 133.82 million m³ CH4/day from consolidated feedstock inventories, reduced to 19.69 million m³/day practical mobilisable potential through sequential correction factor application, representing a 14.7% weighted average retention that encodes binding regulatory and logistical constraints directly in the correction structure. Spatial analysis reveals that 25.1% of municipalities account for 67.0% of the state's practical potential, a four-order-of-magnitude spread across the municipal distribution that confirms that state-level aggregation is analytically insufficient for infrastructure investment decisions and that municipal-resolution outputs constitute a structurally distinct planning information product. Cross-validation against the 2025 São Paulo biomethane roadmap (Instituto 17, PSR, and Amplum Biogás, published by FIESP) yields a mean absolute error of 13.2%, approaching the 15-20% performance range documented for the DBFZ Biomass Monitor operating under substantially denser European data conditions. All primary analytical workflows are complete within sub-3-second response times under 50 concurrent users in production conditions. The data incompatibility challenges resolved by this pipeline, including misaligned administrative units, divergent update cycles, and absent cross-agency classification standards, recur structurally wherever policy-relevant energy assessments depend on combining institutionally separated government sources. The São Paulo deployment demonstrates that these challenges are tractable within a fully open, browser-accessible stack at operational costs within reach of public institutions. PILAR-2b is designed for direct replication in any national or subnational context where MapBiomas-equivalent land cover data, agricultural census records, and municipal waste inventories achieve sufficient completeness, a condition already met across multiple Brazilian states and increasingly across Latin American, African, and Southeast Asian contexts where open satellite and census data are advancing faster than institutional coordination. The result is not a regional tool but a methodological blueprint: an open geospatial integration architecture replicable wherever fragmented data governance, not technical capacity, is the binding constraint on evidence-based energy planning. foss4g-europe-2026-5908-pilar-2b-an-open-geospatial-pipeline-for-biogas-potential-assessment-in-institutionally-fragmented-data-environments Academic track Lucas Nakamura Cerejo en false https://talks.osgeo.org/foss4g-europe-2026/talk/VJT8JW/ https://talks.osgeo.org/foss4g-europe-2026/talk/VJT8JW/feedback/ A01 Talk 2026-06-30T14:30:00+03:00 14:30 00:30 Agriculture accounts for approximately 70% of global freshwater withdrawals and remains highly sensitive to climate variability. Therefore, timely estimates of agricultural water use are crucial for effective basin planning, water-stress diagnostics, and informed climate adaptation strategies. In Colombia, the National Water Study (Estudio Nacional del Agua, ENA) is the official instrument for quantifying water demand across sectors. It relies on an FAO-56 soil–water balance approach that requires extensive input data and numerous intermediate calculations (IDEAM 2023). Because this method is so operationally demanding, the ENA is published only once every four years and suffers from a significant reporting lag. For instance, the ENA 2022 report relies on data from 2020. Consequently, these estimates are often outdated by the time of publication, and lack the capacity to capture intra-annual variability driven by phenomena such as El Niño and La Niña. This research therefore investigates whether freely available satellite data can support the ENA reporting, enabling more continuous and timely monitoring. The study evaluates FAO's WaPOR (Water Productivity through Open access to Remotely sensed-derived data) through an open-source, reproducible framework of publicly available notebooks. It combines WaPOR's monthly actual evapotranspiration and interception (AETI) and precipitation products with ENA's agro-climatic crop mask to restrict the analysis to agricultural areas and perform pixel-level calculations of water consumption. The resulting blue water volumes and irrigation water withdrawals were aggregated at the sub-basin level and compared with official ENA estimates for 2020. Apart from the country-level analysis, the methodology was applied to a specific zoom-in area located within the Magdalena province. This area is characterized by a high dominance of banana and oil palm (Cruz 2020), which allowed a specific comparison between WaPOR-derived evapotranspiration values and the expected agricultural patterns. This selection was based on two main criteria: first, the area exhibits a relatively homogeneous surface according to the FAO-WaPOR (L3-AETI-M - spatial resolution of 30m) layer which facilitates the spatial analysis and interpretation of results; however, it should be noted that the Level 2 (L2-AETI-M - spatial resolution of 100m) product will be used for the analysis, consistent with the methodology applied across the entire national territory. Second, crop mask data from IDEAM reveal that the region contains a significant proportion of two key permanent crops: oil palm and banana The results show a strong spatial agreement (R² = 0.83), indicating consistent identification of priority basins, although WaPOR estimates are approximately 66% of ENA values, with similar patterns for irrigation withdrawals. This systematic offset is consistent with the conceptual difference between the two approaches: ENA estimates potential crop water demand under optimal conditions using FAO-56 crop coefficients, while WaPOR captures actual evapotranspiration under real field constraints. This difference becomes evident when analysing seasonal behaviour: in the Caribbean and Magdalena basins, which concentrate the largest agricultural areas, spatial agreement is notably high during the dry season but drops significantly with the onset of the first rainy season. The detailed analyses for the Magdalena area shows that WaPOR-derived evapotranspiration values are slightly lower than ENA estimates for both banana and oil palm, consistent with the national findings. The seasonal structure of this disagreement reveals that January is the only month where the relationship inverts, with WaPOR marginally exceeding ENA, a pattern consistent with dry-season dynamics in which low atmospheric humidity allows WaPOR's energy balance approach to capture relatively high actual ET. From February onwards, ENA overtakes WaPOR, and the gap widens progressively through the wet season transition, reaching its annual peak in May — precisely when the first rainy season is established — and a secondary peak in October, coinciding with the second rainfall peak over the region. This seasonally structured bias confirms that the difference between the two approaches is not a random but a response to Colombia's climate variability. The developed framework produces spatial outputs and results using freely available tools and data sources such as the WaPOR data which is provided in near-real-time layers of actual evapotranspiration, biomass production, and water productivity at resolutions from 30 m to 300 m globally. The potential of these products for agricultural water monitoring is not new — WaPOR has already been applied in other contexts, such as the assessment of irrigation performance at a sugarcane estate in Mozambique (Chukalla et al. 2022) but its application at a national scale in Colombia for water demand reporting remains largely unexplored. This study aims to contribute to that evidence base in Latin America, and to motivate further exploration of WaPOR's potential in other regions where timely, low-cost alternatives to conventional water accounting methods are needed. By openly sharing the methodological framework through accessible notebooks, this research actively promotes reproducibility and collaborative science, which are core tenets of the FOSS4G community. It empowers local water authorities, researchers, and policymakers in data-scarce regions to independently verify, adapt, and scale the approach to their specific hydroclimatic contexts. Furthermore, integrating these Python-based workflows with QGIS demonstrates how open-source ecosystems can bridge the gap between complex satellite data and operational water management, ultimately democratizing access to critical climate adaptation tools. foss4g-europe-2026-5907-evaluating-the-application-of-fao-wapor-data-to-support-colombia-s-national-water-study-on-water-consumption-in-the-agricultural-sector Academic track Laura Agudelo en false https://talks.osgeo.org/foss4g-europe-2026/talk/QADHQX/ https://talks.osgeo.org/foss4g-europe-2026/talk/QADHQX/feedback/ A01 Talk 2026-06-30T15:00:00+03:00 15:00 00:30 Water-quality monitoring increasingly relies on heterogeneous sensing systems that combine in situ probes, automated acquisition pipelines, interoperable web services, and data-driven analysis. Open geospatial standards such as the OGC SensorThings API [1] were developed to enable interoperable management of observations and metadata from heterogeneous sensor systems, while platforms such as istSOS4 [2] show how these principles can be implemented in open-source environmental monitoring infrastructures. At the same time, recent literature highlights the growing relevance of citizen science and IoT-based participatory sensing for water-quality monitoring [3], both to expand observation capacity and to strengthen communication and public engagement around environmental data. In parallel, machine-learning approaches for algal bloom detection and prediction [4] increasingly combine physicochemical measurements with