2.0 -//Pentabarf//Schedule//EN PUBLISH 3W8Y9W@@talks.osgeo.org -3W8Y9W Registration en en 20251117T080000 20251117T170000 9.00000 Registration FOSS4G 2025 Auckland Conference Registration PUBLIC CONFIRMED Registration https://talks.osgeo.org/foss4g-2025/talk/3W8Y9W/ WG306 Foyer PUBLISH WDUCCK@@talks.osgeo.org -WDUCCK Morning Break en en 20251117T103000 20251117T110000 0.03000 Morning Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Morning Tea https://talks.osgeo.org/foss4g-2025/talk/WDUCCK/ WF Levels 5,6,7 Catering PUBLISH MFNPZ8@@talks.osgeo.org -MFNPZ8 Lunch Break en en 20251117T123000 20251117T133000 1.00000 Lunch Break This is the time where you can have a 60min break to recharge with Tea, Coffee, Juice, Water and Food as well as visit our exhibitors and sponsors. PUBLIC CONFIRMED Lunch https://talks.osgeo.org/foss4g-2025/talk/MFNPZ8/ WF Levels 5,6,7 Catering PUBLISH 9S9U8F@@talks.osgeo.org -9S9U8F Afternoon Break en en 20251117T150000 20251117T153000 0.03000 Afternoon Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Afternoon Tea https://talks.osgeo.org/foss4g-2025/talk/9S9U8F/ WF Levels 5,6,7 Catering PUBLISH ZWVCEH@@talks.osgeo.org -ZWVCEH Cloud-Native Geospatial for Earth Observation Workshop en en 20251117T090000 20251117T120000 3.00000 Cloud-Native Geospatial for Earth Observation Workshop The advent of cloud computing has revolutionised the capabilities of researchers and professionals globally, helping them to access and analyse Earth observation (EO) data more easily than ever. Despite the well-understood tools and technologies, such as cloud-optimised GeoTIFFs and the Spatio-Temporal Asset Catalog (STAC) and STAC API specifications, many EO professionals have not yet had the opportunity to practically apply these innovations. This workshop aims to bridge that gap by showcasing how cloud-native geospatial technologies simplify the process of working with EO data, using Python as the primary programming language. In part one of the workshop, we’ll give an introduction to cloud native geospatial, and then participants will get hands-on coding an Earth observation data science notebook from scratch, loading and visualising Landsat data. In part two, we’ll talk about Al Gore’s vision for a digital earth, and how we’re on the path to realising that vision, and then we’ll delve into a real-world case study focused on documenting land productivity metrics, a crucial component for monitoring the UN Sustainable Development Goal (SDG) indicators for 15.3.1, also using Landsat data. Then we’ll shift over to another example of a long time series of sea-surface temperature data, accessed from Source Coop, before concluding with a discussion session. Throughout the workshop, participants will gain hands-on experience and insights into how cloud-native geospatial technologies have significantly enhanced the ability to access and analyze large volumes of EO data. By the end of the session, attendees will have acquired practical examples and knowledge to further develop their skills in this innovative field. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/ZWVCEH/ WF603 Alex Leith Michelle Roby PUBLISH KZGHTZ@@talks.osgeo.org -KZGHTZ Exploring Cloud-Native Geospatial Formats: Hands-on with Raster Data Workshop en en 20251117T133000 20251117T163000 3.00000 Exploring Cloud-Native Geospatial Formats: Hands-on with Raster Data Workshop Ever wonder what GDAL is doing under the hood when you read a GeoTIFF file? Doubly so when the file is a Cloud-optimized GeoTIFF (COG) on a remote server somewhere? Have you been wondering what this new Zarr thing is all about and how it actually works? Then there's the whole Kerchunk/VirtualiZarr indexing to get cloud-native access for non-cloud-native data formats, what's that about? Cloud-native geospatial is all the rage these days, and for good reason. As file sizes grow, layer counts increase, and analytical methods become more complex, the traditional download-to-the-desktop approach is quickly becoming untenable for many applications. It's no surprise then that users are turning to cloud-based tools such as Dask to scale out their analyses, or that traditional tooling is adopting new ways of finding and accessing data from cloud-based sources. But as we transition away from opening whole files to now grabbing ranges of bytes off remote servers it seems all the more important to understand exactly how cloud native data formats actually store data and what tools are doing to access it. This workshop aims to dig into how cloud-native geospatial data formats are enabling new operational paradigms, with a particular focus on raster data formats. We'll start on the surface by surveying the current cloud-native geospatial landscape to gain an understanding of why cloud native is important and how it is being used, including: * the core tenets of cloud-native geospatial data formats * cloud-native data formats for both raster and non-raster geospatial data * the intersection with SpatioTemporal Asset Catalogs (STAC) and how higher-level STAC-based tooling can leverage cloud-native formats for efficient raster data access processing of cloud-native data Then we'll get hands-on and go deep to build up an in-depth understanding of how cloud native raster formats work. We'll examine the COG format and read a COG from a cloud source by hand using just Python, progressively grabbing data from the image until we can extract a target tile, all without using any image libraries. We'll repeat the same exercise for geospatial data in Zarr format to see how that compares to our experience with COGs. Lastly we'll turn our attention to Kerchunk/VirtualiZarr to see how these technologies might allow us to better optimize data access with non-cloud-native formats. #### Prerequisites This workshop expects some familiarity with geospatial programming in Python. Most of the notebook code is already provided, so any gaps in understanding don't necessarily prohibit completing the exercises. That said, a basic knowledge of STAC and Cloud-Native Geospatial Python tooling and working with rasters as single and multidimensional arrays is quite helpful. A good primer workshop is Alex Leith of Auspatious's [Cloud-Native Geospatial for Earth Observation Workshop](https://github.com/auspatious/cloud-native-geospatial-eo-workshop). It is recommended to work through those activities or have an equivalent knowledge prior to working through the notebooks in this workshop. ### Pre-workshop Prep We'll have a lot to cover in the workshop and time is against us. Please try to come with a working notebook execution environment already setup and ready to go. The [workshop repository README](https://github.com/jkeifer/cng-raster-formats) outlines three different options: build and run the docker container, use a GitHub Codespace, or run from a python venv managed via `uv`. Due to the uncertain quality of conference internet, a local option (docker or using `uv`) is recommended, but Codespaces can be useful for those that cannot run either of those options. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/KZGHTZ/ WF603 Jarrett Keifer PUBLISH PYJ8SB@@talks.osgeo.org -PYJ8SB AGM - OSGeo Oceania en en 20251117T170000 20251117T174500 0.04500 AGM - OSGeo Oceania OSGeo Oceania Annual General Meeting PUBLIC CONFIRMED AGM https://talks.osgeo.org/foss4g-2025/talk/PYJ8SB/ WF603 Elisa Puccioni OSGeo Oceania Board PUBLISH RQLV9M@@talks.osgeo.org -RQLV9M Let’s create Interactive Web Maps with the Open-Source WebGIS: Re:Earth Visualizer + Re:Earth CMS en en 20251117T090000 20251117T120000 3.00000 Let’s create Interactive Web Maps with the Open-Source WebGIS: Re:Earth Visualizer + Re:Earth CMS Experience **Re:Earth Visualizer + CMS** in this hands-on workshop focused on creating and managing interactive web maps — all without writing code. Re:Earth is a data platform that combines a powerful visual map editor (Visualizer) with a flexible content management system (CMS), enabling users to build and publish dynamic maps directly from the browser. You’ll learn how to design interactive maps, manage map layers and content through the CMS, and understand how the two components work together to streamline map publishing workflows. Whether you're new to GIS or looking for a modern alternative to traditional WebGIS tools, this session will provide a practical introduction to Re:Earth's low-code capabilities. ### **What to Expect:** - **Web Map Creation**: Learn to build interactive maps using Re:Earth Visualizer - **Content Management**: Manage layers, properties, and metadata with Re:Earth CMS - **Integrated Workflow**: Understand how CMS and Visualizer work together ### **Who Should Attend:** GIS users, educators, local government staff, and non-developers seeking a user-friendly, a data platform for building and managing interactive web maps — low coding required. ### Advance Preparation You can look our [online textbook](https://eukarya.notion.site/Workshop-textbook-29a16e0fb16580fda3fae3e62b93ce2b?source=copy_link) Please register your Re:Earth account in advance through [the official website](https://reearth.io/home). PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/RQLV9M/ WF613 Hinako Iseki Maher Alhamoui PUBLISH WZJEBN@@talks.osgeo.org -WZJEBN Build the Thing: A Hands-On Product Workshop for Geospatial Makers Workshop en en 20251117T090000 20251117T120000 3.00000 Build the Thing: A Hands-On Product Workshop for Geospatial Makers Workshop This workshop is for anyone with an idea—or the spark of one—for a geospatial product, tool, or app. Whether you’re just starting out or already working on something, this open, supportive session will help you move forward. Led by Stella Blake-Kelly, a product consultant and founder of spatial studio Cartisan, the workshop will walk participants through key stages of product discovery and design, including: • Ideation and framing your product vision • User and market research • Mapping out functionality • Designing wireframes or prototypes You’ll have time to work independently or in small groups, get feedback and guidance tailored to where you’re at, and learn from others along the way. Bring a laptop, your curiosity, and your creativity. By the end, you’ll come away with a better understanding of the product development process and, hopefully, momentum on your big idea. All are welcome: open source experts, newbies, developers, mappers, hackers, and dreamers. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/WZJEBN/ WF610 Stella Blake-Kelly PUBLISH YCAYBW@@talks.osgeo.org -YCAYBW International QField Day Workshop en en 20251117T133000 20251117T163000 3.00000 International QField Day Workshop Join us for the international QField Day! Discover the latest innovations and powerful enhancements in QField. Discover how it streamlines field data collection, simplifies workflows, and empowers professionals and organisations across various industries. Whether you're a long-time QField user, just getting started, or simply curious about cutting-edge field operations, this event is your chance to connect with the QField product team and gain valuable insights into how QField can elevate your projects. From rapid mapping to AI, plugins to community building—and even leading research efforts like the EU-funded egeniouss project—get ready for a half day of inspiration, knowledge sharing, and exploring how QField empowers people to map and understand the world, solving everyday tasks and global challenges alike. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/YCAYBW/ WF610 Marco Bernasocchi Daniel ODonohue Eliane Bernasocchi PUBLISH H7LS7U@@talks.osgeo.org -H7LS7U Introduction to GeoServer Workshop en en 20251117T090000 20251117T120000 3.00000 Introduction to GeoServer Workshop GeoServer is a much loved open-source project and one of the most popular web mapping services in the world. This workshop provides a gentle hands-on introduction in setting up and enjoying GeoServer. This workshop covers the advantages of using GeoServer; looking at the abilities of this open-source technology. This session is a great way to get started, geared towards those with no prior open source experience. Familiarity with GIS concepts is recommended for attendees, and you are welcome to bring your own data. We will start with a demonstration of GeoServer installation and touch on system requirements and installation of extensions * Hands-on publication of spatial data (vector, raster and database). * GeoServer styling and web mapping use * Preflight check-lists making sure your datasets, and web services, are ready for use. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/H7LS7U/ WF611 Jody Garnett's Room! Jody Garnett PUBLISH 37MXUT@@talks.osgeo.org -37MXUT GeoTools Geospatial Introduction Workshop en en 20251117T133000 20251117T163000 3.00000 GeoTools Geospatial Introduction Workshop Are you new to GeoSpatial? This GeoTools session is back by popular demand updated with Java 17 examples and the latest ImageN and CQL2 technologies. Offering a visual introduction for Java developers we will explore how you can integrate GIS services into your next project. For those new to the GeoSpatial scene we provide an introduction to spatial concepts and how to avoid common pitfalls. The workshop offers a steady series of workbooks introducing: * Feature creation * JTS Geometry * Coordinate Reference Systems and Re-projection * Geospatial data and spatial queries * Accessing large format rasters * Rendering, cartography and styling * Raster Operations with ImageN Covering both the concepts and the science of map making the workbooks serve as an excellent reference, but the focus is always on you and the code you need to get the job done. This is a hands-on workshop - so bring your Java IDE or command line and a sense of adventure. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/37MXUT/ WF611 Jody Garnett's Room! Jody Garnett PUBLISH AGFBHA@@talks.osgeo.org -AGFBHA Hands-on DGGS and OGC DGGS-API with DGGRID and pydggsapi Workshop en en 20251117T090000 20251117T120000 3.00000 Hands-on DGGS and OGC DGGS-API with DGGRID and pydggsapi Workshop Discrete Global Grid Systems (DGGS) are getting more attention, and with the new OGC API - DGGS standard released, it's a good time for the open-source community to get practical, hands-on experience. This workshop bridges the gap between the theory of DGGS and a working implementation. We'll show you why DGGS are so useful for integrating and indexing different data sources and spatial data analysis without the usual headache of map projections. Then, we work through a complete, real-world data pipeline using FOSS tools. In this workshop, you will: - take a standard geospatial file (like a GeoTIFF or GeoPackage). - use the command-line tool DGGRID (https://github.com/sahrk/DGGRID | https://dggrid.readthedocs.io/latest/ ), generate grids and index this data onto a hexagonal grid - we will introduce hierarchical indexing for ISEA3H and ISEA7H with the new Z3 and Z7 indexing systems in DGGRID - in the decond part, we set up and configure pydggsapi (https://github.com/LandscapeGeoinformatics/pydggsapi/ | https://pydggsapi.readthedocs.io/en/latest/), an open-source Python server that implements the newly (to be) released OGC API - DGGS standard. - we then point pydggsapi at the data you just created and launch the service - we explore several ways how to interact with your new web service using a browser, or curl/Python notebook, and maybe even QGIS, to make queries and retrieve data. Who should attend? This workshop is for developers, data managers, and generally DGGS and geospatial enthusiasts who want to learn how to publish their data using this new paradigm. Prerequisites: Participants should be comfortable with the command line and have a basic understanding of what geospatial raster and vector data are. We can use Docker or plain Miniconda/Micromamba/Pixi Python environments to ensure the dependencies are easy to set up for everyone. Bring your laptop (Win, Mac, Linux, *BSD might all work). By the end, you will understand the concepts of DGGS-indexed data and will have built a functioning DGGS-based web service yourself. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/AGFBHA/ WF502 Alexander Kmoch PUBLISH CVT8GC@@talks.osgeo.org -CVT8GC Getting Sentinel Data within Seconds with STAC Workshop en en 20251117T133000 20251117T163000 3.00000 Getting Sentinel Data within Seconds with STAC Workshop This hands-on workshop introduces a modern and efficient way to access and analyze Sentinel satellite data using STAC (SpatioTemporal Asset Catalog) APIs and the Microsoft Planetary Computer. You'll learn how to build scalable Earth observation workflows in Python—without downloading massive datasets manually. We’ll begin with an overview of STAC and the role of the Microsoft Planetary Computer as a cloud-native source for open geospatial data. Participants will learn how to search and access Sentinel-2 and Sentinel-1 imagery based on time, location, and cloud coverage—directly within Python using libraries like pystac-client, odc-stac, and xarray. During the workshop, you'll: - Search and preview Sentinel datasets using STAC - Fetch cloud-hosted imagery from Microsoft Planetary Computer - Visualize bands and calculate vegetation indices like NDVI, EVI, and RVI - Perform pixel-level analysis with minimal compute time - Build efficient, reproducible workflows using Jupyter notebooks By the end, you’ll have a working pipeline to go from area of interest to actionable insights in minutes—ideal for environmental monitoring, agriculture, and forest analysis. This workshop is perfect for developers, remote sensing analysts, or GIS professionals looking to simplify and accelerate their satellite data workflows. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/CVT8GC/ WF502 krishna lodha PUBLISH NJERTQ@@talks.osgeo.org -NJERTQ Building Geospatial AI Applications: From Data to Insights with Open Source Tools Workshop en en 20251117T090000 20251117T120000 3.00000 Building Geospatial AI Applications: From Data to Insights with Open Source Tools Workshop This hands-on workshop guides developers through building a complete geospatial AI application using modern open source tools. Participants will create a functional application that processes spatial data, generates AI-powered insights, and presents results through interactive visualizations - all running in the browser with no backend required. ### Technology Stack - **Visualization**: deck.gl for high-performance geospatial rendering - **App development**: Vercel AI SDK - **AI Integration**: AI APIs for intelligent data analysis - **Cloud Analysis**: Using MCP to access data and run cloud analysis from CARTO platform - **Development**: React with TypeScript ### Workshop Structure #### Hour 1: Foundation - Project setup with React, TypeScript - Simple LLM tool integration #### Hour 2: AI & data Integration - Loading and processing geospatial datasets (GeoJSON, CSV) - Controlling UI via AI #### Hour 3: Visualization and Integration - Creating interactive maps with deck.gl - Connecting CARTO maps & external tools - Postprocessing ### Requirements - JavaScript/TypeScript and React experience - Laptop with Node.js and modern browser - Anthropic API key Participants will leave with a complete, functional geospatial AI application and reusable code patterns for building similar applications. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/NJERTQ/ WF503 Felix Palmer PUBLISH XD8Z8K@@talks.osgeo.org -XD8Z8K Doing Geospatial with Python en en 20251117T133000 20251117T163000 3.00000 Doing Geospatial with Python With a low barrier to entry and large ecosystem of tools and libraries, Python is the lingua franca for geospatial development. Whether you are doing data acquisition, processing, publishing, integration or analysis, there is no shortage of solid Python tools to assist in your daily workflows. This workshop will provide an introduction to performing common GIS/geospatial tasks using Python geospatial tools such as OWSLib, Shapely, Fiona/Rasterio, and common geospatial libraries like GDAL, PROJ, pycsw, as well as other tools from the geopython toolchain. Manipulate vector/raster data using Shapely, Fiona and Rasterio. Publish data and metadata to OGC web services using pygeoapi, pygeometa, pycsw, and more. Visualize your data on a map using Jupyter and Folium. Plus a few extras in between! The workshop is provided using the Jupyter Notebook environment with Python 3. **Requirements for the Attendees** Please see https://geopython.github.io/geopython-workshop for details on how to setup the workshop before you attend. A Gitter channel exists at https://gitter.im/geopython/geopython-workshop for discussion and live support from the developers of the workshop. The workshop uses Jupyter Notebooks. Jupyter is an interactive development environment suitable for documenting and reproducing workflows using live code. As the installation of all dependencies on all platforms (Windows, Mac, Linux) can be quite involved and complex, this workshop provides all components within a Docker Image. In addition, geospatial web services like pygeoapi and pycsw in this workshop are provided by Docker images. The core requirement is to have Docker and Docker Compose installed on the system. Once you have Docker and Docker Compose installed you will be able to install the workshop without any other dependencies. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/XD8Z8K/ WF503 Tom Kralidis krishna lodha Jorge S. Mendes de Jesus PUBLISH ZFPBLE@@talks.osgeo.org -ZFPBLE QGIS PLUGIN DEVELOPMENT Workshop en en 20251117T133000 20251117T163000 3.00000 QGIS PLUGIN DEVELOPMENT Workshop 👥 Target Audience: GIS analysts, researchers, environmental scientists, developers and GIS enthusiasts Anyone with basic Python knowledge and interest in geospatial tools 🎯 Learning Objectives: Understand the QGIS plugin architecture and how QGIS interacts with Python Learn to set up a development environment for plugin creation Build and package a basic functional plugin Understand GUI design using Qt Designer Use PyQGIS API to access layers, features, and perform spatial tasks Learn tips for plugin debugging, deployment, and sharing via QGIS Plugin Repository PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/ZFPBLE/ WF510 Michel Nzikou Mamboukou PUBLISH BJJF7M@@talks.osgeo.org -BJJF7M QField & QFieldCloud: Hands-On Fieldwork Workshop en en 20251117T090000 20251117T120000 3.00000 QField & QFieldCloud: Hands-On Fieldwork Workshop QField is the field data collection app for QGIS. It is trusted over million times and used by hundreds of thousands of users every month. QFieldCloud is the synchronization and fieldwork management platform for QField and QGIS. This workshop will introduce the basics of QField and QFieldCloud to achieve effortless fieldwork. We will walk through the entire fieldwork process, including setting up your QGIS project, publishing the project via QFieldCloud, collecting data using the QField mobile app, and synchronizing field data back into your main dataset at the office. Basic knowledge of QGIS is desirable but not essential. Participants are asked to bring their own laptops and have the QField app pre-installed on their smartphone or tablet. The app is available for Android, iOS, Windows, Linux and MacOS. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/BJJF7M/ WF702 Ivan Ivanov Berit Mohr Mathieu Pellerin PUBLISH RUVQSJ@@talks.osgeo.org -RUVQSJ Cartography for Rebels: Building Beautiful Maps with Free Tools Workshop en en 20251117T133000 20251117T163000 3.00000 Cartography for Rebels: Building Beautiful Maps with Free Tools Workshop In this workshop, participants will learn how to create a map from start to finish using open source software such as QGIS and freely available open data. No prior experience is required - the session is designed to be beginner-friendly and welcoming to all skill levels. By working with open source tools and open data, participants will gain hands-on experience in map creation while exploring the power and flexibility of cost-effective, community-driven solutions. During the workshop, we’ll cover techniques such as generating hillshades from Digital Elevation Models (DEMs) and integrating them with other layers, including aerial or satellite imagery, to enhance map presentation. We’ll also explore data-driven symbology, rule-based labelling, and final steps to prepare the map for print. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/RUVQSJ/ WF702 Pierre Kurth Mike Gresham PUBLISH 83XWUZ@@talks.osgeo.org -83XWUZ Diving into pygeoapi Workshop en en 20251117T090000 20251117T120000 3.00000 Diving into pygeoapi Workshop pygeoapi is an OGC Reference Implementation supporting numerous OGC API specifications. Lightweight, easy to deploy and cloud-ready, pygeoapi's architecture facilitates publishing datasets and processes from multiple data sources to the Web. This tutorial will cover publishing geospatial data to the Web, and using the API from QGIS, OWSLib and a web browser. The workshop will cover the following OGC API standards: - OGC API - Features - OGC API - Coverages (OACov) - OGC API - Maps (OAMaps) - OGC API - Tiles (OATiles) - OGC API - Processes (OAProc) - OGC API - Records (OARec) - OGC API - Environmental Data Retrieval (EDR) - SpatioTemporal Asset Catalog (STAC) **Requirements for the Attendees** Please consult the workshop documentation at https://dive.pygeoapi.io, and ensure you are setup accordingly (https://dive.pygeoapi.io/setup) prior to attending the workshop. A Gitter channel exists at https://gitter.im/geopython/diving-into-pygeoapi for discussion and live support from the developers of the workshop. As the installation of all dependencies on all platforms (Windows, Mac, Linux) can be quite involved and complex, this workshop provides all components within a Docker Image. The core requirement is to have Docker and Docker Compose installed on the system. Once you have Docker and Docker Compose installed you will be able to install the workshop without any other dependencies. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/83XWUZ/ WF710 Tom Kralidis krishna lodha Jorge S. Mendes de Jesus PUBLISH RBEXYV@@talks.osgeo.org -RBEXYV Scalable Remote Sensing Workflows with Xarray Workshop en en 20251117T133000 20251117T163000 3.00000 Scalable Remote Sensing Workflows with Xarray Workshop Xarray is an evolution of rasterio and is inspired by libraries like pandas to work with raster datasets. It is particularly suited for working with multi-dimensional time-series raster datasets. With the growing ecosystem of spatial extensions like rioxarray and xarray-spatial and built-in support for parallel computing with Dask, it has become the de-facto standard for working with large spatio-temporal raster datasets. This workshop will show you a modern, scalable approach to remote sensing with cloud-native datasets and parallel computation.. 1. Basics of XArray 2. Basics of STAC and Dask 3. Computation with XArray 3. Cloud Masking 4. Extracting Time-Series 5. Scaling Computation with Coiled Pre-requisites: - This is an intermediate-level workshop where familiarity with Python is useful but beginners are welcome too. - We will be using [Google Colab](https://colab.google/) as the computing environment for the workshop. Participants will need a Google Account to access the platform. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/RBEXYV/ WF710 Ujaval Gandhi PUBLISH 3CNAVV@@talks.osgeo.org -3CNAVV Advanced PostGIS: Beyond the basics. Workshop en en 20251117T090000 20251117T120000 3.00000 Advanced PostGIS: Beyond the basics. Workshop PostGIS, sitting on top of Postgresql, is by most metrics, the most popular spatial database. Many videos are online about how to install and use PostGIS....many are from prior FOSS4G conferences and do a good job in getting you to understand the basics. Most of them, however, only scratch the surface when it comes to the power that can be wielded with PostGIS. This workshop seeks to explore a wide array of functions that may be used on a regular basis or are outside the scope of common spatial queries.. These include, but are not limited to: - Linear Referencing - Clustering - Rasters analytics - Vector Tiles There will also be an emphasis on exposing PostGIS data and functions to the web, to this end, there will be some usage of other software products (for example: Martin Vector tile server or pg_featureserv or pg_tileserv or PostgREST). Consideration will also be given to the ecosystem around PostGIS (For example: ogr_fdw) PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/3CNAVV/ WF711 Rhys Stewart PUBLISH PGV8BG@@talks.osgeo.org -PGV8BG Tile serving with MapLibre/Martin/Planetiler - base and overlays Workshop en en 20251117T133000 20251117T163000 3.00000 Tile serving with MapLibre/Martin/Planetiler - base and overlays Workshop In this workshop we will generate base map tiles from OSM data using Planetiler, set up Martin tile server, set up nginx to serve our sample web site that will use MapLibre GL JS to show the map. Additionally (time permitting), we will add a PostgreSQL server, and will use osm2pgsql to import extra data from the same OSM dump, and do on-the-fly tile generation from PG. What topics do we plan to cover in your workshop? – * generating base maps * setting up postgres with data * generate overlay tiles on the fly * serving tiles * visualizing tiles with MapLibre * adding data layers See https://github.com/maplibre/workshop?tab=readme-ov-file#pre-reqs PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/PGV8BG/ WF711 Yuri Astrakhan Stephanie May PUBLISH PBSLWX@@talks.osgeo.org -PBSLWX FILM EVENT: I Am the River, the River Is Me en en 20251117T180000 20251117T193000 1.03000 FILM EVENT: I Am the River, the River Is Me The Whanganui River in Aotearoa/New Zealand is the first river in the world to be recognized as a legal person, as a living and indivisible being. Māori river guardian Ned Tapa invites a First Nations Elder from Australia and his daughter, who are activists dedicated to saving their own dying river back home, on a five-day canoe trip down this sacred river. Joining them are Ned’s friends, his family, an international film crew and Ned’s dog Jimmy. For the Māori, the Whanganui is a living being – their ancestor. This belief has been institutionalized by New Zealand law as of 2017. Granting the river legal personhood is a way of environmental protection for the river, and as a way of legally validating the Māori worldview. The film is an invitation to experience these values: of thinking about our relationship to the world around us – to above all the natural world – as one of intergenerational care and guardianship rather than just ownership/use/extraction. The film is the result of a four-year long collaboration with the Māori community in Whanganui. https://iamtheriver.org/ PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/PBSLWX/ WG403 PUBLISH 3W8Y9W@@talks.osgeo.org -3W8Y9W Registration en en 20251118T080000 20251118T170000 9.00000 Registration FOSS4G 2025 Auckland Conference Registration PUBLIC CONFIRMED Registration https://talks.osgeo.org/foss4g-2025/talk/3W8Y9W/ WG306 Foyer PUBLISH ZHZB9B@@talks.osgeo.org -ZHZB9B Icebreaker en en 20251118T183000 20251118T223000 4.00000 Icebreaker Our Ice Breaker event will be held at the Maritime Room, New Zealand Maritime Museum. Corner of Quay and Hobson Street, Auckland. PUBLIC CONFIRMED Social Event https://talks.osgeo.org/foss4g-2025/talk/ZHZB9B/ The Maritime Room PUBLISH WDUCCK@@talks.osgeo.org -WDUCCK Morning Break en en 20251118T103000 20251118T110000 0.03000 Morning Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Morning Tea https://talks.osgeo.org/foss4g-2025/talk/WDUCCK/ WF Levels 5,6,7 Catering PUBLISH MFNPZ8@@talks.osgeo.org -MFNPZ8 Lunch Break en en 20251118T123000 20251118T133000 1.00000 Lunch Break This is the time where you can have a 60min break to recharge with Tea, Coffee, Juice, Water and Food as well as visit our exhibitors and sponsors. PUBLIC CONFIRMED Lunch https://talks.osgeo.org/foss4g-2025/talk/MFNPZ8/ WF Levels 5,6,7 Catering PUBLISH 9S9U8F@@talks.osgeo.org -9S9U8F Afternoon Break en en 20251118T150000 20251118T153000 0.03000 Afternoon Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Afternoon Tea https://talks.osgeo.org/foss4g-2025/talk/9S9U8F/ WF Levels 5,6,7 Catering PUBLISH EFLSHB@@talks.osgeo.org -EFLSHB B2B en en 20251118T140000 20251118T173000 3.03000 B2B Placeholder for B2B Event at FOSS4G 2025 PUBLIC CONFIRMED B2B https://talks.osgeo.org/foss4g-2025/talk/EFLSHB/ WG201 Exhibition & Catering Hamish Campbell PUBLISH GQLTEA@@talks.osgeo.org -GQLTEA Cloud-based Remote Sensing with QGIS and Google Earth Engine Workshop en en 20251118T090000 20251118T120000 3.00000 Cloud-based Remote Sensing with QGIS and Google Earth Engine Workshop Google Earth Engine is a cloud-based platform that enables working with large-scale earth observation datasets effectively. The new [Google Earth Engine Plugin for QGIS](https://github.com/gee-community/qgis-earthengine-plugin) brings this power to the desktop and enables QGIS users to combine their geospatial workflows with cloud-based datasets. The workshop will cover the following topics * Installing and setting up the Google Earth Engine Plugin for QGIS * Exploring the Google Earth Engine data catalog * Downloading images from GEE * Creating a Processing Model to use data from GEE Data Catalog. Pre-requisites: * Install the Google Earth Engine Plugin for QGIS: This workshops requires the Google Earth Engine Plugin for QGIS.The plugin can be installed by the Plugin Manager from the official QGIS plugin repository and involves a few extra steps to authenticate with your Google Earth Engine account and set the Google Cloud project. Visit the [QGIS Earth Engine Plugin Installation Guide](https://gee-community.github.io/qgis-earthengine-plugin/installation/) for step-by-step instructions. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/GQLTEA/ WF603 Ujaval Gandhi PUBLISH G8XDLT@@talks.osgeo.org -G8XDLT Building Spatial APIs in PostgreSQL with PostgREST Workshop en en 20251118T133000 20251118T163000 3.00000 Building Spatial APIs in PostgreSQL with PostgREST Workshop This workshop is a deep dive into building geospatial APIs directly from your PostgreSQL database using PostgREST and PostGIS—no backend frameworks required. You'll learn how to expose spatial data (like points, lines, and polygons) as RESTful endpoints, and perform geospatial operations such as distance queries, intersections, and bounding box filters using PostGIS functions. We’ll begin by setting up PostgreSQL with PostGIS and configuring PostgREST to serve your database as a REST API. Then, we'll walk through creating tables, views, and SQL functions to handle various spatial use cases. You'll learn how to query spatial data through HTTP, return GeoJSON responses, and optimize queries for performance. Participants will get hands-on experience with: Installing and configuring PostgREST Working with spatial data types and functions in PostGIS Creating and securing spatial endpoints Building and testing real-world use cases (e.g., find nearby features, filter by geometry) By the end of the session, you’ll have a fully working geospatial API built entirely with SQL—ready for integration with frontend apps, dashboards, or GIS tools. Ideal for developers, GIS analysts, and anyone interested in modern spatial data APIs without the complexity of writing backend code. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/G8XDLT/ WF603 krishna lodha PUBLISH 7S9CLN@@talks.osgeo.org -7S9CLN Terra Draw - cross-platform drawing library for all map applications Workshop en en 20251118T090000 20251118T120000 3.00000 Terra Draw - cross-platform drawing library for all map applications Workshop [Terra Draw](https://github.com/JamesLMilner/terra-draw) is developed and maintained by James Milner. The speaker is a author of maplibre-gl-terradraw that is Tarra Draw plugin for maplibre-gl-js. This workshop's proposed agenda includes two parts - presentation and hands-on: Firstly, introduction of Terra Draw will be delivered in order to let you understand what Terra Draw can bring to your map application. The next part will be hands-on exercise. As an example of use of Terra Draw, the workshop will show you how you can integrate drawing feature with Maplibre GL JS. The agenda of exercise will be: - Installation and setup basic functionality of raw Terra Draw - Advanced functinalities of Terra Draw (layer stying, events, adding data, etc) - Quick introduction and tutorial of maplibre-gl-terradraw plugin Each participant is expected to bring a laptop computer installed in [NodeJS v22 LTS](https://nodejs.org) and [VSCode](https://code.visualstudio.com/) to exercise Terra Draw in own computer with provided sample codes. The workshop will use Maplibre as an example, however participants can choose any mapping libraries such as Leaflet, OpenLayers if they prefer using it. If time is allowed, the exercise will show you how you can integrate Terra Draw with different map libraries other than Maplibre. Terra Draw has a unified API, so it will be pretty easier for you to adapt it once you will be familiar. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/7S9CLN/ WF613 Jin Igarashi PUBLISH TG98J9@@talks.osgeo.org -TG98J9 Create and Customise Your Own 3D Web Maps with TerriaJS Workshop en en 20251118T133000 20251118T163000 3.00000 Create and Customise Your Own 3D Web Maps with TerriaJS Workshop TerriaJS is an open-source framework for web-based geospatial catalogue explorers. It uses Cesium and Leaflet to visualise 2D and 3D geospatial data, and it supports over 50 different Web APIs, file formats and open data portals. It is almost entirely JavaScript in the browser, meaning it can even be deployed as a static website, making it simple and cheap to host. TerriaJS is used across the globe to create next-generation Digital Twin Platforms for open geospatial data discovery, visualisation and sharing - it is used to drive - [Digital Earth Australia Map](https://maps.dea.ga.gov.au/) - [Digital Earth Africa Map](https://maps.digitalearth.africa/) - [Pacific Map (Digital Earth Pacific)](https://map.pacificdata.org/) - [VIC Spatial Digital Twin](https://vic.digitaltwin.terria.io/) (Australian State Gov) - [Tokyo Digital Twin](https://info.tokyo-digitaltwin.metro.tokyo.lg.jp/) - and many others This hands-on TerriaJS workshop will walk you through configuring maps, adding your own data, and applying custom branding using JSON configuration files. You’ll also learn how to publish your map on GitHub Pages, making it accessible to others with just a shareable link. Perfect for anyone wanting to present spatial data and stories, no programming skills required. Participants should bring a laptop with a text editor (such as VSCode) and NodeJS installed. By the end, you’ll have your personalised TerriaJS map running locally and published online via GitHub Pages. For more information about Terria: - https://terria.io/ - https://github.com/TerriaJS/terriajs PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/TG98J9/ WF613 Nick Forbes-Smith Michael Holmes Zoran Kokeza PUBLISH RDZMQP@@talks.osgeo.org -RDZMQP Oxidize to Decarbonize. Building more sustainable geospatial processes with Rust Workshop en en 20251118T090000 20251118T120000 3.00000 Oxidize to Decarbonize. Building more sustainable geospatial processes with Rust Workshop Even though servers, data centers, and high-performance computers use a considerable amount of energy—and thus contribute significantly to carbon emissions, the environmental impact of computation is often overlooked. This is especially ironic in geospatial science, where much of the work aims to understand and protect natural resources. Remote sensing workflows can be resource intensive, and as data volumes and analysis complexity grows, so too do the computational demands. Efficiency becomes not just a practical issue, but an environmental concern. The tools we use may matter more than we think. This workshop explores how programming language choice can meaningfully reduce the environmental cost of geospatial analysis. While interpreted languages like Python and R are the most popular choices due to their accessibility and rich ecosystems, they often fall short at scale. Their reliance on runtime interpretation and garbage collection often translates into longer compute times and higher energy usage, and thus generate more emissions. Compiled languages like Rust and C++ can offer an alternative path. These languages are significantly more efficient, often completing tasks faster and with less energy consumption. Rust in particular is emerging as a powerful option for scientific computing. It provides memory safety without a garbage collector, using a system of ownership and borrowing to manage memory at compile time. The result is software that is fast and reliable, with minimal runtime overhead. Unlike C or C++, Rust helps prevent common errors such as buffer overflows, null pointer dereferencing, and data races on parallel processes, without sacrifice in performance. Rust’s growing adoption by major tech players, like Microsoft, AWS, Google, and even the Linux kernel project reflects its maturity and reliability. This support, highlight that Rust is more than an academic or niche choice, but a practical, long-term solution. This workshop will introduce core Rust concepts relevant to geospatial analysis and explore the growing Rust geospatial ecosystem—including libraries for spatial data handling, raster processing, modelling, and machine learning. In this hands-on session, we will explore Rust fundamentals and use eorst to compute cloud frequency over a two-year period from satellite Sentinel-2 imagery. The eorst crate is an open-source Rust library designed to simplify and accelerate geospatial raster processing. Inspired by tools like Rasterio, RIOS, Dask, and Open Data Cube, eorst wraps complex geospatial operations in high-level abstractions, while preserving the performance benefits of systems programming. It minimizes the overhead of abstraction, letting developers focus on the science rather than the plumbing. Originally developed to support the Spatial BioCondition framework for modeling ecosystem condition, eorst now underpins large-scale operational workflows for Queensland’s Department of the Environment, Tourism, Science and Innovation. The library's main features are: Efficient geospatial raster I/O On-the-fly projection and alignment. Tiling and parallel out-of-core processing. Raster point sampling. Zonal statistics. Mosaicking. Band math and time series operations. STAC integration. XGBoost and LightGBM inference. Optional OpenCV image processing. Rust also interoperates well with Python and R, making it a pragmatic choice for hybrid workflows and for teams gradually transitioning toward more efficient computation. As part of the session, we will also demonstrate how to create a simple Python wrapper using the pyo3 library, allowing Rust functionality to be accessed from Python. If you are curious about producing more sustainable geospatial analysis, this session will be a practical starting point. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/RDZMQP/ WF610 Leonardo Hardtke PUBLISH S9ENL7@@talks.osgeo.org -S9ENL7 Participatory mapping field survey and computer lab: QField integration into machine learning landcover classification within Digital Earth Pacific. Workshop en en 20251118T133000 20251118T163000 3.00000 Participatory mapping field survey and computer lab: QField integration into machine learning landcover classification within Digital Earth Pacific. Workshop FOSS4G abstracts: Workshop abstract: Participatory mapping field survey and computer lab: QField integration into machine learning landcover classification within Digital Earth Pacific. 3-hour workshop The objective of the workshop is to share a workflow to allow for field data, calibration, and validation of a land cover classification model output. Land cover classification: Land cover classification forms serves numerous functions including land cover accounting, monitoring land use changes, biodiversity and conservation monitoring, measurements of urban and agricultural expansion as well as forest inventories and national greenhouse gas inventories. This workshop will include two main components: 1. a field survey component where participants will be able to walk around Auckland to collect data points in QField for QGIS. 2. a computer lab component where participants will use Digital Earth Pacific Python Notebooks to generate a land cover map based on the data collected in their respective surveys. These participatory mapping workflows enable users from a range of disciplinary backgrounds to contribute to land cover mapping outputs. These outputs may be used for a range of applications including land cover classification. The learning outcomes of this workshop will include the following: 1. Participants will learn how to collect field data using QField 2. Participants will also be able to ingest this data into Digital Earth Pacific and through a Jupyter Notebook Environment 3. Participants will be able to build on introductory levels of Python programming knowledge. Within QField and Python, participants will be making use of the following tools and libraries:   QField workflows to be covered: Point data collection Collection of points for different land cover classes Transects Transects Polygons Collecting polygon areas of interest Accuracy assessments Ensuring data collected is within set thresholds of horizontal accuracy Python libraries to be covered: Pandas / Geopandas Vector data analysis and plotting odc-geo Web map plotting Rasterio Raster data analysis and plotting odc.stac Loading satellite data through Digital Earth Pacific Spatiotemporal Asset Catalogues. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/S9ENL7/ WF610 Nicholas Metherall - SPC PUBLISH ZRFGZW@@talks.osgeo.org -ZRFGZW GeoServer 3 Developers Workshop en en 20251118T090000 20251118T120000 3.00000 GeoServer 3 Developers Workshop Please attend this workshop to: * Get Started with the GeoServer 3 codebase * Orientation with a Tour of the GeoServer architecture * Introduction the service dispatch framework, including creating your own service * built chain and test facilities * Create a custom function for use with map styling * Create a custom process for use with style transformations and web processing service * Anatomy of a successful pull request Attendees will build their own GeoServer, learn a bit about how our community operates, and enjoy extending the base application. If you are a developer looking to support GeoServer, or join us for a sprint or bug-stomp, this workshop is great introduction. This course features hands-on development. We encourage and expect you to bring your favourite Java development environment. For a good time with open source join GeoServer today! PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/ZRFGZW/ WF611 Jody Garnett's Room! Jody Garnett PUBLISH 9A37UM@@talks.osgeo.org -9A37UM Getting Started with GeoNetwork 5: A Hands-On Developer Workshop en en 20251118T133000 20251118T163000 3.00000 Getting Started with GeoNetwork 5: A Hands-On Developer Workshop GeoNetwork is the most popular and loved open-source catalog, trusted by organizations to deliver on their geospatial data management objectives. GeoNetwork 5 is the future version based on Spring Boot, which will introduce some architectural innovations for developers. In this practical session we will show you what is boiling into the pot and you’ll learn how to: - Set up GeoNetwork 5 locally using Docker and Docker Compose alongside GeoNetwork 4.x for a hybrid test environment - Explore the new architecture, based on Spring Boot - Test core features such as OGC API Records and the new Formatter architecture, for output with real metadata examples - Navigate the codebase, understand how to contribute or where to start to build you custom application on GeoNetwork foundations Join us to jump-start your GeoNetwork 5 developments and help shape the future of this open-source metadata platform! PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/9A37UM/ WF611 Jody Garnett's Room! Antonio Cerciello Jody Garnett PUBLISH 7PKNY3@@talks.osgeo.org -7PKNY3 FOSS4G with .NET: A Hands-On Introduction for Spatial Developers Workshop en en 20251118T090000 20251118T120000 3.00000 FOSS4G with .NET: A Hands-On Introduction for Spatial Developers Workshop Is .NET Ready for the Spatial World? In 2014, Microsoft announced that the .NET Core platform would become open source. Since then, it has steadily gained traction within the open-source community. But how well does it fit within the FOSS4G (Free and Open Source Software for Geospatial) ecosystem? This workshop aims to explore exactly that—bridging the powerful, modern capabilities of the .NET platform with the rich, well-established tools in the OSGeo stack. Why Consider .NET for Geospatial Projects? There are several reasons why you might choose the .NET platform for geospatial applications: * Legacy integration: You may be working in an enterprise environment where .NET systems already exist, and you're looking to add spatial functionality. * Greenfield projects: You may be considering .NET for its modern tooling, performance, or cross-platform capabilities using .NET 8+. * C# language benefits: C# is expressive, type-safe, and comes with excellent async/await support—useful for scalable geospatial applications. * Strong IDEs: With Visual Studio and Visual Studio Code, .NET developers benefit from some of the best development environments available. This workshop is for anyone curious about using .NET as a viable toolset in geospatial development—whether for new applications or enhancing existing systems. Workshop Structure The workshop will consist of three short, focused programming examples. Each one introduces a different layer of the technology stack—from database interaction to frontend mapping. 1)Database Access with PostGIS Using Npgsql We’ll start at the data layer, using the Npgsql library to connect a C# application to a PostgreSQL/PostGIS database. Topics include: Connecting to PostGIS with connection strings Executing spatial queries Reading and writing geometry types using NetTopologySuite.IO.PostGIS Mapping results to C# objects 2)Handling Spatial Data in the Backend with NetTopologySuite Next, we move to the backend. The NetTopologySuite library provides robust spatial capabilities similar to JTS (Java Topology Suite). Topics include: Representing geometries such as points, polygons, and linestrings Performing spatial operations (e.g., intersection, buffering, distance) Building spatial APIs using ASP.NET Core 3)Serving Frontend Maps Finally, we bring everything to life in the browser. We will demonstrate how to use Blazor (a .NET-based frontend framework) alongside OpenLayers to create an interactive web map. Topics include: Serving GeoJSON data via Web API Displaying spatial data in OpenLayers/Leaflet in a .net-context. Adding interactivity: popups, hover info, and basic layer control This combination lets you stay within the C# ecosystem across the full stack while integrating with powerful open-source geospatial libraries. Who Should Attend? This workshop is ideal for: .NET developers new to the FOSS4G ecosystem who want to understand how to integrate spatial capabilities Project leaders or software architects evaluating .NET for geospatial projects Geospatial professionals who find themselves moving into or collaborating with teams using the .NET platform Whether you're an experienced backend developer or a GIS expert exploring new toolchains, this session will offer practical, real-world insights. Prerequisites Attendees should have: * Basic familiarity with C# and the .NET platform * Experience using an IDE such as Visual Studio or Visual Studio Code * A general understanding of geospatial concepts (e.g., coordinates, layers, spatial queries) Keywords .NET, PostGIS, Npgsql, NetTopologySuite, OpenLayers, Leaflet,C#, FOSS4G, spatial API PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/7PKNY3/ WF502 Joakim Svensberg Anders Dahlgren PUBLISH M3TF7B@@talks.osgeo.org -M3TF7B Collaborative Geospatial Workflows in Action: A Hands-On Alpha with Re:Earth Flow Workshop en en 20251118T133000 20251118T163000 3.00000 Collaborative Geospatial Workflows in Action: A Hands-On Alpha with Re:Earth Flow Workshop Experience **Re:Earth Flow** hands-on in this workshop where you’ll build and run geospatial data workflows entirely in your browser. Re:Earth Flow is a new open-source, visual ETL platform — currently in alpha — designed to make transforming spatial data more accessible, collaborative, and code-free. Working with real datasets, you’ll walk through how to clean, filter, join, and export spatial data using our flow-based UI. You’ll also get a preview of how real-time collaboration works in the browser — with no installs, no command line, and no fuss. This is your chance to test the tool early, give feedback, and help shape its roadmap. ### **What to Expect:** - **A Guided Walkthrough**: Learn how to build ETL workflows visually using Re:Earth Flow - **Real Datasets**: Import shapefiles or CSVs, perform spatial joins, and export to GeoJSON or 3D Tiles - **Live Collaboration**: Work with others in real time via our WebSocket-powered backend - **Open Feedback Loop**: Share what worked, what didn’t, and what you want to see next ### **Who Should Attend:** GIS users, planners, analysts, and developers looking for a modern, collaborative way to transform spatial data — right from the browser. No coding experience required. Textbook url: https://www.notion.so/Workshop-textbook-2a216e0fb16580b7a5eed7631cf3267a?pvs=73 PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/M3TF7B/ WF502 Kyle Waite PUBLISH MHHJE7@@talks.osgeo.org -MHHJE7 Exploring Cloud-Native Geospatial Formats: Hands-on with Vector Data Workshop en en 20251118T090000 20251118T120000 3.00000 Exploring Cloud-Native Geospatial Formats: Hands-on with Vector Data Workshop Cloud-native geospatial is all the rage these days, and for good reason. As file sizes grow, layer counts increase, and analytical methods become more complex, the traditional download-to-the-desktop approach is quickly becoming untenable for many applications. It's no surprise then that users are turning to cloud-based tools to scale out their analyses, or that traditional tooling is adopting new ways of finding and accessing data from cloud-based sources. But as we transition away from opening whole files to now grabbing ranges of bytes off remote servers it seems all the more important to understand exactly how cloud-native data formats actually store data and what tools are doing to access it. This workshop aims to dig into how cloud-native geospatial data formats are enabling new operational paradigms, with a particular focus on vector data formats. Unlike its raster workshop counterpart, this workshop will be a bit more experimental. Vector data formats tend towards greater complexity than raster formats, so exactly how deep we get into which topics will be dependent on the audience’s interests and the time available. Broad themes to explore might include: * GeoJSON: what is it, what does it represent, and how it is not cloud-native * Well-Known Text/Binary (WKT/WKB): how these vector formats work and why they are important in GeoParquet * GeoParquet: how does parquet store data, how geo maps into that paradigm, and what it takes to read some subset of data from a parquet file * Other cloud-native formats like FlatGeobuf, PMTiles, etc. * Practical considerations when using these formats The content of this workshop aims to not only be theoretical: a strong goal is to be as hands-on with these formats as possible by working with them in Python without any specific geospatial format libraries. We’ll look at interacting with object storage directly, to pull down files and fragments and inspect them, to build up working understanding of what common higher-level tooling does under the hood and abstracts away from users. #### Prerequisites This workshop expects some familiarity with geospatial programming in Python and a basic understanding of the vector data model and its utility. Most of the notebook code is already provided, so any gaps in understanding don't necessarily prohibit completing the exercises. That said, some knowledge of the geospatial vector formats and tooling is quite helpful. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/MHHJE7/ WF503 Jarrett Keifer PUBLISH GLTDBP@@talks.osgeo.org -GLTDBP The Deep Magic of QGIS Cartography Workshop en en 20251118T133000 20251118T163000 3.00000 The Deep Magic of QGIS Cartography Workshop QGIS is packed with powerful tools just waiting to be discovered—tools that can take your cartography to the next level. Why settle for a plain map when you can wow your audience? In this workshop we'll be exploring the cartographic features in QGIS that you didn't even know existed. We'll stop along the way to discuss our recommendations for pretty mapping, and what we can learn from our favourite cartographers. A moderate level of QGIS experience is recommended. You need to be familiar with handling layers, basic styling and comfortable finding your way around QGIS. (We've got a lot to cover, so don't have time to cover the fundamentals!) PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/GLTDBP/ WF503 Nyall Dawson Mathieu Pellerin PUBLISH KZDMU9@@talks.osgeo.org -KZDMU9 Semantic Interoperability made easy with OGC Building Blocks Workshop en en 20251118T133000 20251118T163000 3.00000 Semantic Interoperability made easy with OGC Building Blocks Workshop Hands on opportunity to build data schemas that self-document and deliver them using FOSS4G software. Will include schema design, server and client options, and introduce you to the open source OGC Building Blocks for defining, testing, composing and publishing data and metadata that means something to end users. Focuses on JSON, JSON-LD and Linked Data, GeoDCAT and STAC, and OGC API Processes and provenance tracing to make your data and processing chains understandable and trustworthy. Participants will ideally have a github account, and pre-install docker on your own laptops - and by preference preload docker images by following the instructions at: https://ogcincubator.github.io/bblocks-docs/build/local Work shop will be a mix of "theory" - an exploration of the rationale and approach to be discussed - practical hands-on and discussion about the needs and opportunities in the FOSS4G community. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/KZDMU9/ WF510 Robert Atkinson PUBLISH MUKQWL@@talks.osgeo.org -MUKQWL Introduction to QField plugin authoring Workshop en en 20251118T090000 20251118T120000 3.00000 Introduction to QField plugin authoring Workshop The workshop will introduce participants to QField’s plugin framework and its two main plugin types: app-wide plugins and project-scoped plugins. We will look into the decision-making around settling on Javascript/QML as the scripting language and look at its strengths. We will then go through several practical plugin building examples that will cover: Integration with online REST API endpoints; Georeferenced visual map canvas overlays in QML language Feature creation and iteration via plugin Customization of QField user interface The workshop will also provide participants with resources to further increase their knowledge beyond the workshop session itself. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/MUKQWL/ WF511 Berit Mohr Mathieu Pellerin PUBLISH TAJSZZ@@talks.osgeo.org -TAJSZZ Running and Auto Scaling Geoserver and PostgreSQL/PostGIS without managing servers in the AWS cloud Workshop en en 20251118T133000 20251118T163000 3.00000 Running and Auto Scaling Geoserver and PostgreSQL/PostGIS without managing servers in the AWS cloud Workshop The workshop will walk you through the steps required to launch the Geoserver standard docker distribution and host this on AWS Fargate. AWS Fargate is a serverless, pay-as-you-go compute engine that lets you focus on building applications without managing servers. GeoServer can leverage a variety of data sources including PostgreSQL/PostGIS. PostGIS is a spatial database extender for the PostgreSQL object-relational database. With AWS support for PostgreSQL/PostGIS available in Aurora Serverless, we will explore connecting GeoServer to a PostgreSQL source to illustrate a multi-tier architecture. We will also explore scaling out the GeoServer web tier leveraging shared file system using Amazon Elastic File Service (EFS). The intended audience will be Geoserver admins/users that are interested in running Geoserver on AWS in a highly available fashion with minimal server management needs. NOTE: You will need a Internet connected laptop with browser to access the AWS console environment. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/TAJSZZ/ WF511 John Hildebrandt PUBLISH QTAWL7@@talks.osgeo.org -QTAWL7 Modelling Climate Risks Using NASA Earthdata Cloud & Python APIs Workshop en en 20251118T090000 20251118T120000 3.00000 Modelling Climate Risks Using NASA Earthdata Cloud & Python APIs Workshop Predicting and managing environmental risks of various climate-related disasters—e.g., wildfires, drought, and floods—is challenging and critical worldwide. Part of the difficulty is that historical norms (e.g., from last century) for the frequency of such extreme climate events are no longer sufficient to infer the frequency of future disasters. These natural risks are intrinsically linked to the dynamic distributions—varying both temporally and spatially—of surface water, precipitation, vegetation, and land use. These distributions can be modelled for forecasting and analysis (enabling quantification of these environmental risks) using hundreds of petabytes of relevant Earth science data available through [NASA's Earthdata Cloud](https://www.earthdata.nasa.gov/). With the dramatic growth in the availability of such data, today's earth scientists benefit from a strong understanding of open science practices and of cloud-based data intensive computing that enable reproducibly analyzing and assessing changing risk profiles. This workshop provides hands-on examples of using cloud-based infrastructure and data products from [NASA Earthdata Cloud](https://www.earthdata.nasa.gov/) for the analysis of environmental risk scenarios. This involves constructing quantitative estimates of changes in hydrological water mass balance over various defined geographical regions of interest and time windows. The goal is to build enough familiarity with generic cloud-based Jupyer/Python workflows and with remote-sensing data to enable adapting and remixing examples for other region-specific contexts. The workshop's design reinforces best practices of data-proximate computing and of reproducibility (as supported by NASA's [Open Science](https://science.nasa.gov/open-science-overview) and [Transform to Open Science (TOPS)](https://nasa.github.io/Transform-to-Open-Science/) initiatives). Participants are expected be familiar with raster data and common geospatial data conventions. Ideally, they are comfortable using a shell or a command-line interface to interact with data & programs. They should also be comfortable using common scientific Python libraries (e.g., NumPy, Pandas) and related Python data structures (e.g., tuples, dicts, lists, NumPy arrays, Pandas dataframes). There is a brief overview of Xarray, Hvplot, & Geoviews; prior exposure to those Python libraries is useful but not mandatory. Prior experience using Jupyter notebooks and writing short snippets of Python code is helpful. **Approximate schedule**: + *minute 0-19*: Introduction & Setup (logging in, configuring NASA Earthdata credentials) + *minute 20-29*: Reminders about GIS prerequisites: coordinate systems, data formats (if required) + *minute 30-49*: Overview of PyData tools for geographic data: [Rasterio](https://rasterio.readthedocs.io/en/stable/index.html) & [Xarray](https://docs.xarray.dev/en/stable/index.html) (if required) + *minute 50-59*: Break + *minute 60-79*: Overview of PyData visualisation tools: [Hvplot](https://hvplot.holoviz.org/) & [Geoviews](https://geoviews.org/) (if required) + *minute 80-99*: Using NASA Earthdata Products (DIST, DWSx) + *minute 100-109*: Using PyStac for retrieving data + *minute 110-119*: Break + *minute 120-144*: Case study: wildfires + *minute 145-169*: Case study: flooding + *minute 170-179*: Wrap-up The workshop starts by getting participants logged into the cloud infrastructure and verifying their NASA Earthdata Cloud credentials. This is followed by a quick, non-comprehensive overview of GIS prerequisites and Python approaches to manipulating and visualizing geospatial data. The schedule above will be adapted to suit the audience needs (i.e., by increasing or decreading time allocated in each section as appropriate). The hands-on case studies rely on the [OPERA (Observational Products for End-Users from Remote Sensing Analysis)](https://podaac.jpl.nasa.gov/OPERA) suite of data products; in particular, they use two particular categories of data products: [DSWx (Dynamic Surface Water Extent)](https://d2pn8kiwq2w21t.cloudfront.net/documents/ProductSpec_DSWX_URS309746.pdf) and [DIST (Land Surface Disturbance)](https://lpdaac.usgs.gov/documents/1766/OPERA_DIST_HLS_Product_Specification_V1.pdf). The workflows presented extend notebook examples drawn from the extensive [OPERA Applications repository](https://github.com/OPERA-Cal-Val/OPERA_Applications). This workshop—co-developed by [MetaDocencia](https://www.metadocencia.org) & [2i2c](https://2i2c.org)—is part of NASA's [Open Science](https://science.nasa.gov/open-science-overview) and [Transform to Open Science (TOPS)](https://nasa.github.io/Transform-to-Open-Science/) initiatives. An important goal is to reinforce principles of reproducibility and open science-based workflows (as exemplified in TOPS OpenCore, the introductory suite of open science curricula including Open Science 101). PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/QTAWL7/ WF702 Dhavide Aruliah PUBLISH ABSN39@@talks.osgeo.org -ABSN39 H-O-T-T-O-G-O: Mobile Apps That Support Disaster Response and Climate Resilience Workshop en en 20251118T133000 20251118T163000 3.00000 H-O-T-T-O-G-O: Mobile Apps That Support Disaster Response and Climate Resilience Workshop Disaster doesn’t wait for a stable internet connection—or for your laptop to boot up. That’s why the Humanitarian OpenStreetMap Team (HOT) and its global contributors have developed and adapted a suite of mobile-ready tools designed for field mappers, community organizers, and first responders. This workshop introduces participants to the HOTTOGO Toolkit: 🧭 MapSwipe – Tap and swipe to prioritize satellite imagery for mapping. 