image-based or remotely sensed observations, indicating the potential of AI-enabled optical monitoring for aquatic environments. However, the integration of open sensor standards, participatory monitoring, and future AI-derived optical observations within a single geospatial framework remains limited. This contribution addresses that gap through the following research question: how can an open geospatial infrastructure based on istSOS4 support multimodal and participatory water monitoring today, while also providing a coherent integration path for future edge AI-derived optical observations? The work is developed within the Interreg WINCA4TI project, Water Interactions with Nature, Climate and Agriculture for Ticino, which aims to analyse and describe the interactions between water, economy, environment, and agriculture in the Ticino basin. Within this broader framework, SUPSI promotes participatory environmental monitoring initiatives on Lake Lugano, combining scientific observation, local collaboration, and territorial awareness. The monitoring activity described in this paper is part of this effort. Through a collaboration based on citizen science principles, a local nautical club hosts and helps maintain our sensor infrastructure, while receiving in return water-quality information and analytics through dedicated dashboards The current deployment on Lake Lugano consists of a multisensor platform combining conventional aquatic measurements with an optical experimental subsystem. At present, the system acquires fluorimetric measurements and dissolved oxygen observations, together with image data collected by an in-house developed three-camera optical device. These sensing components coexist within the same monitoring initiative, but they do not yet operate within a fully unified observation model. The geospatial backbone of the proposed framework is istSOS4, which implements the OGC SensorThings API and provides a machine-readable, discoverable, and reusable way to organize and expose environmental observations, metadata, and temporal series. Additionally, within this project, the current API is planned to be extended to support the STAplus standard, in order to better address citizen science requirements related to data attribution, storage, and handling. Within this architecture, conventional sensors such as fluorimeters and dissolved oxygen probes naturally fit the SensorThings observation model. The more challenging issue concerns the optical subsystem, whose outputs differ substantially from scalar probe measurements. The methodological choice proposed in this paper is therefore to distinguish between raw optical acquisition and published environmental observations. Raw imagery is not ingested directly into istSOS4; image acquisition, storage, and processing instead remain outside the observation service. Building on this distinction, the paper proposes that the optical subsystem should evolve into an edge AI sensor. In this envisioned configuration, images would be processed locally through dedicated computer-vision pipelines running close to the sensor. These models would transform raw visual input into higher-level variables that can be represented as time-stamped observations, such as algal classification, estimated algal concentration, bloom-related indicators, anomaly flags, and associated confidence scores. Once formalized as observations with explicit timestamps, observed properties, and provenance, these outputs could be published through istSOS4 alongside the measurements acquired by conventional probes. The current results of the work are both practical and methodological. First, the project has produced an operational multisensor deployment on Lake Lugano that already collects conventional water-quality measurements together with optical data from the three-camera system. Second, the project has led to the definition of an integration framework in which istSOS4 supports current probe-based observations and is designed to accommodate AI-derived optical indicators. This contribution is relevant to the FOSS4G Europe Scientific Track because it addresses a concrete environmental-monitoring problem through a geospatial and standards-based approach; it highlights the role of free and open source geospatial software as an enabling infrastructure connecting sensors, metadata, interoperability, and downstream analytics; and it brings together themes like GeoAI, remote sensing for water resources management, participatory monitoring, and open geospatial infrastructures for environmental observation. The originality of the work lies in defining how AI-derived optical indicators, rather than raw imagery, can be integrated into an istSOS4-based observation framework alongside conventional water-quality measurements within a participatory monitoring setting. The framework shows how a standards-based open-source infrastructure can support current sensor observations while remaining extensible toward future AI-enabled optical sensing. Reproducibility is a key aspect of the framework, which uses istSOS4 and the SensorThings API to support explicit sensor descriptions, consistent observation structures, timestamps, and traceable data access. By separating acquisition, storage, inference, feature extraction, and publication, the architecture clarifies provenance and supports reusable environmental observations. Grounded in the Lake Lugano deployment within WINCA4TI / Interreg and supported by local stakeholders, the work proposes a generalizable framework in which istSOS4 acts as the interoperable layer for conventional and future AI-derived environmental observations. (1) Open Geospatial Consortium, OGC SensorThings API Standard, 2025. (2) M. Cannata, M. Antonovic, M. E. Molinari, and M. Pozzoni, “istSOS, a new sensor observation management system: software architecture and a real-case application for flood protection,” ISPRS Archives, 2013. (3) S. Blanco Ramírez, I. van Meerveld, and J. Seibert, “Citizen science approaches for water quality measurements,” Science of the Total Environment, 2023. (4) J. Park, K. Patel, and W. H. Lee, “Recent advances in algal bloom detection and prediction technology using machine learning,” Science of the Total Environment, 2024. foss4g-europe-2026-5912-integrating-participatory-water-monitoring-and-edge-ai-sensing-through-istsos4-a-lake-lugano-case-study Academic track alessandro centazzo en false https://talks.osgeo.org/foss4g-europe-2026/talk/UY9NQK/ https://talks.osgeo.org/foss4g-europe-2026/talk/UY9NQK/feedback/ A01 Talk 2026-06-30T15:30:00+03:00 15:30 00:30 ## Motivation The proliferation of waste-contaminated areas poses a significant challenge to global ecosystems, harming wildlife and posing serious risks to human health. Riverine systems are particularly vulnerable, as floodplains act as temporary storage for mismanaged plastic and debris. During high-water events, accumulated waste is transported downstream, further contaminating aquatic environments. Governmental and non-governmental organizations work extensively to remediate these areas, but identifying illegal dumpsites along long riverbanks is resource-intensive and often requires field surveys by vehicle or boat. Efficient, large-scale monitoring tools are therefore essential. Recent advances in remote sensing and machine learning offer promising solutions. This research aims to develop an automated system for detecting plastic waste along riverbanks and water surfaces using multispectral satellite imagery. ## Key Related Works The field of satellite-based waste detection is rapidly evolving. Previous efforts by *Magyar et al. (2023)* laid the foundation for this study by employing a Random Forest (RF) model on PlanetScope and Sentinel-2 imagery. Other researchers have utilized different sensors and algorithms; for instance, *Sakti et al. (2023)* introduced the "Adjusted Plastic Index" to reduce noise from vegetation and buildings in Sentinel-2 data, achieving 88% accuracy on vegetation but facing challenges with spectral similarities between buildings and debris. *Lanorte et al. (2017)* demonstrated the effectiveness of Support Vector Machines (SVM) for agricultural plastic waste detection using Landsat 8 imagery, achieving overall accuracy up to 94%. Deep learning approaches have also been explored. *Sun et al. (2023)* utilized high-resolution satellite imagery (0.3m–1m) to achieve a 98% detection rate for various waste types, significantly reducing the time required for expert manual review. *Torres and Fraternali (2021)* employed a Convolutional Neural Network (CNN) based on the ResNet50 architecture to identify illegal landfills in 20cm resolution orthophotos with an F-score of 88.2%. While these high-resolution studies show great accuracy, our research focuses on the operational utility of more frequently available multispectral data like PlanetScope to monitor dynamic river environments. ## Methodology ### Data Acquisition and Feature Engineering The study utilizes PlanetScope multispectral imagery, which provides four spectral bands (RGB + NIR). To enhance the model's ability to distinguish waste from natural surfaces, the following spectral indices were calculated: - **Plastic Index (PI)**: Leverages the higher reflectance of plastic compared to water in the NIR spectrum. - **Normalized Difference Water Index (NDWI)**: Used to delineate water features. - **Normalized Difference Vegetation Index (NDVI)** and **Reversed NDVI (RNDVI)**: Used to identify and mask healthy vegetation. - **Simple Ratio (SR)**: Further assists in vegetation classification. ### Training Dataset A comprehensive training dataset was compiled, consisting of 27 million pixels. This dataset includes 29 landfills in Romania — identified via local registries — and the Kisköre reservoir in Hungary, which is a known site for floating waste accumulation. Every pixel was manually annotated into five categories: *Waste*, *Water*, *Pasture/Forest*, *Bare land*, and *Unknown* (including buildings and roads). To improve accuracy, high-resolution aerial imagery was used to differentiate between plastic waste and construction debris. ### Model Development and Optimization A Random Forest classifier was implemented using the Scikit-Learn library. To manage the large dataset, the model was optimized by limiting tree depth to 20, reducing the model size from 14GB to a more manageable 2GB without significantly increasing the false positive rate. Furthermore, because waste pixels are vastly outnumbered by other classes, class weights were applied to mitigate the high false-negative rates caused by data imbalance. ### Advanced Processing Techniques Several techniques were explored to refine performance: - **Principal Component Analysis (PCA)**: Applied to reduce parameter dimensions and suppress noise. It was found that three principal components retained 90% of the variance. - **Seasonal Separation**: Separate models were trained for *summer* (March–October) and *winter* (November–February) to account for variations in vegetation cover and atmospheric conditions. - **Water Masking**: An algorithm was implemented to mask areas distant from the river course, thereby eliminating irrelevant false alarms in urban or agricultural areas. ### Interactive Web Application The results of our research are integrated into an interactive web application that provides a platform for viewing detected waste locations. The application automatically downloads and classifies the latest satellite imagery for monitored areas. The implementation is open-source and is available on GitHub: https://github.com/GISLab-ELTE/WasteDetection/ ## Results and Discussion The model was validated using test data from the Drina River, a site not included in the training set, featuring both land-based dumpsites and floating waste islands. The primary RF model achieved a Match Rate (True Positive) of 29.32% and a Commission Rate (False Positive) of 28.13%. While the Omission Rate (False Negative) was high (70.67%) — largely because the model only classified the core of waste islands — this was considered acceptable for operational purposes where avoiding false leads for clean-up crews is a priority. The model detects the core regions of waste accumulations while maintaining low false positives, which is critical for operational deployment. PCA integration notably improved noise suppression on water surfaces. The PCA-trained model increased the Match Rate to 34.99%, though at the cost of a higher Commission Rate (39.01%). The summer-specific model showed a slight improvement in reliability for summer imagery, reducing the commission rate to 26.1%. Conversely, winter detection remains a challenge due to shadows and poor weather conditions, which hinder spectral accuracy. Our study contributes (i) a large annotated dataset, (ii) an operational RF-based detection pipeline, and (iii) an evaluation of trade-offs between accuracy and usability in riverine waste monitoring. foss4g-europe-2026-5914-automated-riverine-waste-detection-using-random-forest-and-multispectral-satellite-imagery Academic track Máté Cserép en false https://talks.osgeo.org/foss4g-europe-2026/talk/QYAHU8/ https://talks.osgeo.org/foss4g-europe-2026/talk/QYAHU8/feedback/ Auditorium Keynote 2026-07-01T09:00:00+03:00 09:00 01:00 When people think of forestry, they usually picture muddy boots, measuring tapes, and chainsaws—but today’s modern forester is just as likely to be found writing Python scripts and querying geospatial databases. The forestry sector is currently experiencing a quiet revolution fueled by an explosion of free and open-source data, transforming how we monitor, measure, and manage complex woodland ecosystems. This keynote explores the transition from traditional, localized forest inventories to the vast, open-source data ecosystem now available to researchers and practitioners. We will dive into the wealth of global datasets reshaping forest management, including high-resolution canopy height models from Meta and other tech giants, comprehensive datasets from the Horizon Europe PATHFINDER project, and satellite-derived products for biomass estimation from missions like GEDI. Focusing on a case study in the diverse and challenging landscapes of Romania, we will demonstrate how these massive open-source datasets can be fused with local forest management data to create a comprehensive understanding of forest structure and health. We will explore the practical realities of integrating varied data sources—bridging the gap between global satellite observations and ground-level realities—to support sustainable forest management under climate change scenarios. Ultimately, this talk will prove that digital twinning in forestry is no longer just a buzzword, but an accessible reality powered by the global open-source community. foss4g-europe-2026-5885-out-of-the-woods-and-into-the-code-the-rise-of-the-open-source-forester Keynote Mihai Daniel Niță en false https://talks.osgeo.org/foss4g-europe-2026/talk/7GEBHV/ https://talks.osgeo.org/foss4g-europe-2026/talk/7GEBHV/feedback/ Auditorium Talk 2026-07-01T10:00:00+03:00 10:00 00:30 The MapLibre community is currently in the midst of developing the MapLibre Tile Format, a modern, open, and fully community-governed successor to the ubiquitus 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- how properties, geometries and IDs work, how we do optional values and such. Attendees will gain a technical understanding of how the format works, including its data model, feature encoding strategy, metadata approach, and how this is compatibse to 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 how this is primarily based on the contributions and feedback from untold amounts of volunteers. 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. foss4g-europe-2026-5268-state-of-the-maplibre-tile-format Frank Elsinga en false https://talks.osgeo.org/foss4g-europe-2026/talk/8D8RU9/ https://talks.osgeo.org/foss4g-europe-2026/talk/8D8RU9/feedback/ Auditorium Talk 2026-07-01T10:30:00+03:00 10:30 00:30 A default web map takes data in latitude-longitude and displays it in spherical Mercator, which makes two different coordinate systems. But the internal workings of a mapping library require handling even more coordinate systems internally. This talk is a deep technical view into how some web map libraries (Leaflet, MapLibre, OpenLayers, Gleo) handle coordinate systems internally, and how their different strategies affect performance in specific scenarios. foss4g-europe-2026-4903-how-many-coordinate-systems-are-in-a-web-map Use cases & applications Iván Sánchez Ortega en false https://talks.osgeo.org/foss4g-europe-2026/talk/YRQJZW/ https://talks.osgeo.org/foss4g-europe-2026/talk/YRQJZW/feedback/ Auditorium Talk 2026-07-01T11:30:00+03:00 11:30 00:30 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 foss4g-europe-2026-5635-rendering-national-climate-data-in-the-browser-webgl-custom-shaders-with-maplibre-gl-js Use cases & applications Florent Gravin en false https://talks.osgeo.org/foss4g-europe-2026/talk/QD9HHC/ https://talks.osgeo.org/foss4g-europe-2026/talk/QD9HHC/feedback/ Auditorium Talk 2026-07-01T12:00:00+03:00 12:00 00:30 Learn everything MapLibre has been up to, from the new tile format to Martin tile server, MapLibre GL, MapLibre Native, and many other projects foss4g-europe-2026-4933-maplibre-tiles-next-generation-tech-and-other-maplibre-news State of software Yuri Astrakhan en false https://talks.osgeo.org/foss4g-europe-2026/talk/PSPMUT/ https://talks.osgeo.org/foss4g-europe-2026/talk/PSPMUT/feedback/ Auditorium Talk 2026-07-01T12:30:00+03:00 12:30 00:30 Real-time rendered maps are surprisingly complex beasts. Things you may take for granted, such as placement of labels on the map, have been the subject of multiple PhDs and the cause of a large amount of developer blood, sweat and tears. In this talk we will take you on a ride-along and show you the gnarliest challenges of real-time rendered maps, as well as the secrets you will find hidden deep in the MapLibre source code used to tackle them. After this talk, you will understand: - The raison d'être of the MapLibre project. - How we manage to evolve this complex piece of software. - Why creating your own map toolkit is a bad idea, but why you should try anyway. foss4g-europe-2026-5504-secrets-of-real-time-rendered-maps-feat-maplibre-native Bart LouwersStefan Karschti en false https://talks.osgeo.org/foss4g-europe-2026/talk/JCJSVM/ https://talks.osgeo.org/foss4g-europe-2026/talk/JCJSVM/feedback/ Auditorium Talk 2026-07-01T14:30:00+03:00 14:30 00:30 The Mapbox Vector Tile specification has been around in the ecosystem for over 10 years, becoming a formal standard in 2017. They took off due to their lightweight encoding that allows for fast and efficient serving of map tiles. The use of vector tiles is now very common within the geospatial ecosystem with support in many FOSS4G tools for both production and consumption. There are many approaches to serving out vector tiles to end users, with some teams choosing to generate tiles upfront with a library like Tippecanoe and statically serve them, whilst others are using a database like PostGIS and serving them dynamically, potentially with tile servers like Martin or Tegola. Some teams will use a hybrid approach with more frequently updated data being served on the fly and data that is updated less frequently being served as static files. There are trade offs that come with choosing between static or dynamic tile serving, including speed, cost, complexity, flexibility and freshness of data. This talk we dig