📍 EveryDoor – Add points of interest or update OpenStreetMap data right from your phone. 📝 KoboToolbox / ODK Collect – Build and deploy surveys to collect local knowledge, damages, needs, and more. 🗺️ Organic Maps / OsmAnd – Navigate and visualize OSM data offline in disaster zones. ✍️ Field Papers – Go analog with printed maps and draw-on mapping for truly offline areas. 🧩 Mapillary / KartaView – Contribute street-level imagery from your mobile for detailed, up-to-date visuals. The session will include: Live demos of each app Real-world use cases from climate-vulnerable and disaster-prone communities in Asia-Pacific Hands-on mini-exercises Tool selection guide: how to choose the right combo for your local mapping needs PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/ABSN39/ WF702 MIKKO TAMURA PUBLISH FGATAY@@talks.osgeo.org -FGATAY pgRouting basic workshop en en 20251118T090000 20251118T120000 3.00000 pgRouting basic workshop 1. Prepare Data 1.1. Prepare the database 1.2. Get the Workshop Data 1.3. Upload data to the database 1.4. Chapter: Appendix 2. Pedestrian Routing 2.1. pgr_dijkstra 2.2. pgr_dijkstraCost 3. Vehicle Routing 3.1. Routing for vehicles 3.2. Cost manipulations 4. Graph views 4.1. The graph requirements 4.2. pgr_extractVertices 4.3. pgr_connectedComponents 4.4. Preparing the graphs 5. SQL function 5.1. The application requirements 5.2. Additional information handling 5.3. Geometry handling 5.4. Creating an SQL Function PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/FGATAY/ WF710 Vicky Vergara Joseph Percival PUBLISH XLSDYG@@talks.osgeo.org -XLSDYG Simulating Sustainable Urban Spaces on AWS Workshop en en 20251118T133000 20251118T163000 3.00000 Simulating Sustainable Urban Spaces on AWS Workshop Extreme heat poses major threats to human and environmental health and safety. In this workshop, you use Amazon SageMaker AI and open data from Amazon Sustainability Data Initiative (ASDI) to uncover patterns in vegetation and temperature, understand risks to urban areas, and simulate solutions that reduce risk to communities. In particular, you will: - Investigate the relationship among temperature, vegetation, and other environmental factors (such as water and soil build-up) using Amazon SageMaker Studio - Gain hands-on experience working with geospatial data using AWS services - Learn about the Amazon Sustainability Data Initiative (ASDI) to access open satellite imagery data (USGS Landsat) - Build and deploy a machine learning model to simulate more sustainable outcomes (less heat risk) This workshop is aimed at individuals who want to learn how Artificial Intelligence and Machine Learning can help make predications based on open source data, individuals who want to experiment with the power of satellite imagery, as well as individuals who want to on-ramp to cloud-native geospatial workflows. No specific background knowledge is required. This workshop provides step-by-step instructions along with the code required to run each step. Participants can also elect to make additional, optional coding improvements. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/XLSDYG/ WF710 Guyu Ye PUBLISH BMWWR9@@talks.osgeo.org -BMWWR9 Standalone Web Maps, No Platform Required en en 20251118T090000 20251118T120000 3.00000 Standalone Web Maps, No Platform Required In this workshop, we will walk through how to build a map inside a GitHub repository, hosted with GitHub Pages, and built entirely with open source tools. Workshop materials and individual modules with step-by-step instructions are under construction at [https://github.com/mizmay/standalone_web_maps_foss4g2025](https://github.com/mizmay/standalone_web_maps_foss4g2025) and viewable at [mizmay.github.io/standalone_web_maps_foss4g2025/](https://mizmay.github.io/standalone_web_maps_foss4g2025/workshop/welcome/). Attendees are encouraged to come prepared with a laptop, having completed at least step 1. Key concepts / technologies: - [PMTiles](https://docs.protomaps.com/pmtiles/): because they work via HTTP, PMTiles (portable map tiles) do not require a map server to deliver tiled map data to your browser. - [Protomaps](https://protomaps.com/): is a global basemap stored in PMTiles that can be downloaded from a bounding box and customized to meet your needs. - [Overpass](https://overpass-turbo.eu/#): is an API and querying language for OpenStreetMap that can be used to extract select data you want to use to add context and fidelity by styling them within your basemap - [GitHub Pages](https://docs.github.com/en/pages): provide a built-in web hosting platform for any Github repo. Provided your repo is under 1 GB of data, achievable for most project or site-level maps, this is all you need to host your map - [Maplibre GL JS](https://maplibre.org/maplibre-gl-js/docs/): is an open source, vector tile map rendering library for Javascript. It supports PMTiles and can be used to control how your data from PMTiles and other sources is rendered and displayed. PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/BMWWR9/ WF711 Stephanie May PUBLISH VYW8GM@@talks.osgeo.org -VYW8GM Open Data, Open Source, Open Standard: Quickly build your digital twin city with mago3D Workshop en en 20251118T133000 20251118T163000 3.00000 Open Data, Open Source, Open Standard: Quickly build your digital twin city with mago3D Workshop This workshop provides participants with the experience of creating a digital twin city by utilizing open data from the region where FOSS4G is held. Participants will collect open data, transform it using open-source tools (mago3DTiler, mago3DTerrainer), and experience the full workflow of serving the data according to OGC (Open Geospatial Consortium) standards. They will visualize city buildings and terrain in 3D, overlaying road layers and satellite imagery to construct a realistic urban environment. Additionally, participants will transform and overlay point cloud data, and then plant trees in the city. This entire process relies on open data, open source, and open standards, allowing participants to experience the remarkable creation of a digital twin city from scratch within just three hours on an unprepared PC. https://github.com/Gaia3D/mago3d-doc/blob/main/foss4g-2025/README.md This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00143336, NTIS Grant: 2610000396) This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2025-02317649, NTIS Grant: 2610000447) PUBLIC CONFIRMED Workshop https://talks.osgeo.org/foss4g-2025/talk/VYW8GM/ WF711 Yeonhwa Jeong Jungin Yoon Sung-Jun Cho PUBLISH 3W8Y9W@@talks.osgeo.org -3W8Y9W Registration en en 20251119T080000 20251119T170000 9.00000 Registration FOSS4G 2025 Auckland Conference Registration PUBLIC CONFIRMED Registration https://talks.osgeo.org/foss4g-2025/talk/3W8Y9W/ WG306 Foyer PUBLISH D8ARS7@@talks.osgeo.org -D8ARS7 Conference Dinner en en 20251119T180000 20251119T220000 4.00000 Conference Dinner Join the community in celebrating FOSS4G over dinner and live music. Hilton Hotel, Auckland waterfront. 147 Quay Street, Auckland Central PUBLIC CONFIRMED Social Event https://talks.osgeo.org/foss4g-2025/talk/D8ARS7/ Hilton Hotel PUBLISH K3LWDA@@talks.osgeo.org -K3LWDA Travel Grant Program Breakfast en en 20251119T070000 20251119T083000 1.03000 Travel Grant Program Breakfast Travel Grant Program Breakfast Event. Held at Scarecrow 33 Victoria Street East, Auckland Central This event is available to our Travel Grant recipients. PUBLIC CONFIRMED Social Event https://talks.osgeo.org/foss4g-2025/talk/K3LWDA/ Scarecrow PUBLISH WDUCCK@@talks.osgeo.org -WDUCCK Morning Break en en 20251119T103000 20251119T110000 0.03000 Morning Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Morning Tea https://talks.osgeo.org/foss4g-2025/talk/WDUCCK/ WG201 Exhibition & Catering PUBLISH MFNPZ8@@talks.osgeo.org -MFNPZ8 Lunch Break en en 20251119T123000 20251119T133000 1.00000 Lunch Break This is the time where you can have a 60min break to recharge with Tea, Coffee, Juice, Water and Food as well as visit our exhibitors and sponsors. PUBLIC CONFIRMED Lunch https://talks.osgeo.org/foss4g-2025/talk/MFNPZ8/ WG201 Exhibition & Catering PUBLISH 9S9U8F@@talks.osgeo.org -9S9U8F Afternoon Break en en 20251119T150000 20251119T153000 0.03000 Afternoon Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Afternoon Tea https://talks.osgeo.org/foss4g-2025/talk/9S9U8F/ WG201 Exhibition & Catering PUBLISH ZGJA3V@@talks.osgeo.org -ZGJA3V Opening Ceremony en en 20251119T083000 20251119T093000 1.00000 Opening Ceremony The opening ceremony for FOSS4G Auckland 2025 PUBLIC CONFIRMED Opening Ceremony https://talks.osgeo.org/foss4g-2025/talk/ZGJA3V/ WG403 PUBLISH 8BZ9AW@@talks.osgeo.org -8BZ9AW Māori Maps: ‘To the gate’ of intellectual belonging in Aotearoa en en 20251119T093000 20251119T100000 0.03000 Māori Maps: ‘To the gate’ of intellectual belonging in Aotearoa Over five years, and more than twenty thousand kilometres, our small team visited almost every ancestral Māori community of New Zealand to create the first ever map of ancestral marae. Our mission is to reconnect descendants with marae through our web platform – Maorimaps.com. Now 16 years in existence, Māori Maps now guides the 30 thousand (and growing) monthly visitors to the virtual gateway of 780-plus ancestral marae of Aotearoa. Hirini’s keynote reflects on the genesis of Māori Maps and the founding ethic of “to the gate” that guided its creation. Grounded in the ritual of encounter (pōwhiri), “to the gate” embodies both a philosophical stance and a practical approach to protecting indigenous data sovereignty and intellectual belonging. This talk will examine how this principle shaped the project’s framework and continues to inform its responsibilities to marae communities. It will also consider the pathways in managing restricted and non-restricted knowledge—tapu and noa, public and private—in the context of digital platforms and “free and open source” environments. PUBLIC CONFIRMED Keynote https://talks.osgeo.org/foss4g-2025/talk/8BZ9AW/ WG403 Hirini Tane PUBLISH VLQH8H@@talks.osgeo.org -VLQH8H We don't build tech, but we build communities. en en 20251119T100000 20251119T103000 0.03000 We don't build tech, but we build communities. This keynote explores how feminist mappers from Geochicas around the world are transforming tech spaces by centering care, visibility, and collective learning in their communities. By grounding their work in community relationships, they are creating critical forms of human infrastructure that sustain open technologies. We often measure innovation through lines of code, data layers, or new platforms, but behind every map lies something deeper: the communities that make collaboration possible. In this talk, I reflect on the power of collective organizing and how building and strengthening communities is, in itself, a form of technological innovation. PUBLIC CONFIRMED Keynote https://talks.osgeo.org/foss4g-2025/talk/VLQH8H/ WG403 Selene Yang Rappaccioli PUBLISH 9KDXDY@@talks.osgeo.org -9KDXDY QGIS Feature Frenzy en en 20251119T110000 20251119T112500 0.02500 QGIS Feature Frenzy QGIS is packed full of incredible features! You can work with this software for years, and still discover new, weird, and wonderful tricks on a regular basis. Every new release contains a raft of enhancements and whole new areas of functionality. In this talk, Marco Bernasocchi (QGIS.org chairperson) and Nyall Dawson (QGIS software developer) will run through a few of their favourite features in QGIS, including highlights from the last few releases, and take a look at what's about to be unleashed in QGIS 4.0. With an eye on the future, they'll talk about how you can contribute to QGIS, and what the future might hold for this community-driven open source project. Stay around for the following 'Ask me anything' session with Marco and Nyall. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/9KDXDY/ WG403 Marco Bernasocchi Nyall Dawson PUBLISH YVWM8U@@talks.osgeo.org -YVWM8U QGIS "Ask me Anything" session en en 20251119T113000 20251119T115500 0.02500 QGIS "Ask me Anything" session Start thinking about those burning questions that YOU want answered about QGIS. Quiz us about QGIS features, how the project is run, challenges and what the future holds. Don't hold back! PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/YVWM8U/ WG403 Marco Bernasocchi Nyall Dawson PUBLISH VFK79A@@talks.osgeo.org -VFK79A Is Zarr the new COG? en en 20251119T120000 20251119T122500 0.02500 Is Zarr the new COG? Cloud-Optimized GeoTIFF (COG) and Zarr have each earned their place in modern geospatial workflows. While often framed in opposition—raster vs. analysis, imagery vs. data cube—they are in fact deeply complementary. In this talk, we’ll unpack how they address similar challenges from different angles, and why they should be considered parts of a shared toolkit rather than competing paradigms. We’ll highlight where Zarr and COG overlap, where they differ, and how decisions around chunking, compression, tiling strategies, and metadata design affect both formats. We'll discuss implementation pitfalls, emerging best practices, and the still-unanswered questions that data producers and tool builders face. More than a comparison, this talk is a call to action: the community lacks clear guidance and consistent support for practitioners working to produce data in either format. We’ll highlight concrete gaps in the tooling landscape, share ideas from our own work on how to improve decision-making and best practices, and invite others to collaborate on building a healthier, more cooperative open geospatial data ecosystem. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/VFK79A/ WG403 Jarrett Keifer Julia Signell PUBLISH FGPL3P@@talks.osgeo.org -FGPL3P State of GeoServer en en 20251119T133000 20251119T135500 0.02500 State of GeoServer GeoServer powers a number of open source projects like GeoNode and geOrchestra and it is widely used throughout the world by organizations to manage, disseminate and analyze data at scale. This presentation provides an update on our community as well as reviews of the new and noteworthy features for the latest releases. In particular, we will showcase all the new features landed in the 2.27 and 2.28 series. Attend this talk for a cheerful update on what is happening with this popular OSGeo project, whether you are an expert user, a developer, or simply curious what GeoServer can do for you PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/FGPL3P/ WG403 Jody Garnett PUBLISH E8EHWE@@talks.osgeo.org -E8EHWE GIS as a Communication Tool: Using Re:Earth Visualizer in Citizen Workshops en en 20251119T140000 20251119T142500 0.02500 GIS as a Communication Tool: Using Re:Earth Visualizer in Citizen Workshops GIS has long been seen as a professional and complex technology — both technically and conceptually. While WebGIS has made it more accessible, it still remains challenging for non-experts, especially for citizens to quickly learn, participate, and create meaningful outputs within a short time. At the same time, local governments — one of the major GIS users — are increasingly looking for new ways to communicate and co-create with their citizens. From disaster prevention planning to urban redevelopment, there is a growing demand to make maps and data a medium for dialogue, not a technical barrier. In this talk, I will introduce several case studies demonstrating how our team leverages the Re:Earth Visualizer plugin ecosystem together with Re:Earth CMS to design lightweight and intuitive WebGIS applications for citizen participation workshops. These applications transform GIS into a tool for collaboration, storytelling, and civic engagement, helping governments and citizens engage in complex yet enjoyable forms of spatial communication. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/E8EHWE/ WG403 RED (XU CONG) Hinako Iseki PUBLISH KV7EHL@@talks.osgeo.org -KV7EHL Modular, Interoperable, Cross-Language Geospatial libraries with GeoArrow en en 20251119T143000 20251119T145500 0.02500 Modular, Interoperable, Cross-Language Geospatial libraries with GeoArrow [DuckDB](https://duckdb.org/), [GDAL](https://gdal.org/), libraries like [Lonboard](https://developmentseed.org/lonboard/latest/), and more can now efficiently share large vector data at low cost, thanks to [GeoArrow](https://geoarrow.org/), a binary representation for vector geometries that can be shared across libraries without any data copies. GeoArrow is a relatively low-level technology that tends to be unseen by end-users. This is great! Users just see performance improvements! For example, GDAL 3.6 [introduced support](https://gdal.org/en/stable/development/rfc/rfc86_column_oriented_api.html) for exposing the data read by OGR’s vector drivers as GeoArrow. This [dramatically improved performance](https://gdal.org/en/stable/development/rfc/rfc86_column_oriented_api.html#benchmarks): reading from FlatGeobuf or GeoPackage files to a GeoPandas `GeoDataFrame` improved by **20x**. But it can be useful to understand the factors behind what makes GeoArrow so performant. This talk will explain what GeoArrow is, how it differs from other new technologies like GeoParquet, and how to get the best performance when sharing data between these libraries with practical examples. It will also give a quick peek under the hood for how advanced users can create a cross-language library from C or Rust, but this talk will aim to be digestible for wide audiences; no deep technical prerequisites are expected. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/KV7EHL/ WG403 Kyle Barron PUBLISH ZQM9AP@@talks.osgeo.org -ZQM9AP Introducing OSM Clubs in Elementary Schools Building Young Mappers for a Better Future en en 20251119T153500 20251119T154000 0.00500 Introducing OSM Clubs in Elementary Schools Building Young Mappers for a Better Future Youth Mappers has successfully built a global network of university student chapters that contribute to mapping underserved communities through OpenStreetMap (OSM). While this movement has empowered youth in higher education to engage with geography, technology, and civic responsibility, there is a growing need to extend this opportunity to even younger students. This proposal advocates for the expansion of OSM based clubs into elementary schools in Addis Ababa, Ethiopia, to cultivate environmental awareness and digital mapping skills from an early age. Children are naturally curious and observant of their surroundings. Starting OSM clubs at the elementary level will allow students to explore their neighborhoods, learn basic mapping concepts, and develop a sense of ownership and care for their environment. Through hands-on activities such as sketch mapping, local data collection, and digital storytelling, young students can begin to understand their communities in new ways. This not only enhances geographic literacy but also encourages early digital learning, problem-solving, and teamwork. The proposed pilot program, called “Young Mappers Club,” will be launched in selected primary schools in Addis Ababa. The club will follow a simplified and age-appropriate curriculum that introduces key concepts of OpenStreetMap, environmental awareness, and community mapping. Activities will be designed to be fun, interactive, and aligned with existing educational goals. With support from local Youth Mappers university chapters, schoolteachers, and parents, the club will serve as a bridge between university-led initiatives and community-based learning. We believe that empowering students at a young age helps develop a lifelong interest in their environment, technology, and civic responsibility. This initiative will create a pipeline of future Youth Mappers who are already familiar with OSM tools and values before they reach university level. It also contributes to a culture of participation and awareness within families and local communities. To bring this vision to life, we are seeking support from the Youth Mappers and OSM community in the form of training materials, mentorship, and technical guidance. With collaboration and shared resources, Addis Ababa can become a model city for integrating OSM education into primary school systems. Let’s inspire the next generation of mappers, starting from the classroom. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/ZQM9AP/ WG403 Aderaw Tsegaye Aniteneh PUBLISH XGLXVF@@talks.osgeo.org -XGLXVF Tracking Trash: Mapping Marine Debris Using Earth and Ocean Observations en en 20251119T154000 20251119T154500 0.00500 Tracking Trash: Mapping Marine Debris Using Earth and Ocean Observations Plastic pollution and marine debris threaten marine ecosystems, fisheries, and livelihoods across the Pacific. But how do we track pollution pathways when they cross rivers, land, and sea? Enter the CleanSeas project; a collaborative effort leveraging Earth and ocean observation data to identify, track, and predict the movement of marine debris. This presentation dives into the methodology behind CleanSeas, including using SVM, Random Forest machine learning, the floating debris index and on-the-ground validation. We’ll demonstrate how open data and tools from Digital Earth Pacific are being applied to identify pollution hotspots and inform policy responses. The session emphasizes the power of EO in driving tangible outcomes in marine pollution mitigation and the growing importance of cross-domain integration in environmental monitoring. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/XGLXVF/ WG403 Kamsin Raju PUBLISH TSVGYJ@@talks.osgeo.org -TSVGYJ GPU-native Zarr: Optimizing data throughput for large-scale geospatial machine learning workflows en en 20251119T154500 20251119T155000 0.00500 GPU-native Zarr: Optimizing data throughput for large-scale geospatial machine learning workflows The Zarr format is used in the geosciences for storing time-series and multi-variate data, and was designed with parallel access in mind. Previously though, there was a bottleneck where data from storage disk had to go through an intermediate step of being loaded into CPU memory, before it is then copied to Graphical Processing Unit (GPU) memory. Now, with the proper [GPUDirect Storage (GDS)](https://docs.nvidia.com/gpudirect-storage/overview-guide/index.html) drivers configured, one can read and decode uncompressed data from storage into CUDA GPU device memory directly via [`kvikIO`](https://docs.rapids.ai/api/kvikio/stable) for lower latency. To get even more throughput, compressed data in Zarr can be sent directly to GPU memory, and decompressed in parallel using [`nvCOMP`](https://developer.nvidia.com/nvcomp) that works faster than CPU-based decompression algorithms. The result is a fully GPU-native pipeline where I/O latency is minimized by sending compressed data, and GPU utilization is maximized by having it do most of the processing. We'll show some benchmarks comparing the speedups from a CPU baseline to a fully GPU-native workflow, and show some extra tips and tricks on how to use these technologies for your next geospatial machine learning project! PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/TSVGYJ/ WG403 Wei Ji Leong PUBLISH RYDKNA@@talks.osgeo.org -RYDKNA State of the eoAPI en en 20251119T155000 20251119T155500 0.00500 State of the eoAPI In 2023, Development Seed launched the eoAPI project - a growing collection of open-source tools and infrastructure aimed at making it easier to build, deploy, and scale modern Earth Observation (EO) applications. In this talk, we’ll explore the current state of the ecosystem, highlight new developments, and share what’s coming next. We’ll visit some of the core building blocks of eoAPI: - **[pgSTAC](https://github.com/stac-utils/pgstac)** - A performant, normalized STAC catalog backed by PostgreSQL - **[TiTiler](https://github.com/developmentseed/titiler)** - A dynamic tile server for Cloud Optimized GeoTIFFs and STAC Items - **[TiPg](https://github.com/developmentseed/tipg)** - A lightweight OGC API - Features implementation built on top of pgSTAC - **[STAC-FastAPI](https://github.com/stac-utils/stac-fastapi)** - A high-performance, pluggable STAC API built with FastAPI We’ll showcase some exciting new additions: - **[stac-auth-proxy](https://github.com/developmentseed/stac-auth-proxy)** - A flexible FastAPI-based proxy for adding authentication and authorization to any STAC API - **[stac-manager](https://github.com/developmentseed/stac-manager)** - A tool for orchestrating STAC metadata ingestion, validation, and management across pipelines Finally, we’ll explore recent infrastructure efforts that support deploying and scaling these services: - **[eoAPI-CDK](https://github.com/developmentseed/eoapi-cdk)** - AWS CDK constructs for cloud-native eoAPI deployments - **[eoAPI-K8s](https://github.com/developmentseed/eoapi-k8s)** - Kubernetes Helm charts for containerized, production-grade deployments Together, these tools form a modular, interoperable foundation for building next-generation EO platforms. Whether you’re running a small data portal or a high-scale STAC service, eoAPI provides the pieces to get you up and running securely and efficiently. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/RYDKNA/ WG403 Anthony Lukach PUBLISH QJBWMT@@talks.osgeo.org -QJBWMT Building a Business with Open Content and Open Source Software en en 20251119T155500 20251119T160000 0.00500 Building a Business with Open Content and Open Source Software This talk will cover my 5-year journey into building [Spatial Thoughts](https://spatialthoughts.com/) - a learning platform for modern geospatial technologies. Based on the idea that learning should be accessible to all, I made a conscious choice to make all of our learning content open-source and available under a very liberal license. Not monetizing the content turned out to be an important decision, and while it may seem counterintuitive, it was key in powering the growth of the company. Hope this talk will inspire you to consider alternative business models and adopt the ethos of open-source software in your work. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/QJBWMT/ WG403 Ujaval Gandhi PUBLISH PQJVKK@@talks.osgeo.org -PQJVKK IGEO7 and DGGRID - Like H3, but an Equal-Area Hexagonal DGGS for Fairer Global Analysis en en 20251119T160000 20251119T160500 0.00500 IGEO7 and DGGRID - Like H3, but an Equal-Area Hexagonal DGGS for Fairer Global Analysis Uber's H3 has revolutionized spatial indexing, but its cells aren't equal-area, skewing global analyses and visualizations. What if you could have H3's elegant hierarchical indexing and true equal-area cells for statistically sound results? IGEO7 is a pure aperture 7 hexagonal DGGS with a hierarchical indexing system named Z7. It is implemented in the long-standing open-source DGGRID software and has a handy Python wrapper, dggrid4py. - https://dggrid.readthedocs.io/ - https://github.com/sahrk/DGGRID - https://dggrid4py.readthedocs.io/ - https://github.com/allixender/dggrid4py In this talk, I'll shortly introduce IGEO7's capabilities and show how to use it with DGGRID and dggrid4py. Come see how you no longer have to choose between handy hexagon indexing and statistically sound analysis with open-source world. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/PQJVKK/ WG403 Alexander Kmoch PUBLISH MM3UUH@@talks.osgeo.org -MM3UUH Portable CQL2: A Rust Core for Queries Everywhere en en 20251119T160500 20251119T161000 0.00500 Portable CQL2: A Rust Core for Queries Everywhere [CQL2](https://www.ogc.org/standards/cql2/) is a powerful and flexible query language designed by the OGC to support advanced filtering and subsetting of geospatial data, particularly for features and records. It enables expression of complex spatial, temporal, and logical conditions in both text and JSON forms. [**cql2-rs**](https://developmentseed.org/cql2-rs/) is a Rust library built to work with CQL2 expressions. It provides tools to validate expressions, convert between text and JSON formats, combine multiple expressions, simplify logical trees, and evaluate expressions against JSON input. It is intended as a lightweight and reusable core for working with CQL2 in any setting. Because it’s written in Rust, `cql2-rs` can be exposed across many platforms and runtimes. Along with publishing a Rust crate for usage within the Rust ecosystem, we've published a [Python module](http://developmentseed.org/cql2-rs/latest/python/) via PyO3, a [command-line interface](https://developmentseed.org/cql2-rs/latest/cli/), and a [browser-based playground](https://developmentseed.org/cql2-rs/latest/playground/) using WebAssembly; all built from the same codebase. This talk will highlight both the functionality of the library and the philosophy behind it: building small, focused Rust utilities that embrace composability and portability. Whether you're scripting, building APIs, or creating interactive tools, `cql2-rs` demonstrates how a single Rust core can power diverse workflows and tools. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/MM3UUH/ WG403 Anthony Lukach PUBLISH LRSB8Z@@talks.osgeo.org -LRSB8Z Measuring Soil Moisture With GPS Multipath and Open Source Software? en en 20251119T161000 20251119T161500 0.00500 Measuring Soil Moisture With GPS Multipath and Open Source Software? Can multipath be useful? An emerging measurement technique known as "GNSS Interferometric Reflectometry" turns this common source of positioning error into an environmental sensor. See the gnssrefl package by Dr Kristine Larson: https://github.com/kristinemlarson/gnssrefl PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/LRSB8Z/ WG403 George Townsend PUBLISH KXXMGV@@talks.osgeo.org -KXXMGV The Pacific Geospatial Women Network en en 20251119T161500 20251119T162000 0.00500 The Pacific Geospatial Women Network The Pacific Geospatial Women Network (PGWN) is newly endorsed under the Oceans Management and Literacy Programme at the Pacific Community (SPC). This network reports to the Pacific Geospatial and Surveying Council (PGSC) and was established to promote women capacity in the field of Geospatial Science and Earth Observation (EO) in the Pacific. With the overarching goal of raising awareness, celebrating achievements, and most importantly, creating a support group for women in this field. In 2024, the Pacific Geospatial Women Network (PGWN) successfully piloted its initiative with two local women’s groups in Fiji: the Gusunituba Women’s Group of Votua Village in Ba Province and the Daku Women’s Group in Daku Village, Tailevu Province. Both groups are actively engaged in mangrove rehabilitation and agricultural initiatives, supported under the Jobs for Nature funding program administered by Fiji’s Ministry of Economy. This pilot phase aimed to integrate geospatial capacity building into existing community-led environmental efforts. This work recognises that women and marginalised groups often face systemic barriers in accessing technology, technical training, and decision-making spaces. The initiative helps bridge the digital divide and promotes inclusive, equitable participation in climate resilience efforts. by intentionally engaging women at different levels — including those from rural areas and with diverse abilities. Geospatial tools have become vital in addressing sustainable development challenges across the Pacific, and through its regional mandate, PGWN is working to ensure that women are not left behind in this digital and data-driven age. This includes access to hands-on learning, digital literacy, and the practical use of Earth Observation (EO) and mapping tools for decision-making, community planning, and environmental resilience. By equipping local women groups with these skills, PGWN is contributing to inclusive development efforts, ensuring that women are active participants in shaping their future and that of their communities. To support these roles, PGWN is planning a tailored training and awareness programme for Kiribati Women in Mapping (KWIM) that promotes the integration of traditional knowledge with geospatial technology. This will not only empower local women but also support national and regional resilience efforts through inclusive data practices and adaptive community-led planning. At the heart of this initiative lies the application of Geospatial Science and Earth Observation (EO). These tools allow for the collection, analysis, and visualization of spatial data, which is essential for mapping natural resources, land-use planning, and resource management. While the science is already well established in global contexts, the application in rural and local communities of the Pacific is still evolving. Geospatial technologies are being explored for community-led decision-making, particularly in the management of natural resources, waste disposal, and sustainable development. These tools enable local women to gather critical environmental data, empowering them to take an active role in their community's planning and development. While these technologies are powerful, their full potential is still being explored in Pacific contexts, especially for rural women who often have limited access to technical resources. PGWN aims to bridge this gap by providing capacity-building sessions and awareness campaigns to introduce geospatial tools to these communities. The PGWN envisions further collaboration with universities, non-profits, and regional organizations to give additional opportunities to young women entering the geospatial field. One key initiative is creating internship opportunities for female graduates in geospatial science, offering them practical experience and industry exposure. By acting as a catalyst for women’s success in geospatial science, PGWN aims to inspire more women to take leadership roles within STEM fields across the Pacific. Through capacity-building efforts, mentorship, and collaborative projects, the network continues to grow, paving the way for a more inclusive and sustainable future for women in the Pacific PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/KXXMGV/ WG403 Jacqueline Singh PUBLISH NDGHHA@@talks.osgeo.org -NDGHHA Predicting Greenhouse Damage from Heavy Snowfall in South Korea Using FOSS4G en en 20251119T162000 20251119T162500 0.00500 Predicting Greenhouse Damage from Heavy Snowfall in South Korea Using FOSS4G . PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/NDGHHA/ WG403 Soonyeon Kim PUBLISH G9WDHR@@talks.osgeo.org -G9WDHR Re:Earth Building the Open Geospatial Data Platform for Everyone en en 20251119T163000 20251119T165500 0.02500 Re:Earth Building the Open Geospatial Data Platform for Everyone Open data is growing, yet still fragmented, inconsistent, and often locked behind proprietary systems. In this talk, we introduce Re:Earth—an open-source WebGIS platform designed to become the open data platform. We explore the challenges facing today’s geospatial ecosystem, why open source and open data principles must guide the next generation of digital infrastructure, and how Re:Earth enables anyone to host, share, and use spatial data freely. Join us as we discuss how open, sustainable, community-driven platforms can become true digital public goods. PUBLIC CONFIRMED Keynote https://talks.osgeo.org/foss4g-2025/talk/G9WDHR/ WG403 Hidemichi Baba Hinako Iseki PUBLISH A9JR8Y@@talks.osgeo.org -A9JR8Y MapLibre - from data to tile rendering, in one status update en en 20251119T110000 20251119T112500 0.02500 MapLibre - from data to tile rendering, in one status update This talk will cover all aspects of MapLibre efforts - the open source non-profit delivering the ubiquitous map rendering engine plus all tooling to convert data into interactive maps. The engine is used by organizations of every size, from tiny one person sites to Meta, AWS, and Microsoft as their primary map rendering engine. Come learn of the products we are developing, the new features we are excited about, the challenges and success, and the collaboration with the FOSS community and companies of all sizes. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/A9JR8Y/ WG308 TE IRINGA Yuri Astrakhan PUBLISH 7BVH97@@talks.osgeo.org -7BVH97 AI-Powered Wildfire Spread Prediction System Using Open Source Geospatial Technologies en en 20251119T113000 20251119T115500 0.02500 AI-Powered Wildfire Spread Prediction System Using Open Source Geospatial Technologies The frequency and intensity of wildfires are increasing globally due to climate change impacts, and the Republic of Korea is no exception. Particularly during spring seasons, the combination of dry weather and strong winds leads to large-scale wildfires, resulting in national disasters. In March 2025, simultaneous large-scale wildfires that broke out across South Korea were recorded as one of the worst in the nation’s history, burning approximately 100,000 hectares of forest an area 1.7 times the size of Seoul, the capital city. It lasted nine days before it was fully extinguished due to strong winds with maximum instantaneous wind speeds of over 25 m/s, making it extremely difficult to extinguish. To effectively respond to these large-scale wildfires, which are difficult to predict and cause immense damage, it is essential to have technology that can analyze and predict their paths by receiving real-time data on the three key elements of wildfire spread: topography, fuel, and weather. Accordingly, we have developed a system that improves upon existing services based on empirical algorithms, precisely predicting wildfire spread using AI deep learning technology. The system processes and analyzes real-time data on the three elements of wildfire spread and utilizes open-source technology to predict, analyze, and visualize the path and speed of the fire. We have actively utilized a proven open-source tech stack—including QGIS, PostGIS, GeoServer, OpenLayers, and CesiumJS—to implement an integrated development environment that covers everything from the analysis of prediction data to its 2D and 3D visualization. In this presentation, we aim to introduce a practical case study where this technological foundation was used to effectively support on-site decision-making during wildfire events. --- This study was carried out with the support of 'R&D Program for Forest Science Technology '(Project No. "RS-2024-00402509")' provided by Korea Forest Service(Korea Forestry Promotion Institute). PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/7BVH97/ WG308 TE IRINGA Hanjin Lee Hyeeun Ahn Sungeun Cha Hyun-Woo Jo PUBLISH XTRPMG@@talks.osgeo.org -XTRPMG Smart vineyards with QGIS & QField en en 20251119T120000 20251119T122500 0.02500 Smart vineyards with QGIS & QField In this presentation, you will be hearing the storry of OPENGIS.ch and Ian Beecher Jones – a passionate British winemaker - who co-developed an open-source workflow that will allow wineries to fully manage, document, plan, and monitor their operations using QGIS. QField is the central tool for data collection in the vineyard – from mapping stakes to using modern map themes. This makes efficient vineyard management and "smart farming" incredibly easy. You'll see how assigning attributes, defining zones, and integrating spatial data optimize planning and harvesting – even before the first grapes are picked. Curious about how precise location data can transform vineyard management? Discover how QGIS and QField can be used simply and effectively in viticulture – and what that can mean for vineyards in Aotearoa, too. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/XTRPMG/ WG308 TE IRINGA Marco Bernasocchi Berit Mohr PUBLISH XQUEXG@@talks.osgeo.org -XQUEXG Creating 3D Printed Landscape Models with FOSS en en 20251119T133000 20251119T135500 0.02500 Creating 3D Printed Landscape Models with FOSS This talk explores the workflow behind crafting detailed 3D printed landscape models using free and open-source software. From raw elevation data to tangible terrain, attendees will get an inside look at how QGIS is used to scope and prepare elevation datasets, how geometry nodes and modifiers are used to dynamically create 3D geometry in Blender, and how Cura Slicer is used to prepare the digital 3D model for printing. This presentation draws from real-world projects by a New Zealand based cartographer working at the intersection of geospatial data and physical modelling. It will discuss challenges like terrain generalization, tile-based printing, and design for both aesthetic and functional outcomes. Whether you're into 3D printing, terrain visualisation, or just love seeing geospatial data come to life, this session offers practical insight and inspiration for mappers and makers alike. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/XQUEXG/ WG308 TE IRINGA James Ford PUBLISH JHA9NZ@@talks.osgeo.org -JHA9NZ Workflow Automation with QGIS: Case Studies and Tips en en 20251119T140000 20251119T142500 0.02500 Workflow Automation with QGIS: Case Studies and Tips The talk will feature real-world case studies showcasing how the QGIS Model Designer was used to automate complex workflows for a variety of use cases. QGIS processing framework offers Model Designer as the no-code solution for analysts looking to automate their work. These tools allow you to take hundreds of native and 3rd party processing algorithms and build workflows for spatial analysis and map publishing. The talk will showcase lessons learnt from real-world projects and how you can apply those to your own work. Join to discover how you can leverage QGIS to automate your spatial workflows. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/JHA9NZ/ WG308 TE IRINGA Ujaval Gandhi PUBLISH U8AGYU@@talks.osgeo.org -U8AGYU A Digital Twin of 80km of streetscapes, with FOSS4G? Sure! en en 20251119T143000 20251119T145500 0.02500 A Digital Twin of 80km of streetscapes, with FOSS4G? Sure! A hero story, featuring, COTS sensors, ROS, COPC, machine learning and friends. It can be done! PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/U8AGYU/ WG308 TE IRINGA Martin Tomko PUBLISH NCTETY@@talks.osgeo.org -NCTETY Open Source Solution for Topographic Data Production en en 20251119T153000 20251119T155500 0.02500 Open Source Solution for Topographic Data Production This talk will discuss - the main features of the new solution, especially data management, feature editing and quality tools - experiences during the development - experiences in the production PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/NCTETY/ WG308 TE IRINGA Risto Ilves PUBLISH F8BZXS@@talks.osgeo.org -F8BZXS AI Coding and the Future of Open-Source Geospatial Software en en 20251119T160000 20251119T162500 0.02500 AI Coding and the Future of Open-Source Geospatial Software AI-assisted programming is quickly changing how developers write, test, and maintain code. This talk looks at how AI coding tools could push the open-source ecosystem in two directions at once: making it easier than ever to generate new tools or forks, risking fragmentation and short-lived projects, while also creating strong incentives to build core software with higher quality and thorough documentation that AI agents can build on. We’ll look at how AI programming is being used, what developers and maintainers think today, and why deep subject matter expertise — especially in the geospatial domain — is likely to stay vital even as routine coding work becomes easier to automate. How can the FOSS4G community adapt to keep open-source geospatial software resilient and trusted in an AI-driven era? PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/F8BZXS/ WG308 TE IRINGA Matthew Hanson PUBLISH QX3ARG@@talks.osgeo.org -QX3ARG Developing a ‘live’ map of spatial access to health services in Aotearoa New Zealand en en 20251119T110000 20251119T112500 0.02500 Developing a ‘live’ map of spatial access to health services in Aotearoa New Zealand Title: Developing a ‘live’ map of spatial access to health services in Aotearoa New Zealand Mitchell Pincham1 , Marcus Blake2, Sam Quinsey2 & Jesse Whitehead*1 1 Te Ngira: Institute for Population Research, University of Waikato, New Zealand 2 Centre of Australian Research into Accessibility, Deakin Rural Health, Deakin University, Australia Presenting author *Corresponding author: jesse.whitehead@waikato.ac.nz Aims: To develop a real-time model that incorporates current road conditions to estimate spatial access to health services daily, at the address level. Methods: National Highway road-closure data was collected from the New Zealand Transport Agency Application Programming Interface. Data about local road closures was scraped from local council websites. A road network from Open Street Maps was modified by removing any closed highways or local roads. The distance from each address in the Manawataki Health Region, through this new road network, to the nearest Hospital was calculated. The program was automated to run each day in January, using current road conditions for that day to estimate hospital accessibility. Results: Daily estimates of hospital accessibility were successfully automated, with variations in spatial accessibility over time noted. However, the importance of data quality for the accuracy of this model is paramount. Reporting structures and formats meant that data obtained from some local councils was found to be imprecise or unreliable at times. Conclusions: This approach shows potential for quickly estimating access to health services under changing road conditions, such as during and after extreme weather events. NZTA and local councils should be encouraged to work together to improve the reporting of road closures. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/QX3ARG/ WG126 Jesse Whitehead Mitchell Pincham PUBLISH VUAJZK@@talks.osgeo.org -VUAJZK State of mago3DTiler & mago3DTerrainer: Open-Source Tools for Standards-Based Digital Twins! en en 20251119T113000 20251119T115500 0.02500 State of mago3DTiler & mago3DTerrainer: Open-Source Tools for Standards-Based Digital Twins! mago3DTiler (https://github.com/Gaia3D/mago-3d-tiler) is a Java-based OGC 3D Tiles creator that has gained wide adoption thanks to its flexibility, high performance, and extensive format support. It handles over ten 3D formats (CityGML, 3DS, OBJ, FBX, glTF, Collada DAE, IFC/BIM, LAS/LAZ, SHP) and features on-the-fly CRS conversion for seamless integration. It can convert 2D data into extruded 3D Tiles. Additionally mago3DTiler optimizes large point clouds and reality mesh data for smooth web visualization. mago3DTerrainer (https://github.com/Gaia3D/mago-3d-terrainer) is a Java-based quantized-mesh terrain generator designed specifically for Cesium Terrain Tiles. It efficiently converts GeoTIFFs into high-precision quantized-mesh data with customizable settings (depth range, tile size, interpolation method) and supports batch processing . 1. mago3DTiler Key Features - On-the-fly CRS (Coordinate Reference System) transformation during tile generation - Extrusion of 2D features into 3D using height attributes - Support for massive point clouds (e.g., full-scale city level datasets) - Attribute handling for intensity and classification in point clouds - Photogrammetry (Reality Model) data conversion - Reality mesh data optimization with new triangle reduction and geometry simplification algorithms - Mesh quantization for improved rendering performance - Instance-based LOD implementation (e.g., trees and forest data rendering with scalable detail) - 3D Tiles 1.1 compatibility: unified GLB support across .b3dm, .i3dm, and .pnts - Tileset merging: generate parent tilesets from multiple input tilesets 2. mago3DTerrainer Key Features - Easy and flexible conversion of GeoTIFFs into quantized mesh - High accuracy mesh generation - Batch conversion of multiple GeoTIFFs - Detailed customization options (e.g., tile depth, size, interpolation) - Priority handling for overlapping GeoTIFFs based on resolution - Enhanced support for large-scale GeoTIFFs and diverse CRS transformations - Large area conversion (e.g., verified conversion of nationwide DEM datasets) These tools enable seamless integration of various spatial datasets into digital twin platforms based on OGC 3D Tiles and Quantized Mesh standards. Through continuous improvements and real-world applications, such as national digital twin projects and high-resolution environmental simulations, mago3DTiler and mago3DTerrainer are helping advance the open geospatial ecosystem. Join this session to explore how open-source tools can simplify complex geospatial workflows and empower developers and users to build smarter, scalable 3D digital twins. <Acknowledgments> This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2025-02317649, NTIS Grant: 2610000447) PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/VUAJZK/ WG126 Sanghee Shin PUBLISH LNJDKL@@talks.osgeo.org -LNJDKL Earthquakes to Everyday: How an Open Geospatial Ecosystem Supports New Zealand’s Lifeline Infrastructure and National Resilience en en 20251119T120000 20251119T122500 0.02500 Earthquakes to Everyday: How an Open Geospatial Ecosystem Supports New Zealand’s Lifeline Infrastructure and National Resilience In New Zealand, resilience isn’t optional. With earthquakes, landslides, and storms a part of life here in New Zealand, building and maintaining resilient infrastructure is as much about foresight as it is about recovery. What if the tools we need in emergencies were already in everyday use? What if we didn’t have to build new systems during a crisis because they were already in place? That’s the idea behind a growing set of open, federated geospatial tools that are transforming how infrastructure is planned, built, and maintained across Aotearoa. The 2011 Christchurch earthquakes fractured the city – literally and organisationally. As agencies mobilised to rebuild, coordination quickly became a critical issue. Who was working where? Which projects were clashing? How could a city be rebuilt efficiently when no one had the full picture? To answer these questions, the National Forward Works Viewer (NFWV) was born – a shared map of all planned civil works. It helped teams from multiple organisations to coordinate timeframes, reduce clashes, and avoid rework. It was built fast, out of necessity – but it worked, and saved tens of millions in avoidable costs. From that crucible, the idea emerged that this shouldn’t be a one-time fix. It should be the new normal. Today, the NFWV is no longer a recovery tool. It’s used by over 500 organisations including councils, utility providers, and transport agencies – as part of their daily operations. Whether it's resurfacing roads, replacing pipes, laying fibre, or planning major events like marathons and parades, the Forward Works Viewer helps agencies see each other’s plans and work together. But the NFWV is just the beginning. It’s part of a growing ecosystem of open, geospatial tools. Alongside the NFWV are two other national tools, which have been built not to hoard data – but to connect it. One of these tools is the New Zealand Underground Asset Register (NZUAR), which brings visibility to what lies beneath our cities. Piloted in Wellington NZ, it federates subsurface utility data into a common schema giving planners and contractors visibility of underground pipes and cables. It helps prevent asset strikes, protects lives, and improves the information that asset owners receive from the field. Just like the NFWV, it operates on the principle that better decisions come from shared, trusted information – especially when it’s about what can’t be seen. The third tool is the upcoming National Geospatial Catalogue, which federates critical contextual data – like ground conditions, contamination risks, heritage overlays, and more – into a searchable map layer available alongside the NFWV and NZUAR. Instead of scattered datasets across siloed agencies, the catalogue will create a nationwide view of risks and constraints that affect infrastructure delivery and resilience planning. Together, these three tools – NFWV, NZUAR, and the Geospatial Catalogue – form a shared picture of digital infrastructure for Aotearoa. All are built on open-source geospatial software, chosen for its interoperability, transparency, and freedom from licensing barriers. But this openness isn’t just a technical choice – it’s a civic principle. public data, public tools, and public infrastructure should be available for public good. That’s why these platforms are governed by the Digital Built Aotearoa Foundation (DBAF) – a charitable trust formed in 2023 to steward this ecosystem of open data. DBAF operates on a cost-recovery model, reinvesting any surplus into improving the tools, building new features, and keeping subscription costs low. Its independence means it can act as a neutral, non-commercial custodian of data shared between public and private actors – fostering collaboration, not competition. The Foundation’s work has been recognised by the NZ Infrastructure Commission as addressing a problem of national importance - and now included on their Infrastructure Priorities Programme for further investigation. The digital ecosystem is also supporting the National Emergency Management Agency (NEMA) in the development of a pilot national electricity outage map to improve emergency readiness and response. These are not side projects – they are signs that this ecosystem is becoming core national infrastructure. And crucially, the model is scalable. None of the tools require centralised databases. Instead, they use data federation and schema alignment to map disparate datasets into a unified view. Organisations retain their own data and control, while contributing to a greater whole. This is what makes the system work – technically, politically, and culturally. The map-based user-friendly design makes them accessible to all users – allowing organisations to spatialise their data, validate inputs, and generate insights without needing complex software or deep expertise. It brings the power of spatial analysis to non-spatial users, which in turn improves data quality and engagement across the board. New Zealand’s story is a global one in disguise. Every country faces infrastructure challenges. Every community faces disasters, whether acute or slow-moving. But the lesson from Aotearoa is this: if we build the right tools for emergency response, and we embed them into everyday workflows, then we build resilience every day – even when the ground isn’t shaking. For the FOSS4G community, this is a story of what’s possible when open-source meets public need. It’s about designing systems that are federated, open, and governed in the public interest. And it’s about recognising that disaster resilience is not a feature – it’s an outcome of everything we do. This presentation will take the audience on the journey of how Digital Built Aotearoa’s open geospatial ecosystem evolved from emergency response into essential national infrastructure. It will explore why open principles, federated data, and public-good governance were critical design choices, and how these tools enable coordination across hundreds of organisations without centralising control. The presentation will also discuss the real-world technical and organisational challenges of building federated systems at national scale, including schema design, data standardisation, and stakeholder trust. Finally, it will reflect on New Zealand’s unique context of frequent natural disasters and how this shaped the foundational thesis: systems built for disaster recovery must be pre-embedded in everyday infrastructure planning to deliver true resilience. The underlying principles of openness, federation, and public stewardship, which are widely transferable, offer a model for other jurisdictions looking to improve infrastructure delivery, emergency readiness, and cross-sector collaboration. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/LNJDKL/ WG126 Alistair McIntyre Angus Bargh PUBLISH VKQQSY@@talks.osgeo.org -VKQQSY State of TerriaJS en en 20251119T133000 20251119T135500 0.02500 State of TerriaJS TerriaJS is an open-source framework for web-based geospatial catalogue explorers. It uses Cesium and Leaflet to visualise 2D and 3D geospatial data, and it supports over 50 different Web APIs, file formats and open data portals. It is almost entirely JavaScript in the browser, meaning it can even be deployed as a static website, making it simple and cheap to host. TerriaJS is used across the globe to create next-generation Digital Twin Platforms for open geospatial data discovery, visualisation and sharing - it is used to drive - [Digital Earth Australia Map](https://maps.dea.ga.gov.au/) - [Digital Earth Africa Map](https://maps.digitalearth.africa/) - [Pacific Map (Digital Earth Pacific)](https://map.pacificdata.org/) - [VIC Spatial Digital Twin](https://vic.digitaltwin.terria.io/) (Australian State Gov) - [Tokyo Digital Twin](https://info.tokyo-digitaltwin.metro.tokyo.lg.jp/) - and many others In this talk, I will give: - Background information about TerriaJS and how it is used by the community - Current state of the project for users, developers and wider community - New features - Future plans! For more information about Terria: - https://terria.io/ - https://github.com/TerriaJS/terriajs PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/VKQQSY/ WG126 Nick Forbes-Smith Michael Holmes PUBLISH HUAWWF@@talks.osgeo.org -HUAWWF State of UN Smart Maps Group en en 20251119T140000 20251119T142500 0.02500 State of UN Smart Maps Group The UN Smart Maps Group advances geospatial innovation within the UN Open GIS Initiative as Domain Working Group 7. The group tests emerging technologies including Generative AI, Distributed Web, and IoT devices for future geospatial operations. This presentation showcases recent developments in UNVT POD and FOIL4G projects, demonstrating how FOSS4G tools integrate with cutting-edge technologies to democratize geospatial information access. The group operates on three fundamental pillars: Openness, Collaboration, and Innovation. The organization embraces a philosophy of open by default, leveraging open-source technologies, open practices, and open communities to democratize access to geospatial information. Central to this approach is the recognition that AI has become an integral collaborator in the work, with the understanding that we must be prepared to welcome sufficiently advanced AI as collaborators in the near future. The UN Vector Tile Toolkit Portable on Demand (UNVT POD) represents a paradigm shift in geospatial infrastructure deployment. Built on Raspberry Pi technology and utilizing FOSS4G tools including Tippecanoe for vector tile generation, MapLibre GL JS for visualization, and Martin for tile serving, UNVT POD creates map servers that operate independently of traditional internet infrastructure. Key capabilities include edge computing that enables high-speed response in remote areas, offline-first design that maintains functionality without network connectivity, solar power compatibility that supports sustainable operation in energy-limited environments, and community management systems that can be operated and maintained by local personnel without specialized technical knowledge. The Free and Open Information Library for Geospatial (FOIL4G) integrates Generative AI capabilities with traditional UNIX tools and FOSS4G technologies to create an intelligent geospatial information processing system. The group's exploration of Distributed Web (DWeb) technologies, particularly IPFS (InterPlanetary File System), addresses critical challenges in geospatial data sharing and preservation. This approach enables decentralized data storage and distribution, enhanced resilience against network disruptions, reduced bandwidth requirements for large geospatial datasets, and improved data sovereignty for local communities. The technical solutions are built on cloud-native principles while maintaining the ability to operate in completely disconnected environments. The architecture emphasizes microservices design with modular components that can be deployed independently, API-first approach ensuring interoperability with existing systems, and GitOps methodology for transparent and collaborative development processes. This presentation will demonstrate live deployments of technologies and invite the FOSS4G community to engage with initiatives. The combination of proven open-source tools with emerging technologies like AI and DWeb creates unprecedented opportunities for democratizing geospatial information access and empowering communities worldwide. The UN Smart Maps Group's work exemplifies how international organizations can leverage community-driven innovation to address global challenges while maintaining the open principles that make geospatial technology accessible to all. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/HUAWWF/ WG126 Hidenori Fujimura PUBLISH AFDPVX@@talks.osgeo.org -AFDPVX State of JICA Quick Mapping Project (QMP) en en 20251119T143000 20251119T145500 0.02500 State of JICA Quick Mapping Project (QMP) The Japan International Cooperation Agency (JICA) Quick Mapping Project (QMP) represents a initiative that transforms development cooperation through OpenStreetMap utilization. JICA is developing a new map procurement service model that delivers essential mapping solutions within 2 weeks to 2 months. This co-creation, innovation and circulation initiative renews how development agencies access geospatial information for urgent project needs. QMP's new approach treats mapping as a service rather than a lengthy project. This paradigm shift enables JICA to rapidly respond to urgent development needs by leveraging OpenStreetMap's collaborative platform and open-source tools specifically developed for OpenStreetMap data handling. The 2-week to 2-month delivery timeframe makes geospatial information accessible for time-sensitive development interventions, disaster response, and infrastructure planning. Central to QMP's mission is the systematic enhancement of OpenStreetMap with development-relevant geospatial data. This approach involves strategic data addition through identifying gaps in OpenStreetMap and systematically filling them with development-focused information. Community participation ensures that all contributions work within OpenStreetMap's collaborative framework and align with community standards. Sustainability efforts focus on building local capacity to maintain and update contributed data over time. QMP utilizes a comprehensive suite of open-source tools specifically designed for OpenStreetMap data manipulation and visualization. Visualization and distribution utilize Tippecanoe for generating vector tiles from OpenStreetMap data, PMTiles for efficient serverless map tile distribution, MapLibre GL JS for creating interactive web maps from OSM data, Martin for serving vector tiles and providing robust map APIs, and MapLibre GL JS for lightweight map interfaces. This presentation will demonstrate live workflows using the complete open-source toolchain, showcase real-world mapping outputs, and invite community collaboration on tool development and methodology refinement. QMP represents a model for leveraging OpenStreetMap in development cooperation, and the presentation seeks community input on technical innovations and partnership opportunities. QMP demonstrates that thoughtful integration of OpenStreetMap with development practice can create transformative outcomes for both the global mapping community and development effectiveness, proving that open data and open tools can drive meaningful social impact at scale. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/AFDPVX/ WG126 Hidenori Fujimura PUBLISH 3ZXDGP@@talks.osgeo.org -3ZXDGP Cloud-native spatial data ecosystem in the rise of the National Geospatial Data Center of Timor-Leste en en 20251119T153000 20251119T155500 0.02500 Cloud-native spatial data ecosystem in the rise of the National Geospatial Data Center of Timor-Leste The National Center for Geospatial Data (CNDG) of Timor-Leste is implementing a spatial data ecosystem using cloud-native technologies in an experimental basis. This project aims to enhance the accessibility and usability of geospatial data in Timor-Leste. The application of Free and Open Source Software for Geospatial (FOSS4G), such as go-pmtiles, Tippecanoe, MapLibre GL JS, and Martin, and developer platforms like GitHub will facilitate efficient geospatial data management and visualization, with support from the geospatial unit of Japan International Cooperation Agency (JICA). The project is designed to address the current challenges faced by Timor-Leste in managing and utilizing geospatial data. By leveraging cloud-native technologies, CNDG aims to create a scalable and flexible ecosystem that can handle large volumes of data and provide real-time access to geospatial information by everyone, especially by the partner organizations in the Government of Timor-Leste, among other development partners for the country. The use of go-pmtiles and Tippecanoe will enable efficient data processing and storage, while MapLibre GL JS and Martin will provide powerful tools for data visualization and analysis. GitHub Pages will serve as a platform for sharing and collaborating on geospatial data and applications in a cost-effective manner. With the support of JICA's geospatial unit and its consultant teams, CNDG is exploring innovative approaches to improve the quality and accessibility of geospatial data in Timor-Leste. This collaboration aims to build local capacity and foster sustainable development through the use of advanced geospatial technologies. The project is expected to have a significant impact on various sectors, including urban planning, environmental management, disaster response, and infrastructure development. CNDG has implemented LiDAR survey and spatial mapping of Timor-Leste since 2014. Based on the Decree law no. 68/2023 September 14th article 15, CNDG has the following responsibilities: collecting, organizing, managing, producing, and disseminating geospatial information base and thematic. CNDG maintains GNSS CORS network and conducted several UAV survey projects. Future plans include tide gauge projects. CNDG has an ongoing project with JICA in topographic map creation for Dili and surrounding area. For example, go-pmtiles will be used to efficiently process and store large volumes of geospatial data, enabling quick access and retrieval of information. Tippecanoe will facilitate the creation of vector tiles, which are essential for high-performance map rendering. MapLibre GL JS will be employed to visualize geospatial data in an interactive and user-friendly manner, allowing users to explore and analyze the data effectively. Martin will provide a robust backend for serving geospatial data, ensuring reliable and scalable access to information. GitHub Pages will serve as a collaborative platform, enabling stakeholders to share and contribute to geospatial data and applications. The project is expected to result in significant improvements in urban planning, environmental management, disaster response, and infrastructure development in Timor-Leste. By providing real-time access to accurate and up-to-date geospatial information, the project will enable better decision-making and resource allocation, ultimately contributing to the sustainable development of the region. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/3ZXDGP/ WG126 Hidenori Fujimura Ernesto dos Santos Dinis Yosep Belo PUBLISH GEJKD8@@talks.osgeo.org -GEJKD8 Early Action Starts with Local Data, Role of OSM in Community Preparedness en en 20251119T160000 20251119T162500 0.02500 Early Action Starts with Local Data, Role of OSM in Community Preparedness In disaster prone urban areas like Dar es Salaam, acting early can save lives. But anticipatory action requires localized, trustworthy, and community validated data. This talk explores how OpenMap Development Tanzania (OMDTZ),in partnership with CartONG and in collaboration with the Dar es Salaam Mult Agency Emergency Team (DarMAERT), developed a community dashboard powered by OpenStreetMap (OSM) data to support local early warning and preparedness. Instead of relying solely on remote sensing or expensive early warning technology, this initiative focused on local knowledge and low cost open tools. Community members, especially youth and local leaders, were trained to map key risk features (flood hotspots, drainage points, safe shelters) using ODK tools. At the core of the project is the DarMAERT Dashboard, a custom-built platform using OSM data to visualize high risk areas and inform decisions to rescue teams. It allows for, monitoring flood-prone zones with locally collected risk indicators and prioritizing emergency response based on exposure and vulnerability More than 45 community members, including 15 local leaders and elders, were engaged in training. Their involvement ensured the data’s relevance and accuracy, while also building community ownership of the results. For example, ten-cell leaders and the elderly helped identify hidden flood-prone zones and accessible evacuation paths that were not evident in satellite imagery through participatory mapping This presentation explores how local leaders, youth, and first responders collaborated to produce real time, actionable data focusing on flood preparedness. By combining mobile data collection, maps, and a digital dashboard, the project will enable DarMAERT to anticipate risks and act earlier, not just respond after a disaster. This session will offer key takeaways: -How community-generated OSM data can inform local early warning -The role of local leaders in validating and sustaining open data -Challenges in low-tech anticipatory systems and how we addressed them -How to turn maps into tools for local governance and advocacy PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/GEJKD8/ WG126 Asha Mustapher PUBLISH BUHEKS@@talks.osgeo.org -BUHEKS Faster, simpler access to cloud-based geospatial data with Obstore en en 20251119T110000 20251119T112500 0.02500 Faster, simpler access to cloud-based geospatial data with Obstore [Obstore](https://developmentseed.org/obstore/latest/) is a Python library that abstracts how to access data on commercial cloud storage providers, like Amazon S3, Google Cloud Storage, and Azure Storage. Instead of writing code for each provider and manually creating the abstractions, use Obstore’s singular API for data access. While at its face Obstore is similar to [fsspec](https://github.com/fsspec/filesystem_spec)—they both provide abstracted interfaces to cloud storage—Obstore presents some core improvements: - Minimal API with native synchronous and asynchronous support. - Fast with no Python dependencies: obstore wraps the Rust `object_store` library, meaning that your Python environment stays small and you won’t face dependency conflicts. - Streaming downloads, uploads, and listings without manual pagination. - Full type hinting for easier use in Python IDE environments. - Simple access to [NASA Earthdata](https://developmentseed.org/obstore/latest/api/auth/earthdata/) and [Microsoft Planetary Computer](https://developmentseed.org/obstore/latest/api/auth/planetary-computer/) data collections with **automatic credential refreshing** when short-lived tokens expire. While Obstore is a foundational technology that can be used across many domains, this talk will focus on its use in geospatial-related projects: - [Zarr](https://zarr.dev/)\-Python introduced an [Obstore-based backend](https://zarr.readthedocs.io/en/latest/api/zarr/storage/index.html#zarr.storage.ObjectStore) that can be [3x faster than the default fsspec-based backend](https://github.com/maxrjones/zarr-obstore-performance) when reading Zarr datasets. - [VirtualiZarr](https://github.com/zarr-developers/VirtualiZarr), a library to present non-cloud-native file formats like netCDF as virtual Zarr datasets, is being rewritten to use Obstore by default. - [Async-tiff](https://github.com/developmentseed/async-tiff), a fast, asynchronous, Python TIFF, GeoTIFF, and Cloud-Optimized GeoTIFF reader, uses Obstore under the hood to power its data fetching. - A [new Python GeoParquet library](https://geoarrow.org/geoarrow-rs/python/latest/api/io/geoparquet/) uses Obstore as well. This talk will explain what Obstore is, how it differs from existing Python libraries, and how you might use it in your own projects to speed up your own data access. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/BUHEKS/ WA220 Kyle Barron PUBLISH B8PSGF@@talks.osgeo.org -B8PSGF The Fellowship of the Map: Open Mapping for Climate Action, Disaster Preparedness, and Building a New Generation of Gurus en en 20251119T113000 20251119T115500 0.02500 The Fellowship of the Map: Open Mapping for Climate Action, Disaster Preparedness, and Building a New Generation of Gurus What if we could harness the spirit of The Fellowship of the Ring, but for mapping? Imagine a network of passionate youth and community leaders united by a single purpose: to use open mapping for real-world impact—disaster preparedness, climate action, and community resilience. This session dives into the Open Mapping Guru Network—a powerful fellowship of mappers, activists, and mentors across Asia-Pacific, where the maps they create don’t just track roads or buildings—they chart a future of resilience, inclusion, and hope. We'll explore The Origin of the Fellowship: How the Open Mapping Guru Network was born and how it has grown into a cross-border network that empowers youth leaders, supports local communities, and builds strong partnerships. How mappers from different walks of life are using tools like Tasking Manager, MapSwipe, KoboToolbox, and more to respond to climate crises, map disaster-prone areas, and create safe spaces. From the humble beginnings of a mapper to a leader, mentor, and community builder—hear stories of Gurus making a difference in their communities and in the open mapping movement. The power of community solidarity, mentorship, and peer learning in growing the open mapping movement. Why collaboration, not isolation, is key to long-term impact. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/B8PSGF/ WA220 MIKKO TAMURA PUBLISH LPCJRL@@talks.osgeo.org -LPCJRL Improving Climate Data Delivery and Visualisation en en 20251119T120000 20251119T122500 0.02500 Improving Climate Data Delivery and Visualisation Some GIS (Geographic Information System) technologists present today may remember maps before they became 'slippy'. But young or old, we should now question some prior assumptions that shaped the current GIS web ecosystem. Considering its evolution – based on prior learning and precedents – I suggest we re-examine some of the premises underlying NetCDF gridded data. Are legacy OGC (Open Geospatial Consortium) or tiling services really the best abstractions for delivery of NetCDF or gridded data? The following discussion also provides insights into my development of a front-end renderer. I will also briefly mention delivery mechanism services such as TDS (THREDDS - Thematic Real-time Environmental Distributed Data Server) Subsetting Service. These delivery mechanisms, coupled with modern devices and networks, allow us to explore contemporary, timely and scalable ways of delivering and viewing data that have the potential to improve the end-user experience. Following are some live demonstrations for your consideration. My findings are especially useful to custodians, publishers and climate/earth data researchers who seek better alternatives of delivery and visualisation of this important information. If nothing else, my suggestions will help broaden audiences by delivering important data into the hands of the everyday person. And for the technologists present today, I will share some limitations encountered during my journey and possible future directions. Who knows? Perhaps you can also contribute. See Also; - [CFRender - Github Code Repo](https://github.com/harrishudson/CFRender) - [Vista Manifest - Github Code Repo](https://github.com/harrishudson/VistaManifest) - [Demonstration Website - vistamanifest.com](https://vistamanifest.com) - [Short recordings of demonstration website](https://harrishudson.com/vistamanifest_examples/) - [Summary of Presentation](https://harrishudson.com/FOSS4G2025/HarrisHudson_talk_THREDDS3.odp) - [Bio](https://harrishudson.com) PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/LPCJRL/ WA220 Harris Hudson PUBLISH LHCTKU@@talks.osgeo.org -LHCTKU 1% AEP Current and Future Climate Flood Maps for Aotearoa New Zealand en en 20251119T133000 20251119T135500 0.02500 1% AEP Current and Future Climate Flood Maps for Aotearoa New Zealand Introduction Flood inundation modelling and the resultant flood maps are essential for understanding, planning for, and responding to flood events. Flooding is one of the most costly and impactful hazards facing Aotearoa, and the frequency and severity of flooding is projected to rise under a warmer future climate. Historically in Aotearoa, flood hazard products have been produced at the local and regional levels using locally defined methodologies leaving Aotearoa without nationally consistent flood map products available for nation-wide hazard, risk and future climate analyses. This is the aim of the Endeavour project Mā te haumaru ō te wai: flood resilience Aotearoa. Mā te haumaru ō te wai aims to adhere to the principles of open science and access. Our methodology uses open-source software where possible, while also contributing directly to the development of two open-source software projects: BG_Flood and GeoFabrics. Additionally, where possible we use open data sources as inputs into the modelling methodology. Finally, the current and future climate scenario flood maps are published in an open-access data repository. We present the automated cascaded modelling methodology used to produce nationally consistent fluvial and pluvial flood inundation maps by integrating climate science, rainfall statistics, hydrology, hydrodynamics and geospatial data for 256 flood domains across Aotearoa New Zealand. We apply this methodology to produce four current and future climate scenarios at a 1% Annual Exceedance Probability (AEP) for current, 1°, 2° and 3°C warmer than current conditions and present a summary of these results. Method We created a cascaded modelling methodology to produce nationally consistent flood maps (Figure 1) consisting of five major stages: flood domain definition, topography and roughness generation, storm generation, hydrology modelling, and hydrodynamic modelling. The workflow was fully automated using Cylc [1], an open-source workflow engine, to control the progress between different stages. This allowed the workflow to be run across all flood domains for the current and future climate scenarios in an automated fashion. Our modelling methodology begins with the definition of floodplains and associated catchments (Figure A.1.) where we perform our coupled hydrology (Figure A.4.) and hydrodynamic (Figure A.5.) modelling. For each catchment, a design rainfall event is created in the storm generation stage (Figure A.3) which forms another key input to both the hydrology and hydrodynamic modelling stages. The topography and roughness stage (Figure A.2) produces hydrologically conditioned Digital Elevation Models (DEMs) and hydraulic roughness layers across Aotearoa New Zealand which form third key input to the hydrodynamic modelling stage. A flood inundation map showing the maximal flood depths across the current climate scenario is shown for an example domain, the West Coast Fox River (Figure B). The catchments and associated floodplains are defined from a set of basic manual outlines, a NZ wide river network, and nationwide population and building information. The manual outlines roughly indicate each floodplain in the country act to ensure appropriate groupings of nearby river courses. The floodplains are defined using a process to propagate up from the river mouth(s) (or downstream reach for inland catchments) to define relatively flat populated areas with available LiDAR where higher detail hydrodynamic modelling is undertaken. During this stage, river injection points are defined at the intersection between the flood plains and the river network; they are used to couple the hydrology model used in the upper catchment with the hydrodynamic model used over the floodplain in the lower catchment. We use the Aotearoa-specific High Intensity Rainfall Design System (HIRDS) [2] to generate our rainfall events. HIRDS is an open tool (https://hirds.niwa.co.nz/) for generating rainfall estimates for a specified AEP and duration at any location across New Zealand where the rainfall estimates are derived from historic rainfall observations as well as other climatic and topographical information. All hydrology modelling in our workflow is performed using the Aotearoa New Zealand TopNet model [3]. We hydrologically model rainfall events with durations between 6 and 72hrs to experimentally determine a realistic worst case storm duration for each catchment. In each catchment, the selected worst-case duration 1% AEP rainfall event was used to force the coupled hydrology and hydrodynamic model. Hydrodynamic modelling was performed using BG_Flood [4] an open-source software (OSS) GPU-enabled adaptive resolution shallow-water solver that supports rain-on-grid. BG_Flood was actively developed as part of this project. The TopNet river flows from the upper catchment are injected into the hydrodynamic model around the edge of the floodplain. The rainfall event over the floodplain is also included directly to the hydrodynamic model as rain-on-grid. The NZ tide model, with open access through an online tool (https://tides.niwa.co.nz/), was used to provide tidal forcings (mean high water spring tide) around the coast. The hydrodynamic modelling also requires a hydrologically conditioned DEM and hydraulic roughness. These were produced using the OSS Python package GeoFabrics [5], which was also developed for this project. GeoFabrics is a Dask enabled Python tool for creating hydro DEMs and hydraulic roughness from LiDAR point clouds, other elevation, natural feature and infrastructure information. This was included within the workflow so that the topography and roughness information could be updated as LiDAR coverage increased across New Zealand from 20% at the project inception to 80% today. Results We developed our workflow with a focus on iterative improvement. As such, we performed our first nationwide run concluding June 2024. These results were limited to 1% AEP at current climate and an 8m resolution. These were reviewed to identify key areas of improvement. Specifically, we identified: more realistic storm durations, inclusion of inland floodplains, inclusion of lakes and 150 missing culverts, the opening of more than 100 river mouths, and modelling to a resolution of 4m. We have completed our second nationwide run to a resolution of 4m concluding June 2025 across four scenarios: 1% AEP at current, 1°, 2°, and 3°C warmer future climate. In our presentation we will cover several catchments in detail and share summary results comparing the current and future climate scenarios. We will also share the open-data repository where the flood inundation maps across each catchment and scenario can be accessed. Finally, we will highlight how these products can be used to access impact through risk modelling in future studies. References [1] Oliver et al., (2018). Cylc: A Workflow Engine for Cycling Systems. Journal of Open Source Software, 3(27), 737, https://doi.org/10.21105/joss.00737 [2] Carey-Smith, T., et al., (2018) High Intensity Rainfall Design System Version 4, NIWA Client Report 2018022CH prepared for Envirolink, retrieved from https://niwa.co.nz/climate-and-weather/hirdsv4-usage [3] Bandaragoda, C., et al., (2004). Application of TOPNET in the distributed model intercomparison project. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2004.03.038 [4] Bosserelle, C., et al., (2022), BG-Flood: A GPU adaptive, open-source, general inundation hazard model; Australiasian Coasts and Ports 2021. https://github.com/CyprienBosserelle/BG_Flood. [5] Pearson, R et al., 2023, Geofabrics 1.0.0: An Open-Source Python Package for Automatic Hydrological Conditioning of Digital Elevation Models for Flood Modelling. Environmental Modelling and Software. http://dx.doi.org/10.2139/ssrn.4463610 PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/LHCTKU/ WA220 Rose Pearson PUBLISH 9KSQVT@@talks.osgeo.org -9KSQVT I Love a Sunburnt Country: Address Data Standardisation and Enrichment in an Austrailan Context en en 20251119T140000 20251119T142500 0.02500 I Love a Sunburnt Country: Address Data Standardisation and Enrichment in an Austrailan Context Different regions, cultures and countries all have different approaches to the problem of "Addressing". This talk will cover how a private Australian government owned organisation produces a decades old nationalised Australian address file that is extremely broadly used and highly regarded. This main address file is provided for free as "G-NAF" the Geocoded National Address File from `data.gov.au` and reputedly accounts for a very large proportion of downloads from that government resource. Just using open sources, that is what information is available from administrative/governmental bodies and what can observed from the streets and skies, large datasets of insights can be added to the address database. In the decades during which this address set has been maintained and technology has evolved, we have and continue to constantly work to provide this enrichment. Streets, borders and zoning are low hanging fruit these days. Insights such as buildings, trees, swimming pools and solar panels are well established as being available, though constantly improving in quality. In particular we're proud of the work we do providing the best information for emergency services, a surprisingly difficult challenge in some significant edge-cases. Today's most interesting new additions are around land use, aboriginal lands, as well as addressing risk and sustainability around our famous floods and fires. This fast-paced talk will cover some of the most interesting challenges to constantly producing all this information from a senior software engineer inside the company. Not leastly discussing OSGeo's tools that are fundamentally important to us and for which we're extremely grateful. We will briefly touch on some cool uses we have for these tools and will discuss some of the challenges we encountered in doing this work. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/9KSQVT/ WA220 Elena Williams PUBLISH GSRDLN@@talks.osgeo.org -GSRDLN Mapping Community Capital: Using FOSS and Open Data to Reveal Local Gaps in Rural Access and Opportunity en en 20251119T143000 20251119T145500 0.02500 Mapping Community Capital: Using FOSS and Open Data to Reveal Local Gaps in Rural Access and Opportunity The Community Capitals Framework (CCF) offers a structured approach to assessing community strengths and weaknesses across seven forms of capital—natural, cultural, human, social, political, financial, and built. Over the past several years, our team has collected and analyzed data indicators aligned with this framework to evaluate the strengths and weaknesses of cities and counties in the Midwest United States. Drawing on publicly available data, we’ve assessed how various forms of capital—such as built infrastructure, human services, and social networks—vary across communities. While these assessments have offered valuable insights at the county and city level, they often mask important disparities within communities themselves. Our current focus shifts to analyzing those same indicators at a finer spatial scale to better understand how access to critical services and assets is distributed within individual communities. To uncover these internal disparities, we apply spatial analysis techniques using free and open-source software (FOSS) and openly available geospatial data. We begin by analyzing access to key community assets—such as parks, healthcare facilities, broadband infrastructure, and educational institutions—across a multi-county rural region, identifying differences in regional availability. We then zoom in on selected case study communities to map the neighborhood-level distribution of those same assets, revealing gaps in access that are often hidden by broader county- or city-level aggregates. Using free and open-source tools we perform proximity analysis, service area analysis, and spatial overlays to identify underserved areas—places where residents may face barriers to opportunity, infrastructure, or essential services. Our case studies illustrate how a place-based, spatial lens—grounded in open data tools—can guide more equitable rural planning, investment, and community engagement. This session will highlight our methods, case study findings, and how these insights can inform targeted investment in small rural communities. Our reproducible, open-source workflow is designed for use by researchers, planners, and community stakeholders aiming to apply geospatial analysis in low-resource environments. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/GSRDLN/ WA220 Christopher J. Seeger PUBLISH Y3UMBY@@talks.osgeo.org -Y3UMBY Implementing Interactive Indoor Maps with MapLibre and IMDF en en 20251119T153000 20251119T155500 0.02500 Implementing Interactive Indoor Maps with MapLibre and IMDF While outdoor maps are ubiquitous, navigating large and complex indoor venues like shopping malls and airports remains a significant challenge for users. Traditional solutions, often static image-based maps provided by facility operators, lack the flexibility required for modern applications. These image maps are difficult to update and do not support crucial features like interactive search or real-time data integration, as the locational information is not structured as usable data. This presentation tackles these challenges head-on. We will demonstrate how to build a powerful, data-driven indoor mapping solution using open-source tools. First, we will introduce the Indoor Mapping Data Format (IMDF), an international standard by Apple, and explain how to model a venue's floors, units, and points of interest. Next, we will walk through the technical implementation of parsing this IMDF data and rendering it as an interactive, multi-layered indoor map with MapLibre. Finally, going beyond simple map display, we will detail the implementation of a practical search feature. Attendees will learn how to leverage the structured IMDF data to allow users to find specific points of interest, such as stores that are currently open or the nearest available restroom, transforming the map from a static image into a dynamic and genuinely useful tool. Key Takeaways for the Audience: - A solid understanding of the Indoor Mapping Data Format (IMDF) structure and how to apply it to a real-world venue. - A step-by-step guide to processing IMDF data and rendering it as a multi-level indoor map using MapLibre. -Actionable techniques for implementing interactive features such as floor selection and displaying store or amenity information in a sheet when tapping annotations on the indoor map. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/Y3UMBY/ WA220 Haruki Inoue PUBLISH FDJC9Y@@talks.osgeo.org -FDJC9Y GeoFM with OpenDataCube - From arrays to embeddings en en 20251119T160000 20251119T162500 0.02500 GeoFM with OpenDataCube - From arrays to embeddings This talk presents technical implementation and intercomparison of various geospatial foundation models using TerraTorch , the twist is loading satellite data collections via OpenDataCube tooling rather than built in sample datasets. A bit of pre-processing is necessary to make the foundation models comply with the dynamics of the data as-is from cloud native sources, however they stand up quite well and are generalizable for problems they are trained for. In addition, they can also generate embeddings which can be thought of as dimension reduced versions of source arrays that can then be used to fine tune and perform additional tasks the initial foundation model was not trained for. This approach is used for demonstrating application of multiple foundation models - Prithvi v2.0, Clay and DOFA for burn scar mapping (Segmentation) and water quality inference (Regression) tasks. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/FDJC9Y/ WA220 Tisham Dhar PUBLISH 9FVHFA@@talks.osgeo.org -9FVHFA Assessment of Display Performance and Comparative Evaluation of Web Map Libraries for Extensive 3D Geospatial Data en en 20251119T110000 20251119T112500 0.02500 Assessment of Display Performance and Comparative Evaluation of Web Map Libraries for Extensive 3D Geospatial Data In recent years, Japan has experienced significant advancements in the development of large-scale three-dimensional urban models, exemplified by initiatives such as Project PLATEAU, spearheaded by the Ministry of Land, Infrastructure, Transport and Tourism, and VIRTUAL SHIZUOKA, undertaken by Shizuoka Prefecture. As of March 2025, Project PLATEAU offers open data for 3D models (LOD1) encompassing over 23 million buildings across 260 cities. Concurrently, VIRTUAL SHIZUOKA has generated 30 terabytes of 3D point cloud data, covering the entire prefecture (7,200 km²), which is utilized as a digital twin at a 1:1 scale. The efficient visualization of these extensive datasets within web map environments has emerged as a critical technical challenge in the construction of digital social infrastructure. In this study, we focused on Numazu City in Shizuoka Prefecture (population approximately 180,000; area 186.85 km²), which contains both datasets. We converted the 3D point cloud data (LAS format) from VIRTUAL SHIZUOKA and the PLATEAU building models (CityGML format) into a unified data format to evaluate web map display performance. During the data conversion process, we employed open-source tools such as “PDAL” and “PLATEAU GIS Converter” to convert the data to the 3D Tiles 1.1 specification—endorsed as an OGC standard in 2023—and the MVT (Mapbox Vector Tiles) format. The 3D Tiles 1.1 specification marks a substantial advancement from the previous 1.0 version, incorporating technological innovations such as optimized hierarchical level of detail (HLOD), implicit tiling, support for multigranular semantic metadata, and direct integration with glTF 2.0, which have significantly enhanced the streaming performance of large-scale geospatial data. A comparative analysis was undertaken to evaluate the display performance of two prominent WebGL-based web mapping libraries: CesiumJS and MapLibre GL JS, the latter integrated with deck.gl and loaders.gl. CesiumJS is widely regarded as the de facto standard for global-scale 3D visualization, noted for its efficient streaming of large datasets and hierarchical level of detail (HLOD) management, which is enabled by its optimized rendering pipeline and native support for 3D Tiles. In contrast, MapLibre GL JS is architecturally designed for vector tile delivery and, through its integration with deck.gl, offers high-performance 3D rendering capabilities. These libraries are based on distinct design philosophies and optimization algorithms, necessitating a detailed comparison of their performance characteristics tailored to specific use cases. Performance was evaluated using Google Chrome's Lighthouse mode, which conducted a quantitative assessment based on five core web vital metrics: First Contentful Paint, Largest Contentful Paint, Speed Index, Total Blocking Time, and Cumulative Layout Shift. These metrics specifically examine five critical elements: the duration required for the initial content to become visible, the time until the largest element on the page (such as an image or heading) is rendered, the speed at which the overall loading process is perceived visually, the total duration during which the page is unresponsive, and the visual stability of the layout of the page. Notably, the introduction of Interaction to Next Paint (INP) in March 2024 facilitated a more precise measurement of user interaction responsiveness in 3D applications than the previous method. For the evaluation, two scales were established: a broad 10 km-square grid (second-level mesh) and a narrow 1 km-square grid (third-level mesh), which are the standard data preparation ranges in Japan. This approach enabled a systematic analysis of the influence of data volume on display performance. The evaluation indicated that CesiumJS demonstrates superior performance in loading 3D Tiles, particularly during the initial rendering of extensive point cloud data (secondary meshes). Conversely, MapLibre GL JS exhibited remarkable speed in rendering lightweight MVT data, achieving significant results in initial content display (FCP) and layout stability (CLS). In comparing data formats, 3D Tiles were found to be more memory efficient due to their incremental loading capabilities for large datasets, whereas the MVT format offered enhanced responsiveness owing to its lightweight nature. Notably, the hierarchical level-of-detail functionality of 3D Tiles became apparent when transitioning from tertiary to secondary meshes, effectively mitigating performance degradation in wide-area displays. These quantitative assessments have elucidated optimal library selection guidelines based on specific use cases and the technical constraints associated with distributing large-scale three-dimensional data on the web. The technical significance of this study is underscored by its comprehensive performance evaluation utilizing practical-scale datasets amidst a period of technological innovation characterized by the increasing adoption of WebGPU and the standardization of the OGC 3D Tiles 1.1 specification. Specifically, the study offers technical insights into large-scale datasets generated by Japan’s 3D geospatial data development projects and elucidates implementation guidelines for web-based 3D GIS applications. Additionally, a two-screen comparison viewer was developed, facilitating the simultaneous comparison of different datasets and visualization methods, thereby enabling an intuitive understanding of the data differences. Looking ahead, it is imperative to consider compatibility with next-generation technologies, such as data development using CityGML 3.0 and integration with WebXR. Co-Authors: Yohei SHIWAKU（Geolonia Inc.）, Takayuki MIYAUCHI (Geolonia Inc.), Daisuke YOSHIDA (Osaka Metropolitan University) and Yuichiro NISHIMURA (Nara Women's University) PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/9FVHFA/ WG404 Toshikazu Seto PUBLISH 7RPYH7@@talks.osgeo.org -7RPYH7 Evaluating the reliability of Crowdsourced Weather data for urban heat assessment: a case study in Melbourne en en 20251119T113000 20251119T115500 0.02500 Evaluating the reliability of Crowdsourced Weather data for urban heat assessment: a case study in Melbourne 1. Introduction Providing air temperature data with high spatial and temporal resolution remains a challenge in urban research as at least one of these conditions is often not met. The concept of crowdsourcing holds significant potential in solving that issue, particularly in urban areas where population density is high. Muller et al. (2015) defined crowdsourcing as the gathering of atmospheric data from publicly accessible sensors connected to the internet. Current and historical data from Citizen Weather Stations (CWS) are accessible through various online platforms, enabling broader use for local climate analysis and research. For example, Muller et al. (2015) highlighted various web-based initiatives and climate-related crowdsourcing efforts. Among them, Netatmo (https://www.netatmo.com/en-us/weather) and Weather Underground (https://www.wunderground.com/) have been used in urban climate research (Chapman et al., 2017; Fenner et al., 2021). CWS are run by diverse users, with varying quality control and sensor placement in different environments. As a result, the accuracy of CWS data can vary significantly. Several studies have established quality control (QC) procedures to tackle uncertainties in CWS data and remove inaccurate observations. These methods either depend on reference data from Professionally operated weather stations (PWRS) or apply statistical techniques that do not require external meteorological data (Chapman et al., 2017; Meier et al., 2017). One recent approach is CrowdQC+ (Fenner et al., 2021), which filters out erroneous data based on the principle that the collective observations of the crowd, referred to as the “wisdom of the crowd”, are more reliable than those from any single CWS. This paper examines data quality control through following that principle, incorporating a comparative analysis with PWRS data using spatial interpolation techniques. 