into these trade offs, examining how and when each approach makes sense and which options you can choose for each. Attendees can expect to come away with a nuanced understanding of these tradeoffs and insights into the tools that they could use when making this decision. We will also finish off by touching on new developments in the vector tile ecosystem like the new MapLibre Tile specification, and MVT support from DuckDB. foss4g-europe-2026-5534-vector-tiles-static-or-dynamic James Milner en false https://talks.osgeo.org/foss4g-europe-2026/talk/8P3BMH/ https://talks.osgeo.org/foss4g-europe-2026/talk/8P3BMH/feedback/ Auditorium Talk 2026-07-01T15:00:00+03:00 15:00 00:30 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. foss4g-europe-2026-4931-pygeoapi-project-status State of software Tom Kralidis en false https://talks.osgeo.org/foss4g-europe-2026/talk/BG9WH8/ https://talks.osgeo.org/foss4g-europe-2026/talk/BG9WH8/feedback/ Auditorium Talk 2026-07-01T15:30:00+03:00 15:30 00:30 The remote execution of processing workflows is a common task in many spatial data infrastructures and projects. To increase interoperability, the OGC (Open Geospatial Consortium) published the [API Processes standard in 2021](https://ogcapi.ogc.org/processes/). Its RESTful design and use of JavaScript Object Notation (JSON) encoding make it suitable for cloud environments. [Pygeoapi](https://pygeoapi.io/) is an open-source implementation of this standard. In pygeoapi, several plugins are available and a manager component must be implemented to manage process jobs. A common feature of the built-in managers is that the processing jobs are executed directly within the pygeoapi Python environment. Hence, a job with high resource demands influences the resource requirements and usage of the pygeoapi instance itself. Recognizing the limitations of pygeoapi’s built-in job managers regarding isolation and resource handling, we developed the [pygeoapi-K8s-manager](https://github.com/52North/pygeoapi_k8s-manager). Resource sharing and non-existent job isolation are some of the disadvantages of this architecture. Due to the resource-intensive nature and need for scheduled execution of certain processes within our projects, we had to run a “heavy” pygeoapi deployment in our cluster. We also needed to execute processes in diverse runtime environments outside of Python, e.g., using the CUDA Fortran model execution. We decided to decouple the management and execution layers to address these demands. Having already deployed a pygeoapi instance in our K8s cluster, it made sense to take advantage of the cluster's processing capabilities. Our team used K8s-CronJobs for process scheduling and K8s-Jobs for execution. The Kubernetes API server handled the process management and pygeoapi provided an interface. The resulting, generic “pygeoapi-k8s-manager” was developed based on [EOX IT Services GmbH’s](https://eox.at) [“pygeoapi-kubernetes-papermill“](https://github.com/eoxhub-workspaces/pygeoapi-kubernetes-papermill). By decoupling management and execution, we were able to define complex process requirements such as using GPUs via Job properties. An autoscaler installed in the cluster applies these properties. This enables on-demand provision of the requested resources. Our team implemented two processes: a HelloWorld-K8s process and a process to run generic images. The first process demonstrates how to run a preconfigured image. The generic process enables image configuration via the pygeoapi configuration file. We will present the current pygeoapi-K8s-manager implementation, future development plans and illustrate its application through exemplary use cases, such as data ingestion, flood modelling and ship voyage optimization workflows. Listeners will gain practical insights into how Kubernetes and OGC API Processes can improve your geospatial data processing workflows, e.g., by reducing resource requirements. The talk will cover sustainable resource management and explain the operation of pygeoapi in a cloud-native environment. We aim to encourage wider adoption, feedback, and contributions to these ongoing developments through this conference. foss4g-europe-2026-5064-optimizing-resource-usage-of-interoperable-geospatial-processing-infrastructures-with-kubernetes Open standards and interoperability for geospatial Eike Hinderk JürrensMartin Pontius en false https://talks.osgeo.org/foss4g-europe-2026/talk/VWRHVX/ https://talks.osgeo.org/foss4g-europe-2026/talk/VWRHVX/feedback/ Auditorium Keynote 2026-07-01T16:30:00+03:00 16:30 01:00 Stay tuned! More info coming very soon! foss4g-europe-2026-5888-surprise-keynote Keynote Marian Neagul en false https://talks.osgeo.org/foss4g-europe-2026/talk/9WZGDC/ https://talks.osgeo.org/foss4g-europe-2026/talk/9WZGDC/feedback/ Auditorium Talk 2026-07-01T17:30:00+03:00 17:30 00:30 Thank you for coming to FOSS4G Europe 2026! We hope you had a great time in Timișoara, meet old friends, made new ones and learned a lot about this wonderful open source in geospatial! foss4g-europe-2026-5883-closing-plenary Marian Neagul en false https://talks.osgeo.org/foss4g-europe-2026/talk/RCQB9M/ https://talks.osgeo.org/foss4g-europe-2026/talk/RCQB9M/feedback/ A02 Talk 2026-07-01T10:00:00+03:00 10:00 00:30 Building a mobile GIS that works offline, syncs reliably, and handles large datasets is not an easy task. At Mergin Maps, we have spent the last year re-engineering our core architecture to make the platform faster and more adapted for large-scale deployments. This talk breaks down exactly what has been upgraded and how to utilize the new features. We will start with the Mobile Application, where we completed a major migration to Qt6 and overhauled our build system using VCPKG, making it work efficiently cross-platform. By building the new foundation, we have introduced features like Photo Sketching, which required deep integration with the QGIS rendering engine to allow vector overlays on raster images. We also implemented a new smart sync logic that triggers uploads based on user activity (like foregrounding the app) rather than just timers, saving battery and reducing conflicts. In addition, we have added QGIS authentication database support for protected layers and better integration. On the Server side, we tackled the biggest bottleneck for large teams: concurrent synchronization. We completely rewrote the sync protocol to handle simultaneous requests and batch large uploads, significantly reducing timeouts during peak field hours. We will also show how we built the new Public Web Maps feature, which renders QGIS projects as lightweight vector tiles for instant browser access. Finally, we will cover the developer tools: full compatibility with the upcoming QGIS, expanded Python client methods for managing workspaces programmatically, and a native History Viewer for tracking feature-level changes directly in QGIS. foss4g-europe-2026-4964-under-the-hood-the-technology-powering-mergin-maps State of software Gabriel Bolbotină en false https://talks.osgeo.org/foss4g-europe-2026/talk/RGNTLX/ https://talks.osgeo.org/foss4g-europe-2026/talk/RGNTLX/feedback/ A02 Talk 2026-07-01T10:30:00+03:00 10:30 00:30 QField brings the power of QGIS to the field, enabling efficient data collection based on QGIS projects, both offline and online. The release of QField 4, new features further improve performance, usability, and flexibility of the application. Together with QFieldCloud, which enables seamless synchronization and collaboration between field and office, the ecosystem has introduced new exciting functionalities. In this talk, we will explore what’s new in QField 4 and QFieldCloud, and show how they work together to create a smooth, end-to-end field data workflow. foss4g-europe-2026-5430-qfield-4-qfieldcloud-your-fieldwork-companions State of software Ivan Ivanov en false https://talks.osgeo.org/foss4g-europe-2026/talk/898S3H/ https://talks.osgeo.org/foss4g-europe-2026/talk/898S3H/feedback/ A02 Talk 2026-07-01T11:30:00+03:00 11:30 00:30 Maintaining spatial consistency between different thematic geographic datasets and reference layers (such as cadastral parcels or base maps) is a persistent challenge in GIS workflows. Manually snapping boundaries to match updated reference data is not only time-consuming and prone to human error but also difficult to reproduce. To address this, Athumi (The Flemish Data Utility Company) & Flanders Heritage Agency developed brdr, an open-source Python library, and its companion QGIS plugin, brdrQ, designed to automate and streamline the alignment of geometries to reference borders. By decoupling the alignment logic (brdr) from the user interface (brdrQ), the project offers flexibility for both developers and GIS analysts. Developers can integrate the alignment engine into automated data pipelines, while analysts can leverage the QGIS plugin for visual validation and manual fine-tuning. Both ways of working ensure a significant reduction in workload to obtain higher-quality data. In this presentation, we will demonstrate the underlying algorithm, showcase the QGIS integration, and discuss real-world use cases where these tools have improved the efficiency of spatial data management at the Flanders Heritage Agency. ### Python library: brdr At its core, brdr is a Python library built to detect and resolve geometric discrepancies through a series of deterministic spatial calculations. Unlike simple snapping tools, the brdr-algorithm evaluates candidate reference geometries by calculating relevant intersections and differences. It uses these metrics to generate alignment predictions: the library calculates the most likely intended geometry based on geometric stability. This predictive approach allows for a high degree of confidence in automated workflows, as ‘brdr’ can distinguish between a deliberate gap and a registration error, maintaining the overall structural integrity of the original dataset. ### QGIS plugin: brdrQ brdrQ integrates the 'brdr'-library into a user-friendly QGIS-plugin, making the 'brdr' logic more accessible through visual GIS-workflows. brdrQ offers several tools, including: - Feature Aligner: An interactive tool for record-by-record inspection, allowing users to compare "predictions" (suggested alignments) with a correctness score before committing changes. - AutoCorrectBorders: A processing algorithm for bulk alignment of datasets. foss4g-europe-2026-5323-pushing-the-boundaries-automated-geometry-alignment-with-brdr-and-brdrq Use cases & applications Yanko Godaert en false https://talks.osgeo.org/foss4g-europe-2026/talk/79DYEL/ https://talks.osgeo.org/foss4g-europe-2026/talk/79DYEL/feedback/ A02 Talk 2026-07-01T12:00:00+03:00 12:00 00:30 Cadastral data collection and land allocation is a key requirement in the world system of today. In Laos, a complete dataset of the country remains missing and is continuously being updated. There is a central database (LaoLandReg) which is gradually being supplemented with paper forms. Land rights are scanned analogously with fingerprints and signatures and painstakingly entered into LaoLandReg. This presentation will outline the project that has been ongoing for the last year in collaboration with the Laotian Ministry of Land Management, commissioned by KfW, to fully digitize cadastral management. The goal was to make the entire process more efficient, transparent, and future-proof. **Current Status:** Currently, copies of the central LaoLandReg database are distributed to the individual provinces and used offline on local computers. State personnel use GPS devices to record the corner points of buildings and land parcels on-site. Attribute data, fingerprints, and signatures of neighbors are also collected. This information is then manually entered into the local database and, after several months, transmitted to the central system – a time-consuming and error-prone process. **The Solution:** Don't despair – ask Open-Source GIS! Using QGIS, QFieldCloud, and QField, in particular, we automated and optimized this complex workflow. Intelligent workflows, data linking, and flexible layouts make the entire process now significantly more efficient and transparent. **Special Feature:** Thanks to this project, the new feature of [COGO - Coordinate Geometry](https://github.com/opengisch/QField/pull/6923) found its way to QField. COGO is a framework that allows you to define a precise location of any spatial feature, making use of mathematical functions and measurements. foss4g-europe-2026-5391-systematic-land-regulation-tool-slrt-digitalization-that-goes-beyond-borders Use cases & applications Berit Mohr en false https://talks.osgeo.org/foss4g-europe-2026/talk/HJJQLS/ https://talks.osgeo.org/foss4g-europe-2026/talk/HJJQLS/feedback/ A02 Talk 2026-07-01T12:30:00+03:00 12:30 00:30 Many organizations still rely on the manual 'Pen and Paper' method for field surveying. This traditional approach is riddled with common problems: unreadable handwriting, missing attributes, photos taken that are disconnected from their actual locations, and manual data entry back at the office which creates a bottleneck where errors thrive. Perhaps most frustrating is the 'data lag', the days or weeks it takes for information to travel from the field to the person who needs to make a decision. In a world of instant information, these delays are no longer necessary. During the talk, we will present how Mergin Maps was used in real-life scenarios enabling: - Seamless Collection: Prepare standardized forms with safety features like mandatory fields and drop-down menus to ensure that no data is left behind or incorrectly collected. - Integrated Georeferenced Photos: With the mobile app, you can take multiple pictures per data point. They are automatically georeferenced and part of your project right away. Forget about the headache of manual geo-tagging during evenings. - Instant Synchronization: Update your QGIS layers with a single button, no cables and no manual CSV imports required. Our synchronization allows for surveyors to work on the same project at the same time, precise versioning of your data, all the while keeping your office and field teams connected even when they are miles apart. Crucially, while synchronization is handled online, all data collection remains fully available offline for remote locations. Mergin Maps is an open-source platform that enables you to collect field data directly into your QGIS project. Being fully integrated with QGIS means that all layers, background maps, symbology, and custom forms are fully synchronized and shown in the mobile app exactly as they appear on your desktop. Mergin Maps is developed to be intuitive and as simple as possible. Your field team can focus on the tasks at hand without needing to know that QGIS exists, yet they will still be able to collect high-quality, professional data. If you are currently struggling with messy spreadsheets, lost paperwork, or a clunky workflow that makes you want to avoid fieldwork altogether, this talk might be for you. Join us to see that losing data does not have to be a standard. foss4g-europe-2026-4950-how-mergin-maps-connects-qgis-and-field-data-collection-in-seconds-without-using-paper Use cases & applications Patrik Mizera en false https://talks.osgeo.org/foss4g-europe-2026/talk/7ERAEU/ https://talks.osgeo.org/foss4g-europe-2026/talk/7ERAEU/feedback/ A02 Talk 2026-07-01T14:30:00+03:00 14:30 00:30 Precise localization in urban areas, can be tricky despite having accurate GPS devices. Urban canyons may lead to interrupted and reflected satellite signals making navigation unreliable und imprecise. This presentation will present the outcome of the ongoing European Horizon Initiative [Egeniouss](https://www.egeniouss.eu/). In a consortium of more than 10 partners we have worked more than three years to get to the point where we can locate ourselves within 10 cm's in urban areas without adding an external GPS Device. The project’s mission was to harness alternative data sources and develop a cloud service based on novel multi-sensor navigation with a tightly integrated visual localisation component to overcome known GNSS issues and augment existing EGNSS services. Following, this service is made available through a dedicated API. To validate our approach, we defined three real-world use cases to test the reliability and accuracy of the service. We’ll showcase one of these used cases and explain how Egeniouss has been integrated into QField via its innovative plugin framework. The presentation will cover the journey of egeniouss alongside QField and how both were constantly improved to finally serve the same groups of end-users. New features which were specifically developed to finally meet the needs of the end-users of the egeniouss service in the urban areas, will also be presented. foss4g-europe-2026-5452-qfield-goes-e-geniouss Use cases & applications Berit Mohr en false https://talks.osgeo.org/foss4g-europe-2026/talk/NSG7MA/ https://talks.osgeo.org/foss4g-europe-2026/talk/NSG7MA/feedback/ A02 Talk 2026-07-01T15:30:00+03:00 15:30 00:30 QGIS is one of the most widely used open-source desktop GIS platforms, offering strong tools for geospatial data management, editing, and styling. Yet collaboration is still hard in everyday production workflows: teams need shared server-side storage, reliable desktop synchronization, conflict-aware multi-user editing, and a transparent history of changes. We develop an open-source stack that enables smooth simultaneous editing from both QGIS and Web maps. NextGIS Web is a Web GIS server for storing and publishing geospatial data with granular permissions and built-in version control for vector layers. It uses QGIS as a renderer, providing near-complete support for QGIS symbology in web maps. NextGIS Connect is a QGIS plugin that integrates QGIS with NextGIS Web: it supports publishing QGIS projects as web maps, opening web maps as QGIS projects, editing server data directly from QGIS, and resolving conflicts interactively. The latest NextGIS Connect release adds end-to-end handling of feature attachments (photos, documents, and other files), managed consistently from both QGIS and the Web interface. Attachments are also covered by the same versioning mechanics as spatial and attribute edits. In this talk, we will present the current state of the NextGIS Web / NextGIS Connect / QGIS ecosystem, demonstrate workflows for seamless multi-user editing, and show how teams can move from local QGIS projects to enterprise-level collaboration using open source software. foss4g-europe-2026-4926-qgis-teamwork-with-nextgis-web-sync-conflict-resolution-and-version-control State of software Eduard Kazakov en false https://talks.osgeo.org/foss4g-europe-2026/talk/EVMMWW/ https://talks.osgeo.org/foss4g-europe-2026/talk/EVMMWW/feedback/ A11 Talk 2026-07-01T10:00:00+03:00 10:00 00:30 The OSGeo project deegree is server-side open source software for spatial data infrastructures (SDI) and the geospatial web. It implements standards of the Open Geospatial Consortium (OGC) and the ISO Technical Committee 211. The project hosts official reference implementations of OGC standards such as OGC API - Features, WFS, WMS and GML. This talk will give an overview of the latest release 3.6 as well as recent developments of version 3.7 featuring support of Java 21 and Tomcat 11. Additionally, the deegree implementation of the OGC API - Features standard will be presented. Lastly, future directions and planned core developments of the project will be highlighted. foss4g-europe-2026-5392-deegree-server-side-open-source-software-for-the-geospatial-web Dirk Stenger en false https://talks.osgeo.org/foss4g-europe-2026/talk/CSQXBH/ https://talks.osgeo.org/foss4g-europe-2026/talk/CSQXBH/feedback/ A11 Talk 2026-07-01T10:30:00+03:00 10:30 00:30 Mapbender 5.0 is close to come out and comes with many great new features that will be highlighted in this talk. But not only the new features are shown. You will discover what Mapbender offers and why it could be your Geoportal solution. Mapbender is a great open source solutions for creating intuitive and high-performance WebGIS applications. Mapbender offers a set of widgets that you can combine. This software solution enables users to quickly and easily publish applications online without having to write a single line of code. Mapbender improved a lot. With the new version we have a refactored design and many new or improved features. You can integrated WMS Services, WMTS Services, OGC API Features Collections or Vector Tiles Services and configure them individually. You can manage access rights for applications. You can setup applications with search functionality and digitize functionality. Mapbender 5.0 offers new features - Batch print functionality - Support for OGC API Features - Support for styles - interactive Help - and more foss4g-europe-2026-5050-mapbender-5-0-next-level-solution-to-create-your-powerful-geoportal State of software Astrid Emde en false https://talks.osgeo.org/foss4g-europe-2026/talk/T3DDW7/ https://talks.osgeo.org/foss4g-europe-2026/talk/T3DDW7/feedback/ A11 Talk 2026-07-01T11:30:00+03:00 11:30 00:30 The QGIS Web Client (QWC) is a fully-fledged application for publishing QGIS projects on the web. It offers both 2D and 3D views. With the QGIS Web Client (QWC), you can publish your projects on the internet with the same visualisation as QGIS Desktop, thanks to the QGIS Server. The environment consists of a modern web application written in JavaScript based on ReactJS and OpenLayers, as well as the qwc-services, an ecosystem of server-side Python/Flask microservices that can be used, for example, to manage user permissions and process geodata within the web application. The new 3D view, developed using THREE.JS and Giro3D, also offers the ability to display and sketch 3D scenes based on 3D tiles, and is a comprehensive tool for urban planning. QWC is modular and extensible, offering both a standard web application and a development framework. You can easily start with the standard application and then customise your application as required, depending on your needs and development capabilities. This talk will give an overview of the QWC’s architecture and introduce the numerous new features developed over the past year. foss4g-europe-2026-5629-qgis-web-client-latest-from-the-project Sandro Mani en false https://talks.osgeo.org/foss4g-europe-2026/talk/U7H9TV/ https://talks.osgeo.org/foss4g-europe-2026/talk/U7H9TV/feedback/ A11 Talk 2026-07-01T12:00:00+03:00 12:00 00:30 GeoSolutions has been involved in several 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. Examples of GeoNode’s builtin capabilities for extending and customizing its frontend application will also be showcased. foss4g-europe-2026-5397-geonode-what-is-use-cases-custom-applications Giovanni AllegriMattia Giupponi en false https://talks.osgeo.org/foss4g-europe-2026/talk/JFHU7L/ https://talks.osgeo.org/foss4g-europe-2026/talk/JFHU7L/feedback/ A11 Talk 2026-07-01T12:30:00+03:00 12:30 00:30 Terra Draw is an MIT licensed JavaScript library for drawing on web maps. It supports six different mapping libraries out the box, including the popular open source libraries MapLibre, Leaflet.js and OpenLayers. The library has many builtin drawing modes for creating common geometries that users need such as point, linestring, polygon and rectangle amongst others. As well as the builtin modes, Terra Draw supports the ability for users to create their own custom modes. Users can provide deep styling control for the features created in these modes to match their applications design, creating a seamless drawing experience for users. This talk will get those new to Terra Draw up to speed on the library and how it work, and then go into some of the new features we have been working on over the last year. These include the widely requested undo/redo functionality, improved opacity support for features, click and drag drawing support for several modes and support for multiple instances of the same modes with different configurations. We'll also look at some interesting real life use cases that we have observed in the last year, showcasing our users to the wider FOSS4G community. Lastly, we'll give an update on the future of Terra Draw and the expected direction of the project for the next year, showing users what they can expect to see in the near future. foss4g-europe-2026-5478-terra-draw-what-s-new-for-2026 James Milner en false https://talks.osgeo.org/foss4g-europe-2026/talk/SVK98A/ https://talks.osgeo.org/foss4g-europe-2026/talk/SVK98A/feedback/ A11 Talk 2026-07-01T14:30:00+03:00 14:30 00:30 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 foss4g-europe-2026-4901-storing-your-satellite-in-a-dggs Remote Sensing json singh en false https://talks.osgeo.org/foss4g-europe-2026/talk/UKW9M8/ https://talks.osgeo.org/foss4g-europe-2026/talk/UKW9M8/feedback/ A11 Talk 2026-07-01T15:00:00+03:00 15:00 00:30 Hexagon based grids have gained particular attention in geospatial applications with their use in discrete global gridding systems (DGGS). A feature of many DGGS is the indexing of the grid cells at different resolutions to uniquely identify the cells. For hexagon based DGGS like H3 or IGEO7 the underlying system for indexing is based on 2D generalized balanced ternary (GBT). GBT was originally described in the 80s for use in image analysis because of the similarity between hexagon grids and biological vision systems. Here we discuss how GBT encodes indexes and discuss the basics of GBT arithmetic in 2D hexagon grids. We demonstrate how GBT arithmetic can be employed for neighbour traversal and more broadly spatial algorithms on hexagonal girds. Finally, we will look at the special cases that arise in the application of GBT to indexing in DGGS, which are the consequences of necessary pentagons and non-GBT arranged base zones. foss4g-europe-2026-5529-indexing-with-hexagons-gbt-explained Javier Jimenez ShawWeston Renoud en false https://talks.osgeo.org/foss4g-europe-2026/talk/HFJQ9H/ https://talks.osgeo.org/foss4g-europe-2026/talk/HFJQ9H/feedback/ A11 Talk 2026-07-01T15:30:00+03:00 15:30 00:30 Observing the earth is not so straightforward with many web map viewers. How about the poles? How can we easily change the current time, and see which time span each layer has? And what about the day/night cycle? Ifremer and Camptocamp have worked together to come up with a new software for this. We made the Sextant Viewer to be extremely easy to use, but also to address data sources and use cases that other map viewer on the market do not do as much. The Sextant Viewer shows Cloud-Native data, it switches effortlessly to globe mode using MapLibre, and it will fit snuggly inside any web site with its Web Component architecture. foss4g-europe-2026-5688-a-look-at-our-planet-with-the-sextant-viewer State of software Olivia Guyot en false https://talks.osgeo.org/foss4g-europe-2026/talk/ZPD9KG/ https://talks.osgeo.org/foss4g-europe-2026/talk/ZPD9KG/feedback/ A12 Talk 2026-07-01T10:30:00+03:00 10:30 00:30 The development is building on the rich ecosystem of European EO Platforms, as well as on open-source building blocks developed under EOEPCA+. In this talk we will review the EarthCODE initiative, archictecture and suite of services offered to the community, and we will set the scene for a community consultation to take place also at FOSS4G Europe. The scope of the community consultation will be to identify areas for improvement with respect to interoperability and cross-platform integrations. The identified topics will be addressed in the upcoming EarthCODE hackathon (30 Nov - 04 December 2026). foss4g-europe-2026-5539-earthcode-enabling-fair-open-earth-science-for-earth-action Remote Sensing Anca AngheleaSalvatore Pinto en false https://talks.osgeo.org/foss4g-europe-2026/talk/ZE8ZPT/ https://talks.osgeo.org/foss4g-europe-2026/talk/ZE8ZPT/feedback/ A12 Talk 2026-07-01T11:30:00+03:00 11:30 00:30 IGN (French National Map Agency) and its partners are launching the JUNN project : A dynamic 3D digital replica of the territory with online services to interact with (visualization, immersive navigation, simulation). A tool that aims to federate data, technologies, communities and existing initiatives to collectively gain efficiency for the ecological transition.​ This digital twin of France and its territories is a tool-based approach, supported by a consortium of public and private players, enabling the development of a common Open-Source architecture built in support of identified business usescases. Objectives are multiples: * Reducing the cost of local initiatives and facilitate their replication in other areas, * Establishing a framework for interoperability and interface between different projects * Setting up a science platform to facilitate the development of technological advances from R&D, * Building an ecosystem of services and business applications with high added value The project will include 3D data production with 3D mesh data and CityGML LOD 2.2 data. The 3D meshes will be generated using the Wasure software (https://github.com/lcaraffa/sparkling-wasure), which was developed based on research conducted by the Lastig research laboratory. This software will be further refined during the project, in collaboration with INRIA and GeometryFactory. Some of the CityGML data will also be produced by the IGN. Initial tests using the Open-Source Roofer software (https://github.com/3DBAG/roofer) have yielded promising results (https://batiment3d.ign.fr/) regarding what the Digital Twin data might look like. This platform is intended to serve as a hub for scientific research, where it will be possible to connect simulators that have access to the platform’s extensive dataset. This will be the case, for example, with the ICI project (https://x-ngilet.gitlabpages.inria.fr/html_covici/index.html), which offers models of epidemic spread and whose access to the data will improve predictions For visualization, we will use the open-source rendering engine iTowns (https://www.itowns-project.org/), to which IGN is a major contributor. Already high-performing, the close collaboration between the Digital Twin team and the iTowns development team will ensure that this tool is fully compatible with what we will be offering. We are proposing this presentation to give the OSGeo community an overview of the project. This presentation will introduce the various open-source software components we will be using, some of which we will be enhancing (Wasure, iTowns, Rok4, Roofer, etc.). foss4g-europe-2026-5437-junn-a-french-digitaltwin-initiative Use cases & applications lavenantRémi Ferrier en false https://talks.osgeo.org/foss4g-europe-2026/talk/JM8S7N/ https://talks.osgeo.org/foss4g-europe-2026/talk/JM8S7N/feedback/ A12 Talk 2026-07-01T12:00:00+03:00 12:00 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. Over the last few years, with support from a community crowdfunding campaign, we have been working to make QGIS a viable alternative for building Open Source Digital Twins. This talk will walk through the specific hurdle we faced and the solutions we implemented to get QGIS 3D ready for production work. We will move beyond 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. foss4g-europe-2026-4936-qgis-for-digital-twins Transition to FOSS4G Saber Razmjooei en false https://talks.osgeo.org/foss4g-europe-2026/talk/E79HRQ/ https://talks.osgeo.org/foss4g-europe-2026/talk/E79HRQ/feedback/ A12 Talk 2026-07-01T12:30:00+03:00 12:30 00:30 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. foss4g-europe-2026-5404-explore-open-source-tools-for-creating-digital-urban-models-with-mapstore Use cases & applications Lorenzo NataliTobia Di PisaStefano Bovio en false https://talks.osgeo.org/foss4g-europe-2026/talk/JXEHLZ/ https://talks.osgeo.org/foss4g-europe-2026/talk/JXEHLZ/feedback/ A12 Talk 2026-07-01T14:30:00+03:00 14:30 00:30 We are a group of companies: 1. "Geomatics Solutions", Ltd., created in 2002 by a group of specialists in the field of Geomatics. For 24 years, the company has been well proven in production and in delivery of services related to the creation, processing, and use of geospatial data. https://geosol.com.ua/about_en.html 2. Now managing the results of "Intelligence Systems GEO, Ltd." (ISGeo). ISGeo created in 1996 by the group of experts from the V.M. Glushkov Institute of Cybernetics and was the leading Ukrainian company in the sphere of geoinformation systems (GIS) and spatial database development. https://isgeo.com.ua/about Our companies have faced several crisis situations throughout their history. We'll share our experiences, both successful ones and those where we lost a significant portion of our developments and resulting data. We'll explain why and how we came to the OSGeo/FOSS4G ecosystem. Using our own examples, we'll highlight some of the challenges we encountered when working with proprietary software. We'll demonstrate the benefits of using OSGeo tools and projects. We'll also discuss how the latest developments from the Open GIS community helped us make decisions and keep our stack up to date with the help of annual FOSS4G conferences. We have experienced the following directions and their stages: 1) Technological development: a) Transition from paper maps to digital ones. The results of this work include CDs/DVDs, atlas books, and map brochures. We primarily worked with proprietary software. b) Transition from desktop applications to web-based alternatives. This resulted in static websites, Tile-Servers, and other server solutions. Partial transition to an open-source stack. We began implementing products such as GeoServer and Leaflet. 2012–2015. c) Transition from static Web 1.0 to dynamic Web 2.0 between 2010 and 2018. Adding interactivity and strong user engagement when using products. Also, a complete shift in the core stack in favor of open-source OSGeo products. d) Transition from an interactive web resource to the provision of services as a service starting in 2018. 2) Global instability: a) Covid-19. The transition to online. This facilitated the full use of server technologies. Products such as GeoServer and QGIS Server were very helpful. Period 2020–2022. b) Geopolitical instability. The situation in the country since 2014 and globally since 2022. Loss of access to servers, transition to cloud environments. Mobile workstations are being built. Licensing and access to licensed servers have become a pressing issue. Caching and desktop solutions are being used to address unstable internet access. QGIS, GDAL/OGR, and PROJ have proven effective. For data, use GeoPackage, GeoTIFF, PostgreSQL+Postgis and SpatialLite. At each stage, we used different technology stacks. We began using proprietary software that positioned itself as stable, secure, and supported. We were among the first official partners/distributors of such global monopolistic companies of the time as Pitney Bowes MapInfo Corporation, Avenza Inc. (Canada), Infotech Europe (Great Britain), and PCI Geomatics Inc. (Canada). But, with the very first change, we experienced difficulties in transferring already collected data to new stack solutions. The main problem was the proprietary software. The result of this work—a multitude of written add-ons/modules/extensions, a multitude of resulting data—was all "captive" to the proprietary software monopolies. We realized we didn't control the system and didn't own our own data. We were under the influence of mega-corporations. It was their speed of response to global trends and other global/local changes, especially local ones. American companies, which dominated our stacks, didn't quite understand or respond appropriately to our local challenges. The support systems turned out to be very slow and underperforming. Simple fixes for identified bugs took months, which impacted product release schedules. This negatively impacted the company's overall "respect." This also led to problems with understanding, as the mentality, culture, and values "across the ocean" differ greatly from those of Western and Central Europe. After 10-15 years of work, we made a difficult, but ultimately correct, decision. We completely rethought our entire approach to running our IT business. We rewrote all our existing developments. We partially migrated what we could to a new technology stack. This stack became the OSGeo/FOSS4G product suite. The main requirement was open-source code for the development product, as well as open data formats. No binary formats that limit performance, such as DLLs, Flash, and so on. The goal was to ensure that all modules and plugins could be maintained independently even if support for certain software ceased. All data was now stored exclusively in open formats. This set of open formats, those that can be opened using several open-source products, was key when selecting the software. Open source won't eliminate the problem of crisis situations or save us from something beyond our control. However, due to openness, we have complete control over the entire technology stack, all the results of years of development. And this is key for an IT company. Initially, when choosing, we were faced with a huge abundance of frameworks and libraries. This was confusing. We needed to avoid getting bogged down in a multitude of different solutions. Finding truly high-quality software with a strong, supportive community became a pressing issue. Initially, we looked at stars, forks, the dates and frequency of commits and project versions, and the number of contributors on GitHub. But due to the lack of funding, donations became scarce, and even good solutions disappeared from the market. We had to find alternatives or take on full support ourselves. Ultimately, we found a strong community sponsored by OSGeo/FOSS4G products. We enjoyed stable sponsorship, a huge community, and frequent and highly informative FOSS4G conferences. We learned a lot of best practices from these conferences. OSGeo/FOSS4G minimizes the risk of product discontinuation, provides stable support, and operates within a unified community. All OSGeo/FOSS4G products provide confidence and guarantee stability and long-term product support. foss4g-europe-2026-5532-how-to-survive-in-a-rapidly-changing-world-or-how-to-protect-your-data-from-the-captivity-of-proprietary-software Building a business with FOSS4G Vasyl Yasko en false https://talks.osgeo.org/foss4g-europe-2026/talk/ENLRK9/ https://talks.osgeo.org/foss4g-europe-2026/talk/ENLRK9/feedback/ A12 Talk 2026-07-01T15:00:00+03:00 15:00 00:30 Open geospatial ecosystems increasingly depend on collaboration between open source communities, universities, and private companies. While these actors often share common goals, building partnerships that last beyond short term funding or sponsorship remains a practical challenge. This talk explores sustainable collaboration models that balance openness with real world constraints such as institutional timelines, commercial objectives, and governance structures. Drawing on European examples, it highlights what enables long term cooperation: clear roles, shared governance, open contribution pathways, and mutual value creation across research, education, and industry. The session offers a practical perspective on designing collaborations that strengthen the FOSS4G ecosystem without compromising independence or open principles. Audience level: Beginner to intermediate Key takeaway: Sustainable open–enterprise collaboration is built on trust, clarity, and shared long term value. foss4g-europe-2026-5686-when-open-meets-enterprise-sustainable-collaboration-between-communities-universities-and-companies Octavian Borcan en false https://talks.osgeo.org/foss4g-europe-2026/talk/EYNXGT/ https://talks.osgeo.org/foss4g-europe-2026/talk/EYNXGT/feedback/ A12 Talk 2026-07-01T15:30:00+03:00 15:30 00:30 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. foss4g-europe-2026-5632-geollm-in-the-wild-open-source-ai-meets-geospatial Use cases & applications Florent Gravin en false https://talks.osgeo.org/foss4g-europe-2026/talk/JM9A8T/ https://talks.osgeo.org/foss4g-europe-2026/talk/JM9A8T/feedback/ A13 Talk 2026-07-01T10:00:00+03:00 10:00 00:30 This presentation explores GeoServer’s capabilities for publishing rich data models, including complex features with nested properties and multi-valued relationships, through standard OGC services and OGC API - Features. It focuses on recent developments such as the Smart Data Loader and Feature Templating extensions, highlighting both current capabilities and ongoing and planned enhancements within the GeoServer ecosystem. GeoServer already provides strong support for implementing view and download services for complex data models through its core architecture and a range of free and open-source extensions. Among these, App-Schema has long been the primary solution for modeling and exposing complex feature structures and enabling advanced vector data services, despite its steep learning curve. Building on these foundations, newer approaches are emerging to simplify and modernize the publication of rich data models. These include direct integration with NoSQL data sources such as MongoDB, leveraging their native document-oriented structures, as well as support for modern output formats like JSON-LD, which enables the embedding of explicit semantics alongside the data. The session will conclude with real-world use cases from organizations that have adopted GeoServer and GeoSolutions solutions, providing practical insights, architectural patterns, and lessons learned to help attendees effectively design and implement scalable, production-ready services for complex data models. foss4g-europe-2026-5621-publishing-rich-data-models-in-geoserver-with-smart-data-loader-and-feature-templating Andrea AimeNuno Oliveira en false https://talks.osgeo.org/foss4g-europe-2026/talk/E7GXTY/ https://talks.osgeo.org/foss4g-europe-2026/talk/E7GXTY/feedback/ A13 Talk 2026-07-01T10:30:00+03:00 10:30 00:30 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. foss4g-europe-2026-5565-supporting-precision-farming-with-geoserver-past-experiences-and-way-forward Andrea AimeSimone Giannecchini en false https://talks.osgeo.org/foss4g-europe-2026/talk/8QDHKS/ https://talks.osgeo.org/foss4g-europe-2026/talk/8QDHKS/feedback/ A13 Talk 2026-07-01T11:30:00+03:00 11:30 00:30 Five years ago, **Awesome Spectral Indices** (ASI) was launched to address a persistent gap in Earth observation workflows: while hundreds of spectral indices existed in the literature, their definitions were fragmented, inconsistently documented, and rarely designed for direct programmatic use. What began as a curated effort to standardize and consolidate these definitions has since evolved into shared open geospatial infrastructure. The first public release in 2021 included 66 indices structured under a common schema with explicit naming, formulas, application domains, and bibliographic references. A key design decision was the introduction of a cross-sensor band naming standard aligned with widely used satellite platforms such as Landsat, Sentinel, and MODIS. By enabling expressions like “`(N - R) / (N + R)`” to be both human-readable and machine-executable, ASI moved from being a static catalogue to a lightweight and interoperable specification. Over the past five years, the project has grown to more than 260 indices (v0.9.0) and expanded beyond a single repository into a multi-language ecosystem. Open-source APIs operationalize the specification in Python (*spyndex*), the Google Earth Engine Code Editor (*spectral*), and Julia (*SpectralIndices.jl*), alongside community-driven implementations such as the R package *rsi*. With over 1k GitHub stars, more than 200k downloads across PyPI and conda-forge, and alignment with the electro-optical STAC extension, ASI now functions as reusable infrastructure embedded in reproducible Earth observation workflows. This talk reflects on five years of technical and community development: the evolution from list to specification, a design that supports scientific completeness and implementation simplicity, and the role of metadata and versioning in ensuring long-term sustainability. It concludes with the next phase of development, including extensions to the band standard, richer metadata, expanded categorization, and API refinements aimed at strengthening interoperability and ensuring that spectral indices remain stable and accessible within the open geospatial ecosystem. foss4g-europe-2026-4955-from-ndvi-to-an-open-ecosystem-five-years-of-awesome-spectral-indices Remote Sensing David Montero Loaiza en false https://talks.osgeo.org/foss4g-europe-2026/talk/YWDSJ9/ https://talks.osgeo.org/foss4g-europe-2026/talk/YWDSJ9/feedback/ A13 Talk 2026-07-01T12:00:00+03:00 12:00 00:30 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. foss4g-europe-2026-5082-detecting-rooftop-solar-panels-with-deep-learning-using-open-remote-sensing-data-and-openstreetmap Remote Sensing Gefei Kong en false https://talks.osgeo.org/foss4g-europe-2026/talk/ENYAFG/ https://talks.osgeo.org/foss4g-europe-2026/talk/ENYAFG/feedback/ A13 Talk 2026-07-01T12:30:00+03:00 12:30 00:30 The Open Earth Monitor Cyberinfrastructure is an European project (2022-2026) that has brought together Earth Sciences and computer scientists that imagined and calculated a significant amount of worldwide and regional geospatial data products based on satellite data, from agriculture to forestry, from air quality to flood monitoring. Behind the science and technical progress done to support these results, it is essential to also expand their benefits beyond their traditional fields. Thus, work was done to assess to which extent the project’s results can assist in providing a clearer image of the risks to which the private European sector is exposed due to climate change. A special attention was given to the financial sector with an emphasis on the insurance branch, given its significant role in the stability of the economy, through its intermediary services to transfer and allocate financial capital. The team used the open source tool Climada to evaluate the potential of OEMC generated products in the analysis of risk assessment of the European private sector. This talk will present the results obtained. foss4g-europe-2026-5709-mapping-the-unavoidable-using-open-source-and-open-data-to-better-understand-climate-change-impacts-on-the-private-sector FOSS4G ‘Made in Europe’ Codrina Ilie en false https://talks.osgeo.org/foss4g-europe-2026/talk/GHPZKA/ https://talks.osgeo.org/foss4g-europe-2026/talk/GHPZKA/feedback/ A13 Talk 2026-07-01T14:30:00+03:00 14:30 00:30 This presentation will introduce the most important innovations from the GeoNetwork community and present the roadmap for 2026 and beyond. These include the ambitious GeoNetwork 5 initiative, new features of the modern GeoNetwork UI framework, and what lies ahead for existing GeoNetwork 4 instances. foss4g-europe-2026-5687-geonetwork-for-a-connected-europe State of software Olivia Guyot en false https://talks.osgeo.org/foss4g-europe-2026/talk/EXMNUS/ https://talks.osgeo.org/foss4g-europe-2026/talk/EXMNUS/feedback/ A13 Talk 2026-07-01T15:30:00+03:00 15:30 00:30 MapStore is a powerful open-source framework for building, managing and sharing web-based maps, dashboards and geostories directly in the browser. It supports a wide range of geospatial data and highly customizable viewer applications. In this talk, GeoSquare Belgium presents a real-world implementation of MapStore for the Department of Mobility and Public Works (DMOW) of the Flemish Government. What started as a proof of concept, has quickly evolved into a fully operational open-source WebGIS platform supporting data-driven mobility policy in Flanders. The platform integrates MapStore, GeoServer, GeoNetwork and PostgreSQL to power a geoportal that hosts multiple thematic applications. These applications combine interactive maps, dashboards and geostories to support internal decision-making while simultaneously publishing selected data and insights to citizens through public web services and embedded web applications. To illustrate the capabilities and flexibility of the platform, several use cases will be highlighted, such as bicycle infrastructure monitoring, supporting the deployment of intelligent traffic lights and using the custom developed photoviewer extension to view and navigate images on the map. We will also briefly touch on community contributions like OpenID integration, dynamic filtering, and translation updates. Join us to discover how MapStore’s flexible design enables organizations like DMOW to deliver both ready-to-use and customized user-friendly geospatial solutions. foss4g-europe-2026-5310-supporting-mobility-and-infrastructure-decisions-in-flanders-with-mapstore Use cases & applications Yanko Godaert en false https://talks.osgeo.org/foss4g-europe-2026/talk/B8UFHS/ https://talks.osgeo.org/foss4g-europe-2026/talk/B8UFHS/feedback/