2. Methods 2.1. CrowdQC+ Five northern suburbs of Melbourne, Victoria, Australia, were selected including the Local Government Areas (LGAs) of Brimbank, Maribyrnong, Moonee Valley, Merri-bek, and Darebin. Hourly air temperature data from Wunderground and Netatmo stations were collected within the study area and a 10 km buffer zone, covering the period from December 1st, 2022, to February 28th, 2023. A first QC has been conducted using the CrowdQC+ package in R by Fenner et al. (2021). The main QC steps in the tool are: (m1) latitude and longitude check, (m2) distribution check, (m3) validity check, (m4) temporal correlation check, and (m5) spatial buddy check. 2.2. Spatial interpolation and comparison with PWRS data Using the quality controlled CWS data, air temperature measurements were compared against PWRS data to further evaluate consistency and potential biases. First, the daily maximum air temperature recorded by CWS located within a 2000 m radius and situated in similar Local Climate Zones (LCZs) was compared to corresponding PWRS data from five Bureau of Meteorology stations in and around the study area, as done by Napoly et al. (2018) and Fenner et al. (2021). Second, CWS air temperature was interpolated using Empirical Bayesian Kriging (EBK) at 06:00 AM on a single observation day. This time was chosen as it typically reflects stable atmospheric conditions with minimal human activity and limited solar influence, occurring just before sunrise, as also noted by Chapman et al. (2017). EBK is a geostatistical interpolation method that predicts values by accounting for both distance and spatial autocorrelation, using a variogram to model how similarity between points decreases with distance. Kriging is especially effective in relatively flat and homogeneous terrains, such as Melbourne’s northern and northwestern suburbs (Dodson & Marks, 1997). Predicted air temperature at PWRS stations using the EBK layer were compared to observed values. A Wilcoxon signed-rank test assessed the difference between observed and predicted air temperatures. 3. Results 3.1 Data availability over the levels of quality control As the QC process progressed, the number of stations meeting the criteria gradually decreased. The original dataset comprised 465 stations. At the m3 quality control level, 460 stations passed the validity check. This number slightly decreased to 457 at the m4 level, following the temporal correlation check. A moderate reduction was observed at the m5 level (spatial "buddy check"), with 277 stations meeting the required standards. 3.2. Comparison of the average maximum temperatures between CWS and PWRS stations Scatter plots comparing daily maximum air temperature data from PWRS and CWS stations, within areas where LCZs are uniform over a 2000 m radius, between December 2022 and February 2023 reveal that CWS stations tend to record slightly higher temperatures. This difference is likely due to the siting of CWS in more built-up or enclosed environments compared to the typically open settings of PWRS stations. 3.3. Comparison of predicted and observed air temperatures The interpolated spatial distribution of predicted air temperature at 6:00 AM across the study area, generated based on five PRWS stations shows a clear temperature gradient, with cooler conditions (18.0–19.5 °C) in the northern and northeastern of Melbourne, particularly around Melbourne and Essendon Airports, and progressively warmer temperatures (20.5–21.5 °C) toward the southern and southeastern areas. The Wilcoxon signed-rank exact test produced a test statistic of V = 0 and a p-value of 0.0625, indicating no statistically significant difference between the two sets of values at the 5% significance level. the model achieved a high coefficient of determination (R² = 0.989), indicating that 98.9% of the variance in the observed data is explained by the predictions. The root mean square error (RMSE) of 0.306 further supports the model’s accuracy, reflecting a low average prediction error. 4. Conclusion This study demonstrates the feasibility and reliability of using CWS data for high-resolution urban temperature analysis. The quality control process proved effective, retaining 277 out of the original 465 stations after a series of checks, including validity, temporal correlation, and spatial consistency. Comparison with PWRS data revealed that CWS stations tend to record slightly higher maximum temperatures, likely due to their location in more built-up or enclosed environments. The findings highlight the importance of spatial statistics in leveraging CWS data for urban air temperature assessments. 5. References - Chapman, L., Bell, C., & Bell, S. (2017). Can the crowdsourcing data paradigm take atmospheric science to a new level? A case study of the urban heat island of London quantified using Netatmo weather stations. International journal of climatology, 37(9), 3597-3605. - Dodson, R., & Marks, D. (1997). Daily air temperature interpolated at high spatial resolution over a large mountainous region. Climate research, 8(1), 1-20. - Fenner, D., Bechtel, B., Demuzere, M., Kittner, J., & Meier, F. (2021). CrowdQC+—a quality-control for crowdsourced air-temperature observations enabling world-wide urban climate applications. Frontiers in Environmental Science, 9, 720747. - Meier, F., Fenner, D., Grassmann, T., Otto, M., & Scherer, D. (2017). Crowdsourcing air temperature from citizen weather stations for urban climate research. Urban Clim 19: 170–191. In. - Muller, C., Chapman, L., Johnston, S., Kidd, C., Illingworth, S., Foody, G., Overeem, A., & Leigh, R. (2015). Crowdsourcing for climate and atmospheric sciences: current status and future potential. International journal of climatology, 35(11), 3185-3203. - Napoly, A., Grassmann, T., Meier, F., & Fenner, D. (2018). Development and application of a statistically-based quality control for crowdsourced air temperature data. Frontiers in Earth Science, 6, 118. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/7RPYH7/ WG404 Percy Yvon Rakoto PUBLISH XKFCXZ@@talks.osgeo.org -XKFCXZ Development of open-source digital twins for automated analysis of flood risk en en 20251119T120000 20251119T122500 0.02500 Development of open-source digital twins for automated analysis of flood risk Digital Twins are dynamic virtual representations of physical systems, with automated data exchange and analytics being key attributes; they are enabling the development of smart cities and may also represent the natural environment. For example, over the next several years the EU's “Destination Earth” system is being developed as a Digital Twin for climate services, to facilitate access to weather and climate models which can be used for impact studies. Our research is developing scalable open-source environmental Digital Twin technology, applicable to diverse geospatial applications. We have applied this to address the complex geospatial analysis required for compound flood risk assessment (fluvial, pluvial and coastal). Our system enables automated scenarios for mitigation and adaptation, particularly those for natural flood management, while accounting for climate change; once complete, our system will be able to ingest weather and climate model data from the Destination Earth system, further enabling scalability. Through our close engagement with our indigenous partners, we are ensuring that mātauranga Māori (indigenous knowledge) is embedded within the system and scenarios developed. This will help to ensure that flood mitigation measures developed recognise Māori values and practices, as needed under the United Nations’ Declaration on the Rights of Indigenous Peoples. Here, we present our fully open-source Digital Twin software framework including the core Environmental Digital Data Intelligence Engine (EDDIE), a data management system which is generically applicable to multiple use cases, and a module which interfaces with this for flood risk assessment, the Flood Resilience Digital Twin (FReDT). This framework provides the generic base for data ingestion, model configuration, and data visualisation that can be applied to many distinct use cases through extensions. In the implementation presented here, FReDT interacts with EDDIE to automatically produce, run, ingest, analyse and visualise outputs from flood model simulations. Users can interact with FReDT by using a 3D geospatial web application based on the open-source geospatial library TerriaJS for control and visualisation. Alternatively, users can interact using an API via Open Geospatial Consortium standards such as WPS for starting models and WFS or WMS for retrieving data to use with existing tooling such as GIS software. To begin processing a flood model scenario, users of FReDT must specify an area of interest and provide configuration parameters for the flood model, such as choosing the projected year for a climate scenario. Once the backend receives a processing request, it begins a Python Celery worker task to model the flood extents and depths and assess impact. Open data sources such as LiDAR terrain datasets, sea level rise predictions, river network shapes and statistics, and more are downloaded, processed and saved to database. If the same data is later required for another scenario run, then can be retrieved from the database, or it can be flagged for update and new data will be retrieved when required. Dynamic model forcing data (rainfall, river flows, tide levels) are currently produced statistically from open-data sources, and the system will be extended to include live observations and forecasts, predicted streamflow from a hydrological model, as well as user-provided scenarios. Once the data is retrieved and processed it is passed through to the flood inundation numerical modelling software BG-Flood. Outputs from BG-Flood are passed through to the user and are also passed through to post-modelling analysis stages such as cross-referencing with building footprint polygon datasets to predict which buildings may be inundated for the scenario assessed. These data are presented to the users of the web application as geospatial layers on a 3D map, with a time slider to explore the depth of flooding over the course of the event, a comparison slider for viewing multiple scenarios side-by-side, and the ability to query the features such as individual buildings and the depth of a location to see a plot of depth over time. The software system uses multiple containers to provide backend, frontend, and database services. The processing chains required for running flood models in new areas can take hours or more, especially for large domains. Currently work is under development to allow running the digital twin software on AWS cloud Elastic Container Services, which will allow for processing nodes to expand resources during periods of high demand and reduce outside of those times. Physics-based Digital Twins such as FReDT will revolutionise access to and use of numerical model predictions, through a “digital twin web” powered by rapidly growing data and distributed cloud computing. Yet individual components need to be built and tested to ensure they are fit-for-purpose, democratic and adaptive to society’s needs. Our research is enabling this, initially in Aotearoa New Zealand but with global applicability. Building on the existing FReDT codebase, our current research is adding a hydrological model to allow upper river catchment changes to be accounted for in downstream flood risk and enable land management scenarios such as reforestation. Our aim is to facilitate rapid, low-cost risk assessments with on-demand scenario analytics, and effective ways to visualise and communicate this risk and its associated uncertainties, with communities placed at the centre by enabling them to participate in the design of solutions for flood mitigation and adaptation. GitHub repository: https://github.com/GeospatialResearch/Digital-Twins PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/XKFCXZ/ WG404 Matthew Wilson Luke Parkinson PUBLISH SKWYSQ@@talks.osgeo.org -SKWYSQ A5 Technical Deep Dive: The Mathematics of Pentagonal Perfection en en 20251119T133000 20251119T135500 0.02500 A5 Technical Deep Dive: The Mathematics of Pentagonal Perfection ### Overview Mathematics and geometry have always been humanity's tools for understanding and organizing space. This presentation takes you on a visual journey through the mathematical foundations of A5, revealing how geometric principles dating back to ancient Greece combine with modern computational techniques to create a new spatial indexing system. ### The Mathematical Quest: Why Break From Regular Polygons? A fundamental question drives A5's innovation: why restrict ourselves to regular polygons when projecting onto a sphere warps all shapes anyway? Traditional DGGS approaches use regular polygons (triangles in HTM, squares in S2, hexagons in H3) on platonic solids, but projection distorts these shapes significantly. A5 recognizes that since projection destroys regularity regardless, we should optimize for the final spherical result rather than the initial planar form. This insight led to embracing irregular equilateral pentagons that, while not regular on the plane, achieve superior properties when projected onto the sphere. ### The Five Platonic Solids: Building Blocks of Space To understand A5, we must first explore the five Platonic solids - the only three-dimensional shapes where all faces are identical regular polygons. These geometric forms, known since antiquity, provide the foundation for all Discrete Global Grid Systems (DGGSs). Each platonic solid offers different characteristics: - **Tetrahedron** (4 triangular faces): Highest vertex curvature - **Cube** (6 square faces): High vertex curvature - **Octahedron** (8 triangular faces): Moderate vertex curvature - **Icosahedron** (20 triangular faces): Low vertex curvature - **Dodecahedron** (12 pentagonal faces): Lowest vertex curvature The key insight is that vertex curvature directly relates to distortion when projecting onto a sphere. The dodecahedron, with its twelve pentagonal faces, has the lowest vertex curvature of all platonic solids, making it the most "sphere-like" geometric form. ### Hilbert Curves: Elegant Spatial Indexing A critical feature in A5 is how cells are spatially indexed using Hilbert-like curves - space-filling curves that map one-dimensional integer sequences to two-dimensional spatial arrangements. This elegant mathematical solution ensures that cells with similar locations have similar cell IDs, leading to several desirable properties: - **Spatial Locality**: Nearby cells in space have nearby IDs in the integer sequence, enabling efficient spatial queries and neighbor finding operations. - **Hierarchical Consistency**: Parent cells are guaranteed to overlap with their children, though they don't cover them exactly. This overlap ensures spatial coherence across resolution levels. - **Efficient Traversal**: The curve's path through the pentagon hierarchy creates natural ordering for spatial operations and range queries. ### Efficient Encoding: What can fit into 64 bits? The mathematical journey culminates in A5's elegant encoding system. Each cell is represented as a 64-bit integer, enabling: - **Hierarchical Addressing**: Parent-child relationships encoded in bit patterns - **Computational Efficiency**: Fast spatial operations using integer arithmetic - **Millimeter Precision**: Extraordinary accuracy at the finest resolution PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/SKWYSQ/ WG404 Felix Palmer PUBLISH VDJC93@@talks.osgeo.org -VDJC93 Moving from Science to Product: Making Open GIS Software Work for Us en en 20251119T140000 20251119T142500 0.02500 Moving from Science to Product: Making Open GIS Software Work for Us At CTrees, we create machine learning models that integrate multiple data sources to produce high-resolution, time-series datasets on forest carbon and activity. Our outputs— estimates of carbon stocks, emissions, removals, and forest change—support projects ranging from jurisdictional analysis to deforestation monitoring. However, scaling from research-driven models to a production-ready platform required solving two major challenges: (1) data management and (2) enabling fast, on-demand analysis that serves both internal researchers and external users. On the data side, we faced the all-too-familiar chaos of manual and ad-hoc dataset versioning (v2, v2_final, v2_final_final), inconsistent folder structures where important data dates could be recorded in the filename or prefix, and directories packed with thousands of tiny GeoTIFFs meant to be mosaicked together. Instead of chasing S3 paths and manually stitching ad-hoc datacube files, we adopted Icechunk (open source) by Earthmover, which leverages Zarr and Icechunk to enable structured, versioned cloud-native datacube access. As a bonus, Arraylake (built on icechunk) makes it easy to view the data via a WMS service, streamlining the visualization process for both internal users and external stakeholders. The second challenge was transitioning from scientific scripts—often written in R or relying on heavy GDAL operations—to a streamlined, scalable approach. To enable on-demand analysis, we refactored these scripts into ctreeskit (https://github.com/ctrees-products/ctreeskit) , our open-source Python package that consolidates spatial processing and zonal statistics using Xarray. Currently in its pre-release (beta) version, ctreeskit is designed for flexibility, it supports researchers across CTrees in papers, reports, and internal workflows. More broadly, ctreeskit is available to anyone performing similar calculations. By standardizing these processes and ensuring transparency, we enhance reproducibility, efficiency, and accessibility, allowing both internal teams and external users to understand and verify our methodologies. With ctreeskit and Arraylake, we now have three key things: (1) a reliable way to save and access data, (2) a simple way to slice and dice high-dimensional data in the time and spatial domains, and (3) lightweight tools for running reproducible calculations. Moving from raw GeoTIFFs to an array-based Zarr model gives us fast, structured access to data without the complexity of STAC catalogs. By shifting from scattered S3 paths and heavyweight processing to a structured, cloud-native approach, we’ve made geospatial data easier to access, faster to analyze, and more transparent—helping both internal teams and external users work more efficiently. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/VDJC93/ WG404 Naomi Provost PUBLISH K77CMD@@talks.osgeo.org -K77CMD EOPF Zarr Access en en 20251119T143000 20251119T145500 0.02500 EOPF Zarr Access This talk will describe the EOPF Zarr format and present practical notebooks in Python and R for accessing ESA Sentinel data. It will touch on practical use cases for Sentinel 1, 2, and 3 Zarr data access and help users understand the EOPF ecosystem and the tooling best suited to access and exploit EOPF data. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/K77CMD/ WG404 James Banting PUBLISH DZELVK@@talks.osgeo.org -DZELVK Did you get that thing I sent you? Simplifying spatial data en en 20251119T153000 20251119T155500 0.02500 Did you get that thing I sent you? Simplifying spatial data To quote Stewart Brand: On the one hand information wants to be expensive, because it’s so valuable. The right information in the right place just changes your life. On the other hand, information wants to be free, because the cost of getting it out is getting lower and lower all the time. Regardless of which hand you hold, making sure you can deliver data efficiently and quickly is important. Spatial data is not special - it's just more data - but it poses major challenges in distribution for a number of reasons: competing file formats; multiple data types (vector and raster is just the beginning); and the most common issue: the quantity of data. This talk walks through different strategies for simplifying your data, why you might want to (and why you might not), and what choice you might make to perform the simplification using GDAL. Topics covered include: - reducing precision and delivering data suitable to scale; - filling holes and removing noise; - the Ramer-Douglas-Peucker algorithm and other common simplification algorithms; - and, when to use raster vs vector data for distributing information. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/DZELVK/ WG404 Henry Walshaw PUBLISH FJYFLZ@@talks.osgeo.org -FJYFLZ maplibre-gl-terradraw - new drawing plugin for maplibre-gl-js en en 20251119T160000 20251119T162500 0.02500 maplibre-gl-terradraw - new drawing plugin for maplibre-gl-js This talk introduces a new drawing plugin for MapLibre: maplibre-gl-terradraw. Previously, both Mapbox and MapLibre have relied on an older plugin, mapbox-gl-draw, to provide drawing functionality in maplibre-gl-js. However, mapbox-gl-draw is no longer actively maintained, making it increasingly difficult to use with MapLibre. As an alternative, Terra Draw maintained by James Milner, offers advanced drawing features for multiple mapping libraries, including Mapbox, MapLibre, OpenLayers, Leaflet, Google Maps, and ArcGIS, all within a unified user interface. Compared to mapbox-gl-draw, Terra Draw is significantly easier to use. However, integrating its full functionality into MapLibre still requires extensive configuration. I developed maplibre-gl-terradraw, a new plugin that enables a pre-configured drawing feature with a single line of code. With maplibre-gl-terradraw, users can easily add drawing controls with just a line of code. This immediately grants access to all drawing modes (point, line, polygon, rectangle, circle, etc) powered by Terra Draw. The plugin comes pre-configured with icons and additional functionalities, such as: - Selecting and deleting features - Downloading drawn features - Customizing Terra Draw options and styles, as described in the documentation Furthermore, the measure control allows users to: - Measure the distance of a line or the area of a polygon - Query elevation data from raster DEM sources (MapLibre Terrain, TerrainRGB, and Terrarium) In this talk, I will demonstrate the core functionalities of maplibre-gl-terradraw and show how easily you can integrate drawing features into your MapLibre applications. References: main project: - maplibre-gl-terradraw: MIT License. https://github.com/watergis/maplibre-gl-terradraw dependencies of the main project: - Terra Draw: MIT License. https://github.com/JamesLMilner/terra-draw - maplibre-gl-js: https://github.com/maplibre/maplibre-gl-js PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/FJYFLZ/ WG404 Jin Igarashi PUBLISH HNQRBD@@talks.osgeo.org -HNQRBD State of GeoTools, JTS Topology Suite, and ImageN en en 20251119T110000 20251119T112500 0.02500 State of GeoTools, JTS Topology Suite, and ImageN This talk covers three foundation Java Geospatial Projects that do the heavy lifting behind applications you know and love: * GeoTools - Open-source Java library that provides tools for geospatial data. * JTS Topology Suite - Java library for creating and manipulating vector geometry. * ImageN - image and raster processing We have been having an amazing year, and an amazingly active year. There have been big changes in the Java ecosystems for us to respond to. We have also been revising our community interaction as development culture changes around communication and security. Attend this talk to learn more about this projects, their project teams, and what we are doing next. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/HNQRBD/ WG802 Jody Garnett PUBLISH TJSB9P@@talks.osgeo.org -TJSB9P Scaling GeoNetwork 4.4.x in Kubernetes: Production Deployment Strategies and Performance Analysis en en 20251119T113000 20251119T115500 0.02500 Scaling GeoNetwork 4.4.x in Kubernetes: Production Deployment Strategies and Performance Analysis GeoNetwork, a widely adopted open-source metadata catalog, faces significant challenges when deployed in modern containerized environments requiring high availability and scalability. This presentation examines current capabilities and limitations for scaling GeoNetwork 4.4.x deployments in Kubernetes environments. We present a comprehensive Helm chart implementation that enables both vertical and horizontal scaling strategies for GeoNetwork instances. Our analysis reveals that while vertical scaling (CPU/memory increases) provides straightforward performance improvements, horizontal scaling presents complex challenges due to GeoNetwork's architecture, particularly around session management, database connections, and Elasticsearch cluster coordination. Key deployment pitfalls identified include persistent volume configuration issues and inter-pod file sharing. Ingress load balancer configurations that maintain catalog consistency across multiple instances. Performance benchmarking using Locust load testing platform on a catalog containing 7,000 metadata records reveals critical capacity thresholds for single GeoNetwork instances. Our tests simulate realistic user behavior including HTML page loading for metadata record viewing, providing concrete metrics for infrastructure planning and resource allocation decisions. GeoCat provides products to its customers that need to be fast, reliable and scalable. Moving the GeoCat Live product to a Kubernetes environment contributes to this goal. The presentation concludes with recommendations for organizations implementing scalable GeoNetwork infrastructure, including when to choose vertical versus horizontal scaling approaches, and operational monitoring strategies essential for production deployments. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/TJSB9P/ WG802 Jorge S. Mendes de Jesus PUBLISH JQYB79@@talks.osgeo.org -JQYB79 Data delivery simplified with GeoCat Bridge en en 20251119T120000 20251119T122500 0.02500 Data delivery simplified with GeoCat Bridge If you need to publish your (meta)data to GeoServer and/or GeoNetwork straight from QGIS or even ArcGIS, GeoCat Bridge might just be the tool for you. In this talk we'll show you how Bridge works and what it does: from converting native symbology into SLD or MapLibre GL JS, to publishing feature and raster layers to a variety of services, as well as publishing metadata records and linking them to your services. Furthermore, we're excited to announce the upcoming integrated Catalog Search functionality, which will allow you to search through OGC API Records from any server - including your own - and add the results directly to your desktop GIS. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/JQYB79/ WG802 Jeroen Ticheler PUBLISH FAF9BC@@talks.osgeo.org -FAF9BC Towards universal building blocks for cloud-native digital-twins en en 20251119T133000 20251119T135500 0.02500 Towards universal building blocks for cloud-native digital-twins The exponential growth of Earth Observation (EO) data challenges our ability to efficiently access, process, and analyze it. Conventional web service standards like WCS and WMS have long provided standardized access, but there are challenges and work-arounds associated with the scale and complexity of global datasets, including coordinate system handling. In parallel, data cubes have become valued abstractions to analyse large-scale geospatial data over space and time. However, data cubes currently can only be implemented meaningfully in projected coordinate reference systems, which limits their extent before introducing too large areal distortions. A new paradigm is emerging, built on modern data structures, formats, and APIs. This paper introduces an architecture that integrates these innovations into a universal building block for geospatial data management. The foundation of this approach is the Discrete Global Grid System (DGGS). A DGGS offers a unified spatial reference framework, partitioning the Earth's surface into a hierarchy of cells. Equal-area DGGS like ISEA or HEALPix are particularly valuable for applications in fields like catchment hydrology and land use analysis, as they ensure statistical validity by maintaining consistent cell areas across the globe. The Open Geospatial Consortium (OGC) has formalized this paradigm through its DGGS Abstract Specification and the recently finalized the OGC API - DGGS standard, which specifies a lightweight web service for accessing DGGS-organized data. In parallel, the scientific Python ecosystem has revolutionized data handling with tools like Xarray for labeled multi-dimensional arrays and its XDGGS extension for native DGGS operations. This analytical power is maximized when paired with cloud-native storage formats like Zarr. Inspired by pioneering FOSS projects like pygeoapi, TiTiler, or XPublish, our work seeks to bridge the gap between these powerful analytical backends and standardized, web-friendly access patterns. We present pydggsapi, an open-source Python server built with FastAPI that implements the OGC DGGS API standard. It exposes cloud-optimized Analysis-Ready Data (ARD), such as Zarr archives or Parquet files, where data is indexed by high-resolution DGGS cells. This architecture creates a seamless continuum between two distinct operational scales. On one end, data scientists can perform large-scale modeling by directly accessing the DGGS-indexed Zarr archives in object storage using Xarray. On the other, lightweight web and mobile clients can consume the same underlying data through the standardized, RESTful pydggsapi interface, which is documented via a built-in OpenAPI/Swagger UI. We present a conceptual framework that links data access middleware via OGC API DGGS and direct data access via cloud storage. On initialization, it connects to cloud-storage Zarr archives and extracts DGGS parameters - such as the grid type, indexing scheme, available refinement levels and variables. To optimize performance for clients, we also leverage a concept similar to classic image pyramids or overviews in Cloud-Optimized GeoTIFFs - pre-aggregating data to several DGGS refinement levels. These pyramids are efficiently can be represented using Zarr groups and the Xarray DataTree model, or in Parquet using partitioning. For light-weight interactive visualization, we implement a tiles endpoint serving Mapbox Vector Tiles (MVT) on-the-fly. This enables highly efficient in-browser rendering with WebGL libraries like MapLibre GL JS. Alternatively, DGGS-aware clients can access the DGGS API endpoints directly and request data formats like DGGS-JSON. The architecture is also extensible, featuring a connector for the ClickHouse database to serve fast, on-demand analytical queries on DGGS-indexed tables, as explored in the OGC Testbed-16 report. This overall architecture enables advanced analysis in geomorphology, land cover change, or hydrology, by accessing the full data via extensible compute frameworks like Xarray or even Apache Spark directly from cloud-based object storage. For instance, a researcher studying catchment-scale erosion can query pydggsapi for all land cover and slope data DGGS cells within their basin. The API backend accesses a massive, continental-scale Zarr dataset, and only extracts the required data. Alternatively, the user can execute an Xarray batch job computes the necessary statistics, and returns a comprehensive analysis result. In addition, user can easily coalesce data from other DGGS enabled data sources via simple cell-id based joins. This allows for a much simpler data federation. While initial benchmarks are promising, the implementation has limitations that guide future work. Xarray’s indexing of dimensions can consume significant memory with very large DGGS archives, when trying to load the full array of all DGGS cell ids. Here we compare the access with Parquet, which always gets scanned on-demand, and explore more direct Zarr-native access patterns and DGGS cell index representations. Also, the strict requirements of the OGC DGGS API, such as subzone ordering, also present implementation challenges for certain DGGS types, highlighting ongoing needs in the FOSS DGGS software landscape. The integration of a FOSS OGC DGGS API implementation with cloud-native Zarr storage represents a significant step toward universal building blocks for Earth Observation. This approach offers a powerful, dual-access pattern that serves both high-performance computing and lightweight clients from a single, consistent data foundation. In addition, joining various data variables from different data providers will be trivial based on the DGGS cell ids. We believe this model also charts a path toward a new generation of value-added, GeoAI-ready data market APIs. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/FAF9BC/ WG802 Alexander Kmoch PUBLISH TLCCXY@@talks.osgeo.org -TLCCXY Seeing Through the Crowd’s Eyes: AI-Powered Urban Insights from Street Imagery en en 20251119T140000 20251119T142500 0.02500 Seeing Through the Crowd’s Eyes: AI-Powered Urban Insights from Street Imagery Seeing Through the Crowd’s Eyes: AI-Powered Urban Insights from Street Imagery Introduction Cities are dynamic systems shaped by the interplay between built form and human experience. Traditional approaches to urban informatics often rely on top-down spatial data, such as remote sensing and census statistics, which are limited in capturing how people perceive and interact with urban space. The emergence of human-centred GeoAI offers an alternative approach by integrating street-level imagery with spatial analytics to understand cities from the perspective of their inhabitants. Street view imagery (SVI) from platforms such as Google Street View and Mapillary provides immersive, ground-level visual data that mirrors the pedestrian experience of the city. Combined with advances in computer vision, machine learning, and geographic information systems (GIS), these data enable fine-grained assessments of urban conditions, ranging from infrastructure quality and visual aesthetics to safety and comfort. This paper presents four implementation case studies that apply object detection, semantic segmentation, and perceptual modelling to address pressing issues in urban infrastructure, climate resilience, street livability, and urban form perception. Background: Human-Centred GeoAI and Street-Level Imagery Human-centred GeoAI refers to the integration of artificial intelligence techniques with spatial data to model, interpret, and support human experiences in the built environment. It draws from computer vision, geospatial science, and human geography to create tools and insights that are sensitive to both objective spatial metrics and subjective perceptions. Street-level imagery is particularly well-suited for human-centred GeoAI. Unlike satellite or aerial imagery, SVI captures the visual field as experienced by pedestrians, enabling assessments of visual enclosure, greenery, safety cues, signage, and cleanliness. Combined with deep learning techniques, these images can be analysed to detect objects, classify urban scenes, or infer human perceptions at scale. Methods Across all four case studies, we employed deep learning techniques, including object detection and semantic segmentation, applied to SVI. Outputs were integrated with spatial data in GIS to produce interpretable urban metrics and policy-relevant insights. Object Detection: In the first study, we used the DNN object detection framework to identify Stop and Give Way signs from Google Street View images. These bounding boxes were geo-located using photogrammetric triangulation and integrated into GIS for validation and spatial pattern analysis. Semantic Segmentation for Environmental Features: For climate-sensitive urban assessment, we developed a convolutional neural network model to segment tree canopy, buildings, and sky from SVI. The percentage of each class was calculated for each street segment and analysed in conjunction with land surface temperature and social vulnerability indices. Space Syntax and Visual Composition: In the third study, segmented streetscape compositions (sky, tree, and building percentages) were mapped and compared with space syntax metrics such as integration and connectivity. These spatial-social variables were analysed to determine the alignment between physical form and social interaction potential. Semantic-Based Human-Labelled Perceptions: Human perception was measured using Mapillary SVI and the MIT Places Pulse 2.0 dataset through semantic segmentation performed with deep residual networks (ResNet50), pre-trained on the ADE20K dataset. The Places Pulse dataset includes over 100,000 SVIs across 56 cities, including Melbourne, rated by participants on six perceptual indicators: Beautiful, Wealthy, Livable, Safe, Boring, and Depressing. To infer perceptual scores from SVIs, we trained six Radial Basis Function (RBF) kernel Support Vector Machine (SVM) models on the Places Pulse dataset. Semantic segmentation translated the visual content of each image into categorical features, which were then used as input for the SVM models. Five-fold cross-validation ensured robustness across varying parameter settings. These models were subsequently applied to the Mapillary SVI dataset in Melbourne, generating spatial surfaces of perceived urban quality. Case Studies Traffic Sign Detection and Spatial Registration Local governments require accurate records of street signage for safety and regulatory compliance. Our object detection pipeline achieved 95.63% detection accuracy and 97.82% classification accuracy for Stop and Give Way signs in selected areas of Melbourne. The derived locations were mapped in GIS and compared against council asset layers. This open-source workflow enables cost-effective monitoring and maintenance of road infrastructure, and is transferable to other signage types and cities. Heat-Resilient Streetscape Planning for Active Travel We identified microclimatic disparities in Bendigo's active travel corridors using street-level segmentation of tree cover and sky openness. Combining these results with satellite LST and census-derived vulnerability indices revealed areas where thermally uncomfortable walking environments overlapped with high pedestrian exposure and low greening. This informed a prioritised plan for tree planting and shading infrastructure. The method provides a scalable, equity-focused tool for urban heat adaptation. Assessing Streetscape and Social Integration Using Space Syntax Focusing on Greater Bendigo, we examined whether visually well-designed streetscapes aligned with high social interaction potential. Integration and connectivity scores from space syntax were correlated with visual segmentation outputs. Results showed that areas with high space syntax scores did not always align with aesthetically rich environments. This suggests the need for integrated design and network planning to promote livability. Demographic overlays showed older populations gravitating to highly connected areas, highlighting the social implications of spatial configuration. Perceived vs. Measured Urban Form In metropolitan Melbourne, we compared the Places Pulse-based perceptual scores derived from ResNet + SVM models with objective spatial metrics for the 5D dimensions of urban design: Density, Diversity, Design, Distance to Transit, and Destination Accessibility. Our analysis confirmed that areas with moderate density, high walkability, and green space were perceived as livable and safe. However, in very high-density zones, perceptions shifted negatively, with terms like "Depressing" and "Boring" appearing more frequently. These mismatches point to the need for planners to consider not just what is built, but how it is experienced. Discussion These case studies collectively demonstrate the potential of human-centred GeoAI to bridge the gap between technical urban analytics and lived urban experience. By leveraging crowd-sourced imagery and advanced computer vision models, planners and policymakers can generate fine-scale, scalable insights into infrastructure, climate vulnerability, social inclusion, and perceptual quality. The combination of image analysis and spatial reasoning opens new avenues for participatory planning and targeted intervention. For example, the ability to spatially map perceptions of safety or beauty enables a deeper understanding of place attachment and mental well-being. Similarly, detecting infrastructure assets and environmental features at scale supports more equitable service delivery. Conclusion and Future Work Human-centred GeoAI grounded in street-level imagery offers a promising path for more inclusive, responsive, and perceptive urban analytics. Future work will extend these methods to other cities across Australia and globally, refine perception models with more culturally specific training data, and develop interactive tools for real-time urban planning and public engagement. By embedding human perception into spatial modelling and leveraging scalable, open-source tools, we move closer to cities that are not only functionally efficient but also emotionally resonant and equitable for all. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/TLCCXY/ WG802 Qian (Chayn) Sun PUBLISH TCZTK7@@talks.osgeo.org -TCZTK7 Performance Comparison of an Offline PMTiles-Powered Map Server Running on a Raspberry Pi for Decentralized Geospatial Access en en 20251119T143000 20251119T145500 0.02500 Performance Comparison of an Offline PMTiles-Powered Map Server Running on a Raspberry Pi for Decentralized Geospatial Access ### 1. Introduction Modern web-mapping stacks like Google Maps and Mapbox deliver rich cartography by pushing pre-rendered tiles from cloud storage into a browser. Formats like COG and PMTiles have improved access but still rely on data centers with steady fiber, power, and credit cards. In regions with unstable connectivity, this model fails. Discussions by Cedeno Jimenez et al. (2022) and Rawal et al. (2024) highlight the need for decentralized geospatial information using blockchain and NFTs. However, even open datasets like OpenStreetMap often rely on centralized servers. The necessity of offline web maps, as noted by Esmail (2019), Paul et al. (2019), and Olyazadeh et al. (2017), has not been practically implemented. This study aims to measure the performance of a mobile web map server for UN operations. ### 2. Research Objectives UNVT Portable aims to quantify the capabilities of a mobile web map server. The study has four objectives: a. Design a reproducible hardware-software recipe to turn a Raspberry Pi into a zero-configuration tile server. b. Construct a reference dataset—global OSM Planet vector tiles, Global DEM, Satellites/Aerial photos, and thematic layers—packed into a PMTiles archive on a 512 GB MicroSD card. c. Benchmark PMTiles against a canonical S3/CloudFront deployment across latency, throughput, energy, and storage footprint. d. Evaluate operational suitability for UN field missions requiring 24/7 availability under erratic connectivity and power budgets. ### 3. System Architecture **Hardware:** - Raspberry Pi 5 (8 GB RAM) with a 512 GB MicroSD Card - 2.4 GHz Wi-Fi in access-point mode, emitting SSID: UNVTportable - 20,000 mAh USB power bank capable of Power Delivery at 5V / 3A **Software:** - Raspberry Pi OS LITE (64-bit) - Nginx - Network Manager for local Wi-Fi - MapLibre GL JS ### 4. Method We measured the access performance of raster and vector tile data stored as PMTiles files on an SD card within a Raspberry Pi, evaluating latency, throughput, and other factors. This was compared to internet-based access to raster and vector tiles in a typical cloud environment. We also compared the access performance of PMTiles against raw raster and vector tiles extracted into folders as XYZ tiles. Field tests determined if 24-hour continuous operation is feasible using a standard mobile battery, even without internet connectivity and during power outages. We also tested usability in environments affected by natural disasters or lacking infrastructure. ### 5. Results Both raster and vector tile data stored in PMTiles format on a Raspberry Pi's SD card demonstrated access performance (latency and throughput) equal to or surpassing that of tile datasets hosted in online cloud environments. When comparing access performance in a local environment between PMTiles-format tile data and raw raster and vector tiles extracted into folders as XYZ tiles, PMTiles was found to be sufficiently effective. Field tests confirmed that continuous operation for over 14 hours is possible with consumer-grade mobile batteries of 20,000 mAh or higher. ### 6. Discussion The PMTiles format excels in offline environments due to its single-file architecture, consolidating millions of HTTP requests into efficient linear range requests. This design is advantageous on microSD cards, where random seek times can impact performance. By minimizing these seeks, PMTiles delivers faster response times, enhancing user-perceived speed in resource-constrained settings. The format’s streamlined structure simplifies critical field operations, such as checksum verification and data copying, reducing the risk of errors during data transfers. PMTiles’ compatibility with lightweight hardware like Raspberry Pi optimizes storage efficiency and reduces power consumption, making it ideal for decentralized, offline mapping applications, including UN field operations. ### 7. Conclusion The UNVT Portable system showcases the potential of a €200, pocket-sized Raspberry Pi-based computer to provide global-scale, interactive mapping capabilities with performance rivaling cloud-based solutions, all while operating entirely offline. Comprehensive benchmarks validate its exceptional performance, demonstrating low latency and significant throughput advantages over traditional XYZ raw tile formats. The system achieves an impressive full day’s battery life using standard commodity power banks, enhancing its practicality for extended field use. These results represent a pivotal advancement toward decentralizing geospatial data access, transforming interactive maps from a cloud-dependent privilege into a universally accessible resource. UNVT Portable fosters egalitarian access to critical geospatial information, empowering communities, humanitarian workers, and volunteers in remote or underserved regions to leverage mapping technology effectively. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/TCZTK7/ WG802 Taichi Furuhashi PUBLISH BELNJN@@talks.osgeo.org -BELNJN Manage your fieldwork directly from your app with QFieldCloud API integration en en 20251119T153000 20251119T155500 0.02500 Manage your fieldwork directly from your app with QFieldCloud API integration QField helps people map and understand the world, QFieldCloud helps people automate their data collection and fieldwork. If your organization already has an application that manages processes and workflows, or you simply want to run a nightly cron to automate processing on your data, the QFieldCloud RESTful API will help you do that. We will look at some of the newest QFieldCloud features and how we can access them via QFieldCloud CLI, QFieldCloud SDK or simply by running a HTTP request against QFieldCloud API endpoint. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/BELNJN/ WG802 Ivan Ivanov PUBLISH KBYFFX@@talks.osgeo.org -KBYFFX (Re)Making Cirrus: Five Years Building a Data Orchestration Framework en en 20251119T160000 20251119T162500 0.02500 (Re)Making Cirrus: Five Years Building a Data Orchestration Framework Cirrus is an open source, cloud-native framework for orchestrating geospatial data pipelines built using the concept of STAC (SpatioTemporal Asset Catalog) workflows. It provides a flexible and modular approach to deploying and managing serverless pipelines in AWS via python components and a Terraform-based deployment mechanism. Cirrus enables scalable, repeatable data processing workflows in the cloud, and is designed to help teams transform, validate, and catalog geospatial data in STAC-compliant formats at scales both large and small. Over the past five years, cirrus has evolved from a directory of loosely-organized bits of configuration and components built on top of the Serverless Framework to a robust, open source cloud-native data pipeline management system. In this talk, I’ll share my journey maintaining and evolving cirrus from what I inherited to its current state, and lessons I’ve learned along the way. Together we’ll explore cirrus’ origins and the original architecture and its challenges. We’ll examine the decision to shift away from duplicating deployment code via the configuration merge system of the first cirrus CLI, and what benefits and pitfalls that brought along with it. We’ll trace some of the tooling and ideas that spun out along the way, like stac-task and swoop. Finally, we’ll look at the version 1.0 release’s move away from Serverless Framework and the decoupling of the deployment logic from the codebase, the new cirrus Terraform module, and how this 1.0 release has prompted the reconsideration of what actually constitutes cirrus now. Whether you're maintaining your own internal tooling, building cloud-native data processing pipelines, or just trying to keep an open source project healthy through shifting technical landscapes, this talk will offer practical insights drawn from real-world experience. We'll cover the technical decisions, tradeoffs, and lessons learned—especially those relevant to anyone maintaining cloud-native tooling in a fast-moving landscape. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/KBYFFX/ WG802 Jarrett Keifer PUBLISH WPLVJQ@@talks.osgeo.org -WPLVJQ GeoAI Transformer–LSTM Boosts Maize-Yield Accuracy in Malawi’s Smallholder Fields en en 20251119T163000 20251119T165500 0.02500 GeoAI Transformer–LSTM Boosts Maize-Yield Accuracy in Malawi’s Smallholder Fields 1. Introduction Maize supplies almost 60 % of Malawi’s caloric intake, so mid-season yield forecasts are pivotal for food-security planning (FAO, 2015). Conventional ground surveys reach farmers only after harvest and sample < 1 % of the 1.8 million smallholdings. Optical earth-observation offers plot-scale coverage, and deep learning is now outperforming index-based regressions (Muruganantham et al., 2022). Transformers have recently eclipsed CNNs in US Corn-Belt studies (Lin et al., 2023), yet their benefit for densely inter-cropped African fields is unknown. We therefore benchmark five modelling paradigms, ranging from linear regression to a novel Vision-Transformer–LSTM (ViT-LSTM) hybrid, on a hand-harvested dataset from Zomba District. 2. Materials and Methods Eight rain-fed maize fields (0.2 – 0.9 ha) in Zomba District were GPS-delineated during the 2024/25 season. Grain harvested from ten 10 m × 10 m quadrats per field was dried to 13 % moisture and weighed, yielding plot-level reference values. Sentinel-2 Level-2A surface reflectance (13 bands) was accessed via Google Earth Engine (GEE). Scenes acquired between 1 December 2024 and 28 February 2025, vegetative tasselling to first silking (VT–R1), were cloud-masked with s2cloudless and aggregated into rolling 10-day medians. All bands (native 10 m / 20 m) were re-projected to EPSG:32736 (UTM 36 S) and bilinearly up-sampled to 1 m to produce inputs compatible with CNN and ViT backbones pre-trained on large-dimension Sentinel-2 imagery (e.g., BigEarthNet). For each field, images were tiled using a 32 × 32 px sliding window with a 16 px stride, augmenting the sample count. Five spectral indices, NDVI, EVI, red-edge NDVI, NDWI, and MSI, were computed from the 1m bands so that every raster layer shared the same grid. 2.1. Models * LR-Indices: ordinary-least-squares regression implemented with PyTorch BSD-licensed Linear layer, applied to 10-day means of the five indices and raw bands. * XGBoost: gradient-boosted decision trees using the Apache-2.0 XGBoost library on aggregated bands + indices, fully open source and cross-platform. * CNN-LSTM: frozen ResNet-101 encoder pretrained on the open BigEarthNet v2.0 Sentinel-2 archive, released under the CDLA-Permissive licence; its patch embeddings and mean indices feed a three-layer LSTM, all implemented in PyTorch (BSD licence). * Frozen ViT: ViT-B16 encoder pretrained on the same BigEarthNet weights, kept frozen; token sequence plus mean indices pass to a linear head; codebase remains entirely PyTorch and open source. * ViT-LSTM (proposed): shares the open-source ViT encoder above, but pools tokens and mean indices with an LSTM decoder; the full pipeline (data and code) is published under GPL-3.0 in our repository. All models were implemented in PyTorch 2.3 and trained on an NVIDIA Quadro P1000 GPU. Hyper-parameters were optimised with Optuna. Deep networks trained for 50 epochs using AdamW, cosine-annealed learning rates, batch = 1, FP16 mixed precision, and early-stopping (patience = 10). A leave-one-field-out (LOFO) scheme ensured each field served once as the unseen test set. Performance was assessed with RMSE and MAE. Exact paired-permutation tests compared fold-wise RMSEs, and average inference time per tile was computed for every fold. All code, configuration files, and anonymised data are released under GPL-3.0 at https://github.com/jahnical/yield-pred-models-comp, enabling full replication of the workflow. 3. Results * Accuracy: ViT-LSTM achieved the lowest cross-validated RMSE 0.022 t ha⁻¹ and MAE 0.019 t ha⁻¹. CNN-LSTM followed at RMSE 0.088 t ha⁻¹; frozen ViT, 0.219 t ha⁻¹. XGBoost and LR-Indices exceeded 0.22 t ha⁻¹. * Significance: Both recurrent models (CNN-LSTM and ViT-LSTM) significantly out-performed non-recurrent baselines (p ≤ 0.02). The gap between ViT-LSTM and CNN-LSTM was also significant (p = 0.046). * Speed: LR-Indices and XGBoost predicted in < 0.02 ms per tile. CNN-LSTM needed 14 ms, whereas ViT-LSTM required 36 ms, 2.5 times slower. 4. Discussion Explicit spatio-temporal learning is critical because Malawi’s smallholder plots are tiny, irregular and often inter-cropped; spectral signatures therefore vary sharply over just a few metres and change quickly as plants develop. Recurrent layers already capture the crop’s phenological curve, but the self-attention blocks in the transformer let the model weigh non-contiguous pixels and dates, teasing out subtle edge effects and mixed-crop patterns that a CNN-LSTM misses (Liu et. el, 2023). That extra context cuts RMSE by ≈ 0.07 t ha⁻¹ (about 60 %), yet self-attention is quadratic in sequence length, so inference jumps from 14 ms to 36 ms per 32 by 32 px 1 m-tile, a 2.5× latency cost. Data is streamed through Google Earth Engine, a free (though not open-source) cloud platform, while QGIS for vector editing, Rasterio/xarray for raster I/O, and PyTorch/XGBoost for modelling are fully open-source. This workflow demonstrates how combining free cloud access with FOSS4G tools can deliver high-resolution, scalable yield mapping in resource-constrained settings. 5. Conclusion We present the first open-source, plot-scale benchmark that pits classical machine-learning models, CNN-LSTM, and a transformer–recurrent hybrid on Malawian maize yields. ViT-LSTM attains state-of-the-art accuracy (RMSE 0.022 t ha⁻¹), an 60 % improvement over CNN-LSTM, at a four-fold latency cost. All code and data are freely released, inviting the FOSS4G community to replicate, critique, and extend the workflow to other crops, sensors, and regions. References (abridged) 1. Chen T & Guestrin C (2016) XGBoost: A scalable tree-boosting system. KDD. Dosovitskiy A et al. (2021) An image is worth 16×16 words: Transformers for image recognition at scale. ICLR. 2. FAO (2015) National Investment Profile: Water for Agriculture and Energy – Malawi. 3. Gaddy D, Li K & Eisner J (2022) Exact paired-permutation testing for structured test statistics. NAACL-HLT. 4. Krause J, Smith L & Brown M (2019) A CNN-RNN framework for crop-yield prediction. Frontiers in Plant Science 10:1750. 5. Lin F et al. (2023) MMST-ViT: Climate-change-aware crop-yield prediction via multi-modal spatial-temporal Vision Transformer. ICCV. 6. Muruganantham P et al. (2022) Systematic review on crop-yield prediction with deep learning and remote sensing. Remote Sensing 14(9):1990. 7. Zupanc A et al. (2023) s2cloudless: An open-source cloud mask for Sentinel-2. Earth Science Informatics 16:399–415. 8. Li, C., Chimimba, E. G., Kambombe, O., Brown, L. A., Chibarabada, T. P., Lu, Y., … Dash, J. (2022). Maize yield estimation in intercropped smallholder fields using satellite data in Southern Malawi. Remote Sensing, 14(10), 2458. 9. Sumbul, G., Utilkar, Y., Kaplan, S., & Demir, B. (2021). BigEarthNet-MM: A large-scale multi-modal benchmark archive for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 182, 49-62. 10. Liu, F., Jiang, X., & Wu, Z. (2023). Attention Mechanism-Combined LSTM for Grain Yield Prediction in China Using Multi-Source Satellite Imagery. Sustainability, 15(12), 9210. https://doi.org/10.3390/su15129210 PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/WPLVJQ/ WG802 Kondwani Munthali Mathews Jere PUBLISH 3W8Y9W@@talks.osgeo.org -3W8Y9W Registration en en 20251120T080000 20251120T170000 9.00000 Registration FOSS4G 2025 Auckland Conference Registration PUBLIC CONFIRMED Registration https://talks.osgeo.org/foss4g-2025/talk/3W8Y9W/ WG306 Foyer PUBLISH GR8LGK@@talks.osgeo.org -GR8LGK Geochicas en en 20251120T180000 20251120T220000 4.00000 Geochicas GeoChicas is a group of women who do mapping in OpenStreetMap and work to close the gender gap in the OpenStreetMap community. Event will be held at RocketMan, 8 Roukai Lane, Auckland Central PUBLIC CONFIRMED Social Event https://talks.osgeo.org/foss4g-2025/talk/GR8LGK/ RocketMan PUBLISH TAQKBE@@talks.osgeo.org -TAQKBE Women in Geospatial Breakfast en en 20251120T070000 20251120T090000 2.00000 Women in Geospatial Breakfast Women in Geospatial Breakfast Event. 7 am at Amano, 66 - 68, Tyler Street, Britomart Place, Auckland PUBLIC CONFIRMED Social Event https://talks.osgeo.org/foss4g-2025/talk/TAQKBE/ Amano PUBLISH WDUCCK@@talks.osgeo.org -WDUCCK Morning Break en en 20251120T103000 20251120T110000 0.03000 Morning Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Morning Tea https://talks.osgeo.org/foss4g-2025/talk/WDUCCK/ WG201 Exhibition & Catering PUBLISH MFNPZ8@@talks.osgeo.org -MFNPZ8 Lunch Break en en 20251120T123000 20251120T133000 1.00000 Lunch Break This is the time where you can have a 60min break to recharge with Tea, Coffee, Juice, Water and Food as well as visit our exhibitors and sponsors. PUBLIC CONFIRMED Lunch https://talks.osgeo.org/foss4g-2025/talk/MFNPZ8/ WG201 Exhibition & Catering PUBLISH 9S9U8F@@talks.osgeo.org -9S9U8F Afternoon Break en en 20251120T150000 20251120T153000 0.03000 Afternoon Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Afternoon Tea https://talks.osgeo.org/foss4g-2025/talk/9S9U8F/ WG201 Exhibition & Catering PUBLISH G79YEK@@talks.osgeo.org -G79YEK Evaluating LLMs as Intermediaries for FOSS4G CLI-based Geospatial Analysis en en 20251120T090500 20251120T091000 0.00500 Evaluating LLMs as Intermediaries for FOSS4G CLI-based Geospatial Analysis The complexity of CLI-based geospatial analysis tools presents a significant barrier to widespread adoption of FOSS4G technologies. While tools like GDAL, PDAL, and Python geospatial libraries offer powerful capabilities, their command-line interfaces require substantial technical expertise. This limits effective utilization to technical specialists, despite FOSS4G's promise of democratizing geospatial analysis. Recent advances in Large Language Models (LLMs) suggest potential for bridging this technical gap. LLMs could theoretically interpret natural language requests and generate appropriate CLI commands, making these tools accessible to domain experts who possess valuable geospatial knowledge but lack programming backgrounds. However, effective geospatial analysis requires understanding of domain-specific concepts such as coordinate reference systems, data formats, and regional standards. Our preliminary investigations reveal that current LLMs often fail to correctly handle country-specific geospatial information. This presentation evaluates whether existing LLMs possess sufficient geospatial domain knowledge to serve as reliable intermediaries. We examine their performance and evaluate specific knowledge gaps that prevent LLMs from effectively facilitating FOSS4G CLI tool usage for non-technical users. Acknowledgment This work was supported by JSPS KAKENHI Grant Number JP25K21880, 24K16071. This research was performed by the Environment Research and Technology Development Fund (JPMEERF25S12421) of the Environmental Restoration and Conservation Agency provided by Ministry of the Environment of Japan. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/G79YEK/ WG403 Nobusuke Iwasaki Ayaka Onohara PUBLISH FDHJDH@@talks.osgeo.org -FDHJDH "Osmia" - A quick Openlayers project for OSM data verification en en 20251120T091000 20251120T091500 0.00500 "Osmia" - A quick Openlayers project for OSM data verification Quality control tools greatly benefit map maintainers. The purpose of this tool is to find map features, in this case roads or paths, that have not been updated in a long time. "Osmia" is a tool primarily to help mappers perform quality control analysis of OpenStreetMap data, and add missing data where it is required. Osmia's Obsolete Road Geometry function colours road lines and the line vertices increasingly red if they have not been edited recently. This highlights data that needs reviewing; due to improvements in the quality of aerial photography recently, the theory is that old data could be quite inaccurate. As far as I know, despite all the quality assurance tools available for mappers, none focus specifically on the temporality or recency of the data, so this is fairly novel. Additionally, there is a function that finds roads with no surface tags. Previously, the only way to achieve this kind of data visualisation was through manually downloading and styling the vector data in QGIS. This tool fully automates this task in a computationally light, browser-based workflow. Osmia hinges on the Nominatim and Overpass APIs to retrieve data easily. Openlayers is used for the slippy map and vector data styling. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/FDHJDH/ WG403 Peter LeGras PUBLISH FYMNUV@@talks.osgeo.org -FYMNUV A GIS-Based Study on the Development of a Blue Carbon Database and Digital Mapping System en en 20251120T091500 20251120T092000 0.00500 A GIS-Based Study on the Development of a Blue Carbon Database and Digital Mapping System Coastal ecosystems such as tidal flats, salt marshes, and seagrass meadows are referred to as “Blue Carbon” ecosystems. Although they occupy smaller areas than terrestrial forests, they can absorb and store carbon up to 50 times faster. Recognizing this value, this study established a Blue Carbon database and developed a GIS-based digital information map system for the Korean coast. The system serves as a GIS-based decision-support platform for the effective management of marine carbon sinks and for supporting international certification. Blue Carbon ecosystems such as salt marshes, seagrasses, tidal flats, and sediments have attracted global attention for their remarkable ability to absorb and store carbon at much faster rates than terrestrial forests, yet examples of their integration into systematic management frameworks have been limited. To address this, between 2022 and 2024, data from 289 survey points along the Korean coastline were collected and integrated. The collected data included carbon cycling processes in salt marshes, biomass distribution of seagrasses and seaweeds, organic carbon content of intertidal and sublittoral sediments, and carbon storage in shellfish habitats. These diverse datasets were unified into a Blue Carbon database, enabling reliable estimation of carbon storage, time-series monitoring, and GIS-based visualization. A distinctive methodological feature of this study was the combination of remote sensing and ground verification to enhance data reliability. For example, salt marsh habitats were initially detected through aerial RGB imagery, divided into 5 km grids, and the top 20% of grids with the largest habitat areas were selected for detailed field surveys. This approach reinforced remotely sensed results with species-level ground truthing, providing a robust data foundation applicable to both research and policy. Based on this dataset, the GIS-based digital information map provides decision-support functions beyond simple visualization. Users can query layers related to vegetation, sediments, and new carbon sinks, analyze data by administrative units or standardized grids, and perform spatial analyses within user-defined areas. In addition, a Site Suitability Index (SSI) was designed to evaluate optimal sites for establishing carbon-absorbing coasts in Korea by weighting environmental variables such as wave energy, slope, depth, and tidal range. Through these functions, policymakers, researchers, and stakeholders can effectively identify priority areas for conservation, restoration, and sustainable coastal development. The platform also expands accessibility and international cooperation through multilingual support in Korean and English, and ensures transparency by including reliability metadata that indicates whether each dataset was derived from remote sensing or validated through field surveys. This improves scientific credibility and supports the future international certification of tidal flat Blue Carbon under IPCC frameworks. Ultimately, the Blue Carbon Digital Information Map provides an integrated and user-friendly environment that connects ecological science with geospatial technology. It contributes to achieving Korea’s 2050 carbon-negative strategy, conserving coastal ecosystems, and demonstrating the potential of open-source GIS as a global climate solution. By combining innovation, transparency, and accessibility, this study serves not only as a practical management tool but also as an international cooperation model for the sustainable use of Blue Carbon resources. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/FYMNUV/ WG403 Yeonjin Kim PUBLISH DACAAJ@@talks.osgeo.org -DACAAJ Development of Core Technologies for a Metaverse-Based Training Engine in a Scientific Training Environment Platform en en 20251120T092000 20251120T092500 0.00500 Development of Core Technologies for a Metaverse-Based Training Engine in a Scientific Training Environment Platform Development of Core Technologies for a Metaverse-Based Training Engine in a Scientific Training Environment Platform Joohyuk Park, Kwangin Han, Hyeongi Min, Sanghak Kim, Geunha Kim, Chaeyoun Lee Sundosoft Co., Ltd. 1. Introduction Traditional 2D geospatial systems present significant limitations in the context of real-time training, simulation, and decision-making. With increasing needs for immersive and spatially rich environments in areas such as disaster response, defense training, and urban planning, metaverse-based platforms leveraging high-resolution 3D data have become essential. This paper introduces a core technology platform developed to support metaverse-based scientific training environments. It integrates multi-source 3D spatial data, interactive modeling tools, and web-based visualization interfaces, creating an advanced simulation engine for scenario planning, operational training, and geospatial analysis. 2. Objectives The primary objectives of this project are: To overcome the limitations of flat 2D mapping by enabling realistic and immersive 3D simulations. To support high-resolution modeling of terrains, buildings, roads, and operational assets. To provide web-based access to a 3D simulation environment for use in policy-making, training, and public services. To integrate spatial data into metaverse engines for enhanced collaboration and planning. To promote public–private–academic collaboration through scalable, shareable data and open APIs. 3. Data Sources and Processing A variety of spatial data sources were employed to construct the 3D models, including: LiDAR point cloud data: Captured via drone or aerial platforms to model surface elevation and object geometry. Orthophotos and aerial imagery: Used for texture generation and spatial referencing. Vector map data: Used for defining roads, building footprints, and infrastructure. Custom field survey data: To fill gaps in public datasets and validate terrain accuracy. These datasets were fused using a hybrid data processing pipeline. The result was a seamless digital terrain model (DTM) and digital surface model (DSM), which served as the foundation for object and unit placement within the metaverse environment. 4. System Components The system comprises five primary components: 4.1 3D Terrain/Object Editing Module This module allows users to generate and modify terrain and object layouts. Features include: Topographic surface editing (elevation, slope adjustment) Road/pathway creation using spline tools Object positioning and orientation External model import/export using CSV format 4.2 Unit and Scenario Modeling A central feature of the platform is its ability to simulate operational units (e.g., armored vehicles, troops) and define scenario-based behaviors: Each unit can be assigned movement paths, states, and display attributes. Painting schemes or external features of vehicles can be modified interactively. Training sequences can be saved and replayed for review or evaluation. 4.3 Web-Based 3D Viewer and API A web-based 3D viewer was developed using CesiumJS and WebGL technologies. It allows remote users to: Visualize terrain and unit deployment in real-time Interact with editable 3D objects via browser Access scenario data through RESTful APIs for integration with third-party platforms 4.4 Metaverse Operational Tool (CSCI) The “Metaverse Integrated Operational Tool,” codenamed CSCI, acts as the main interface for simulation control: Displays the operational situation in a real-time 3D environment Provides an overview of unit lists, terrain zones, and scripted events Enables data exchange with external systems through API gateways Supports planning, monitoring, and post-exercise analysis 4.5 UI/UX Design The user interface is designed to balance complexity and usability. Major UI features include: Top Toolbar: For simulation playback, camera control, and object visibility Side Panels: To manage lists of operational units and active objects 3D Display Area: Central workspace for real-time spatial interaction External System UI: Supports access to other spatial or simulation databases 5. Application Scenarios The platform is intended for cross-domain deployment. The following use cases demonstrate its versatility: Military Training: Realistic simulation of urban combat or tactical missions using real-world terrain and movable units. Disaster Preparedness: Planning emergency responses for earthquakes, floods, and wildfires with modeled risk zones and evacuation routes. Urban Planning: Simulation of infrastructure expansion, zoning changes, or traffic flow optimization. Environmental Education: Interactive 3D modules for terrain analysis, land cover study, and climate change impact visualization. Each scenario benefits from customizable data layers, editable units, and immersive interaction, enhancing training fidelity and decision-making accuracy. 6. Results and Evaluation Initial testing demonstrated that the platform can handle large-area terrain datasets (>50 km²) and render them interactively within web browsers. The real-time responsiveness and modularity of the simulation engine were validated through internal scenario drills. User feedback from early adopters in the disaster response and defense sectors highlighted: Improved situational awareness through 3D visualization Enhanced engagement and learning during training exercises Flexibility in configuring and deploying custom scenarios Strong potential for interoperability with digital twin systems 7. Discussion and Challenges While the platform demonstrates high performance and flexibility, several challenges remain: Data Standardization: Integrating heterogeneous spatial datasets from multiple agencies requires harmonization and metadata control. Real-Time Scalability: Future deployments will require support for concurrent users and larger datasets, demanding optimization of rendering pipelines and network infrastructure. AI Integration: The system would benefit from AI-based decision support, such as automated unit behavior or hazard prediction based on real-time data feeds. Interoperability: Continued effort is needed to align with standards like CityGML, 3D Tiles, and OGC WFS/WMS. 8. Conclusion and Future Work This study introduced a scalable, browser-accessible platform that combines high-precision geospatial data with metaverse-based simulation tools. It enables immersive, flexible training and planning across diverse domains. Future plans include: Expansion of supported data formats and 3D model libraries Integration with live sensor feeds and AI-powered analytics Deployment in public safety agencies and educational institutions Full support for VR/AR hardware to enhance immersion By providing an open, extensible framework, this platform contributes to the growing ecosystem of geospatial metaverse applications, supporting smarter, more informed decision-making in both public and private sectors. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/DACAAJ/ WG403 Hyeongi MIN PUBLISH JECL7Z@@talks.osgeo.org -JECL7Z A Year as a Baby OSGeo Local Chapter: OSGeo Nepal en en 20251120T092500 20251120T093000 0.00500 A Year as a Baby OSGeo Local Chapter: OSGeo Nepal OSGeo Nepal officially became a local chapter of the Open Source Geospatial Foundation (OSGeo) in 2024. This lightning talk will highlight how even a young chapter can make a meaningful impact within a year through grassroots organizing, student engagement, and open collaboration. OSGeo Nepal is structured into four active Discord-based working groups: Engineering, Data Access, Training & Knowledge Sharing, and Design & Communication. These groups are open spaces where members get to support a wide range of activities like helping each other solve problems, share useful resources, collaborate on tools and datasets, organise events, opportunity sharing, create training materials, and contribute to ongoing projects. It’s a place where beginners can learn from seniors, and seniors can mentor the new contributors in the open-source geospatial space. Regular meetups are held on the first Friday of every month, offering a welcoming space for connection, discussion, learning, and community updates. The chapter has also led community-driven events such as Coding Parties, GIS Day celebrations and Knowledge Sharing Sessions. Throughout the year, OSGeo Nepal has supported and collaborated with student organizations such as GESAN, GES, and KUCC in events like the OSM Hackfest WRC-2024, Geospatial Meet, NEPGEOM-2024, WebGIS Training, IT Meet-2024, Geo-Talk and Map Design Competition. Chapter members have contributed as mentors, trainers, speakers, judges, and volunteers in such events. This lightning talk will also highlight the expansion of collaboration to professional bodies like NGES through programs like GeoSeries- an online platform that brings together experts and learners to explore applications of geospatial technologies. With student representatives from four institutes and a strong volunteer culture, OSGeo Nepal serves as a bridge between the national and global open geospatial communities. Anyone with a question or idea can drop it in the Discord and someone will always be ready to support. Along the way, the chapter has encountered challenges common to emerging communities. Despite these challenges, the commitment of members has remained really strong. Looking ahead, OSGeo Nepal aims to reach more people from underrepresented communities, develop educational content in both English and local languages to improve accessibility, and strengthen collaboration with global OSGeo projects and contributors. This talk aims to inspire new chapters, demonstrate the power of grassroots organizing, and focuses on how even a “baby chapter” can make a meaningful impact within a year. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/JECL7Z/ WG403 Aayush Chand PUBLISH CRKRSF@@talks.osgeo.org -CRKRSF The Open Data Cube is Dead, Long Live the Open Data Cube en en 20251120T093000 20251120T093500 0.00500 The Open Data Cube is Dead, Long Live the Open Data Cube For years, the Open Data Cube has been the go-to framework for managing massive collections of satellite imagery. But with changing cloud-native practices, new open standards, and better APIs, there’s a simpler way forward. In this talk, Alex Leith, Executive Director at Auspatious, explains how the heavy Postgres-driven ODC tool is being replaced by flexible, open STAC APIs and elegant Python tools like pystac-client and odc-stac. The result? A leaner, easier way to discover and analyze EO data, with just a few lines of code. Long live the Open Data Cube. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/CRKRSF/ WG403 Alex Leith PUBLISH CPHNTZ@@talks.osgeo.org -CPHNTZ Fast, Free, and (Mostly) Painless: Getting Started with Open-Source Web Mapping en en 20251120T093500 20251120T094000 0.00500 Fast, Free, and (Mostly) Painless: Getting Started with Open-Source Web Mapping Open-source web mapping libraries have opened up a world of possibilities for building fast, flexible, and modern geospatial applications without being tied to proprietary platforms. In this talk, we’ll dive into three of the most widely used open-source mapping libraries—Leaflet, OpenLayers, and MapLibre—comparing their performance, developer experience, documentation, and suitability for different types of applications. Drawing from real-world projects, I’ll share why MapLibre was chosen for a recent road safety tool, how OpenLayers offers a great developer experience, and where Leaflet’s simplicity starts to struggle in modern development workflows. We’ll cover practical examples to help you get started quickly, as well as also touch on how these libraries can be integrated with a range of spatial data services. If you’re building interactive maps, experimenting with spatial data, or just looking to visualize some locations on a map, this session will help you choose the right tool for the job and hit the ground running. Code samples will be provided via GitHub. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/CPHNTZ/ WG403 Luke Sussex PUBLISH C3PYUV@@talks.osgeo.org -C3PYUV Interactive Simulation for Visualizing Bus Locations Using GTFS Data en en 20251120T094000 20251120T094500 0.00500 Interactive Simulation for Visualizing Bus Locations Using GTFS Data This proposal utilizes the FOSS4G toolset to encourage bus ridership and enhance the sustainability of urban transportation systems. It specifically showcases an interactive simulation that employs General Transit Feed Specification (GTFS) data to dynamically represent bus positions on a map, corresponding to specific dates and times. This system is ingeniously crafted with FOSS4G tools to enable straightforward tracking of buses' current and planned routes. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/C3PYUV/ WG403 Kei Yamazaki PUBLISH BYCVNH@@talks.osgeo.org -BYCVNH Finding the Farthest Point: Implementing Longest Path Analysis in QGIS with NetworkX and Python en en 20251120T094500 20251120T095000 0.00500 Finding the Farthest Point: Implementing Longest Path Analysis in QGIS with NetworkX and Python This presentation demonstrates advanced network analysis methods using Python and NetworkX within QGIS to calculate the farthest reachable point from any starting location. Users can transform road network data into graph networks and visualize complex routing paths across geographic regions. This approach makes advanced network analysis accessible to GIS practitioners without requiring specialized graph theory knowledge. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/BYCVNH/ WG403 Xinmiao Qu PUBLISH B8GNQU@@talks.osgeo.org -B8GNQU Hitchhiker's Guide to LINZ Open Data en en 20251120T095000 20251120T095500 0.00500 Hitchhiker's Guide to LINZ Open Data A whirlwind tour across LINZ's data landscape. From Topo maps and Navigation Charts, to Property and Elevation. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/B8GNQU/ WG403 Tobias Schmidt PUBLISH AHCRNT@@talks.osgeo.org -AHCRNT Outside Track: A Rapid Round-up of Unrepresented Open Source Geospatial Projects en en 20251120T095500 20251120T100000 0.00500 Outside Track: A Rapid Round-up of Unrepresented Open Source Geospatial Projects FOSS4G is a space where collaboration and innovation converge. But with so many incredible projects, many of them volunteer run, not every maintainer can make it to our global event. In this talk, I’ll take you on a fast-paced, tour of some of the most interesting, impactful, and lesser-known open source geospatial projects that haven’t otherwise found representation at FOSS4G 2025. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/AHCRNT/ WG403 Hamish Campbell PUBLISH UJRQVC@@talks.osgeo.org -UJRQVC Let’s solve the problem of object storage en en 20251120T100000 20251120T100500 0.00500 Let’s solve the problem of object storage Object storage transformed data at planetary scale, giving us cheap, durable, elastic storage. But in geospatial, it also turned chunking into everyone’s problem. Misaligned queries waste time, compute, and money, while producers struggle to optimize datasets for all users. This talk reframes our problems with chunking as a limitation of the object model itself. We’ll look at why object storage makes chunking visible, what that means for usability and efficiency, and how the [Coalesced Chunk Retrieval Protocol (CCRP)](https://ccrp.dev) could restore transparent, efficient access — this time at cloud scale. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/UJRQVC/ WG403 Jarrett Keifer PUBLISH WSLKQD@@talks.osgeo.org -WSLKQD Building Open Geospatial Communities in Nepal: The Role of OSGeo Nepal in Open Data and Youth Empowerment en en 20251120T100500 20251120T101000 0.00500 Building Open Geospatial Communities in Nepal: The Role of OSGeo Nepal in Open Data and Youth Empowerment Open geospatial data is reshaping how communities understand and manage their environments, and in Nepal, OSGeo Nepal is leading this change by empowering youth to engage meaningfully with open geospatial tools and data. Since its formal recognition in 2023, OSGeo Nepal has grown into a vibrant, youth-led network of over 150 active members committed to fostering a culture of openness, collaboration, and data accessibility. The community hosts monthly meetups, which serve as collaborative spaces where participants share opportunities, discuss open data practices, and build their capacity to contribute to platforms such as OpenStreetMap, HDX, and QGIS. Senior members actively mentor newcomers, guiding them on how to start their open-source journey and emphasizing the value of contributing to the global open geospatial community. Outreach has been a cornerstone of this movement. OSGeo Nepal has presented at several leading universities, including Tribhuvan University and Kathmandu University, to raise awareness about OSGeo Nepal, open-source geospatial tools, and the importance of accessible data. One of the major programs conducted in collaboration with these university groups is training on QGIS, OSM Hackfest 2024, geo-talk series, and hackathons. Student representatives have been appointed across campuses to sustain engagement and act as catalysts for local geospatial initiatives. Several skill-building sessions and trainings have been conducted on impactful topics like GeoAI, OpenEO, and the application of LLMs in GIS. These events not only provide technical skills but also inspire participants to view open data as a tool for solving real-world challenges. Collaborations with national student groups like NGES and GESAN have helped amplify these efforts. A major milestone has been the launch of OSGeo Nepal’s organizational presence on the Humanitarian Data Exchange (HDX), where volunteers are curating and publishing standardized datasets—such as road networks and administrative boundaries—for public use. This involvement offers young contributors direct experience in the data lifecycle, from acquisition and cleaning to metadata creation and public release. As impactful as the journey has been, OSGeo Nepal is only getting started. In the coming months, the community aims to expand its reach, conducting training programs on open-source software and data stewardship. One of our goals is to build a centralized, accessible data platform where anyone from students to policymakers can discover high-quality geospatial datasets for analysis and application. Further, OSGeo Nepal plans to deepen its collaborations with both national and international organizations, strengthening Nepal’s role in the global open geospatial movement. Through these next steps, OSGeo Nepal envisions a more informed, engaged, and empowered youth community—one that doesn’t just use open data but becomes a driving force in creating, managing, and sharing it for the public good. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/WSLKQD/ WG403 Aayush Chand Dibikshya Shrestha PUBLISH R3DEV3@@talks.osgeo.org -R3DEV3 Mapping Ink: Using Open Source GIS Data to Tattoo a River en en 20251120T101000 20251120T101500 0.00500 Mapping Ink: Using Open Source GIS Data to Tattoo a River Using LINZ Open Data and a few QGIS geoprocessing tools, I was able to extract various river lines for a friend to then pass on to his tattoo artist to add his flare. It was a fun way to use GIS for something personal—turning open data into a design that tells a story. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/R3DEV3/ WG403 Phil Greville PUBLISH CYL8GX@@talks.osgeo.org -CYL8GX Tree shadow modelling in QGIS + GRASS en en 20251120T101500 20251120T102000 0.00500 Tree shadow modelling in QGIS + GRASS Using QGIS, GRASS and Python along with LINZ Dataservice to calculate shadow impact of trees 50 years in the future, Tim from New Zealand Carbon Farming briefly outlines their approach to maintain compliance with NZ National Environmental Standards for commercial forestry: What we tried; what worked and what didn't. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/CYL8GX/ WG403 Tim Barnes PUBLISH SKWBT8@@talks.osgeo.org -SKWBT8 Digital Earth Machine Learning Operations en en 20251120T102000 20251120T102500 0.00500 Digital Earth Machine Learning Operations Digital Earth (DE) is implementing a Machine Learning Operations proof-of-concept for an automated system designed to support the end-to-end development, training, and release of complex machine learning workflows and models for satellite imagery, such as artificial surface detection used in Land Cover. The PoC focuses on the discoverability of open-source machine learning models, streamlining data versioning, feature transformation, containerised model training, hyperparameter tuning, governance, model, and artefact management. The lightening talk will share our architecture and learnings, with a focus on the integration of various open-source components and specifications with cloud-services. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/SKWBT8/ WG403 James Miller PUBLISH 8VHAU8@@talks.osgeo.org -8VHAU8 Introduction to OSGeo and ideas for the future en en 20251120T110000 20251120T112500 0.02500 Introduction to OSGeo and ideas for the future The Open Source Geospatial Foundation is a not-for-profit organization whose mission is to foster global adoption of open geospatial technology. It is the formal host of the FOSS4G global conferences like the Auckland 2025 conference. It also acts as an umbrella organization for a large stack of geospatial software applications and libraries. This talk will introduce what OSGeo is and does. What are the challenges faced and how can we future proof the foundation? PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/8VHAU8/ WG403 Jeroen Ticheler PUBLISH RM9RHS@@talks.osgeo.org -RM9RHS Navara map engine: The Next-Generation Flexible Map Engine for Advanced GIS Visualization. en en 20251120T113000 20251120T115500 0.02500 Navara map engine: The Next-Generation Flexible Map Engine for Advanced GIS Visualization. Traditional map engines often struggle to (1) expand their visualization capabilities and (2) prevent interactive logic from becoming overly complicated and slow. **Navara**, under development since FY 2024, breaks this mold by *fully decoupling the GIS engine from the rendering engine*. Complex GIS operations—coordinate transformations, LOD management, ellipsoid computations—run in a Rust/WASM headless core, while any renderer (e.g., Three.js) handles drawing. During the current fiscal year we implemented: - **Visual API** – realistic atmosphere, clouds, night-time lighting, and other photoreal effects. - **GIS API** – coordinate conversion, ellipsoid geometry, and other GIS-specific computations. In this talk we will dive into these APIs, share architectural insights, and demonstrate the kinds of applications Navara is built to power. ### Intended Audience - 3D-engine and real-time graphics developers - Researchers, municipalities, and enterprises working with GIS data - Creators who want to combine photoreal rendering with rich data visualization ### Takeaways - Design patterns for **headless GIS + rendering-engine** architectures - Examples of photorealistic visual effects achievable with Navara - How the **GIS API** enables advanced, high-speed interactions PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/RM9RHS/ WG403 Keiya Sasaki PUBLISH Z3JQDZ@@talks.osgeo.org -Z3JQDZ Cut the clutter - clean and clear cartography en en 20251120T120000 20251120T122500 0.02500 Cut the clutter - clean and clear cartography People love to throw everything including the kitchen sink onto their maps mistakenly thinking more information is better. This leads to a mental burden, making the map harder to read and putting some readers off entirely. The talk was given at FOSS4G Oceania in 2023 and was inspired by the work of Edward Tufte and how my approach to cartography has changed over the years. In this talk, I will cover designing maps which are clear, uncluttered and support the viewer. I discuss how links to other concepts, such as visual hierarchy and layout, the history of thinking about clutter with Tufte and Cairo, cover the idea of the elegant and the invisible, as well as how to keep your viewer foremost in your mind. This talk will be more focused on cartography theory rather than practice with open source tools, however it willl give examples using QGIS. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/Z3JQDZ/ WG403 Peter King PUBLISH CJF3BD@@talks.osgeo.org -CJF3BD Re:Earth Flow (Alpha): Your Spatial ETL Workspace — In the Browser, Together en en 20251120T133000 20251120T135500 0.02500 Re:Earth Flow (Alpha): Your Spatial ETL Workspace — In the Browser, Together Discover **Re:Earth Flow**, a new open-source platform for building geospatial data workflows — visually, collaboratively, and entirely in your browser. Whether you’re cleaning municipal datasets or transforming large geospatial files, Re:Earth Flow lets you connect and configure transformation steps like building blocks, no code required. In this talk, we’ll introduce the tool (currently in alpha), explain its technical foundation, and show how it brings a familiar ETL model to the web — powered by a Rust engine, Go APIs, and real-time collaboration via WebSockets and Yjs. ### **What to Expect:** - **Why We Built It**: Friction points with desktop tools and the case for a browser-native alternative - **How It Works**: A look at the architecture behind our visual ETL engine and collaboration system - **Live Demo**: A walkthrough of the alpha release — what’s working, what’s missing, and what’s next - **What’s Ahead**: Our roadmap toward beta and full public release, and how you can help shape it ### **Who Should Attend:** GIS analysts, developers, and open-source enthusiasts curious about accessible, collaborative alternatives to traditional spatial ETL tools — and anyone interested in testing and contributing to an early-stage platform. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/CJF3BD/ WG403 Kyle Waite PUBLISH K7GULV@@talks.osgeo.org -K7GULV Enhancing MapProxy with New 2D and 3D Layers Support via Plugins en en 20251120T140000 20251120T142500 0.02500 Enhancing MapProxy with New 2D and 3D Layers Support via Plugins As the demand for modern geospatial data formats grows, integrating 2D and 3D content into open-source GIS infrastructures becomes increasingly critical. This talk presents our work on extending MapProxy—a widely used open-source tile and proxy server—to support next-generation geospatial formats through a modular plugin architecture. We introduce new plugins that enable seamless support for: - **Cloud Optimized GeoTIFFs (COG)** - **Vector tiles** - **3D Tiles** These enhancements allow MapProxy to serve as a unified gateway for both traditional and modern geospatial services. They enable seamless integration of high-performance, scalable 2D and 3D data delivery into enterprise workflows, while preserving compatibility with established OGC standards such as **WMS**, **WMTS**, and **TMS**. **Attendees will gain insights into:** - The architecture and design of the plugin system - Implementation details - Deployment strategies PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/K7GULV/ WG403 Mete Ercan Pakdil Alex Briggs PUBLISH EFZPKM@@talks.osgeo.org -EFZPKM AI in QGIS: Hype, Help, or Just a Gimmick? en en 20251120T143000 20251120T145500 0.02500 AI in QGIS: Hype, Help, or Just a Gimmick? Artificial Intelligence (AI) is no longer just a buzzword - it’s becoming increasingly a useful tool in the world of GIS, and QGIS plugins are quickly becoming more relevant and powerful in this space. This talk explores how AI and integrated LLM’s can enhance geospatial workflows within QGIS. We’ll start by unpacking key terminology: What is AI? What is machine learning? What are large language models (LLMs)? And why do these technologies matter for geospatial professionals? There’s already an abundance of tools available - but which QGIS plugins actually work? Are they helpful in real-world projects? Can we trust their outputs? Who is using them, and how? We’ll explore these questions through practical examples, and also look at the pros and cons of current tools - highlighting key challenges around transparency, usability, and data quality. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/EFZPKM/ WG403 Pierre Kurth Mike Gresham PUBLISH 99UNTK@@talks.osgeo.org -99UNTK Introducing OGC Developer Tier membership en en 20251120T153500 20251120T154000 0.00500 Introducing OGC Developer Tier membership OGC is introducing a new developer tier of membership to allow developers to contribute and take advantage of OGC resources and activities more easily. This talk will outline the approach and benefits. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/99UNTK/ WG403 Robert Atkinson PUBLISH 7LDKC8@@talks.osgeo.org -7LDKC8 Shaping a FOSS4G community in Bulgaria en en 20251120T154000 20251120T154500 0.00500 Shaping a FOSS4G community in Bulgaria People and companies working with FOSS4G technologies had never gathered together for networking until the first FOSS4G:BG in March 2024. The event had great success bringing together more than 100 participants from both the public and the private sectors. The second edition, held in March 2025, was even more ambitious and featured a full day of workshops followed by a day of talks. Moreover the OSM mapathon in May 2025 sparked interest in many university and high school students. All of this was possible to happen thanks to our informal organisation - QGIS.bg. More than just an organization, QGIS.bg serves as a platform for sharing GIS materials and tutorials in Bulgarian language. This is only the beginning for us in shaping a stable community. While we face many challenges, we believe that we are on the right track. With enough motivation, consistency and shared passion we will eventually grow into the strong and vibrant FOSS4G community that Bulgaria deserves. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/7LDKC8/ WG403 Teodora Koleva PUBLISH 3FH8AK@@talks.osgeo.org -3FH8AK Filling the Gaps: Mapping Marine Habitats with Divers, Aerial Imagery, and Algorithms en en 20251120T154500 20251120T155000 0.00500 Filling the Gaps: Mapping Marine Habitats with Divers, Aerial Imagery, and Algorithms ORA Reefs is a new initiative developed by Ocean Regeneration Aotearoa (ORA) Trust to actively restore degraded rocky reef and benthic ecosystems across Tīkapa Moana/the Hauraki Gulf, New Zealand. Sea urchin barrens are a dominant stressor on rocky reef ecosystems worldwide due to the overfishing of key predators. Evidence shows that releasing barren rocky reefs from kina (Evechinus chloroticus) grazing pressure in the Hauraki Gulf through large-scale removal enables biodiversity regeneration within 2 years. ORA Reefs’ initial focus is to pilot large-scale kina barren removal alongside the development of artificial reefs on degraded benthic ecosystems. If successful, these interventions, alongside the development of Blue Nature Credits, may offer a way to unlock capital for ecosystems that have historically lacked funding. While dive surveys can provide high-resolution data including species composition and physical characteristics of the subtidal environment, they are expensive, require specialised scientific divers, and only cover small areas. Geospatial classification techniques could better support broad-scale habitat identification and monitoring over a significantly larger area at relatively little cost. However, at present it is difficult to accurately depict biodiversity and physical traits of subtidal marine habitats. Here, we aim to combine aerial imagery with diver surveys using machine learning and deep learning algorithms to classify near-shore broad-scale marine habitat. We have begun developing a pipeline using Python and QGIS to classify these marine habitats, mitigating the issue of water column reflectance contribution to seabed reflectance by applying the Depth-Invariant Index (DII). The initial use-case trialled feeding DII layers through a Random Forest model with drone imagery that captured Blue, Green, Red, Red Edge, and Near Infrared (NIR) bands at ~5 cm resolution. Validation accuracy scores are promising and justify continual pipeline development to enhance marine habitat identification at different locations and times. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/3FH8AK/ WG403 Laura Read Thomas Dowling PUBLISH TBNEFD@@talks.osgeo.org -TBNEFD Semantic Spatial Search with Natural Language: Integrating NL2SQL with PostGIS & pgVector en en 20251120T155000 20251120T155500 0.00500 Semantic Spatial Search with Natural Language: Integrating NL2SQL with PostGIS & pgVector A Semantic Spatial Search Architecture Based on Natural Language: Integrating NL2SQL with PostGIS and pgVector Traditional location searches have relied on explicit keywords or structured database queries, such as “Auckland Sky Tower.” However, with the widespread adoption of LLMs, users increasingly expect to explore places using ambiguous and abstract expressions, like “the tallest building nearby” or “a cafe with a great view.” To overcome the limitations of these conventional search methods, we propose an architecture capable of directly understanding users’ natural language and performing semantic spatial search. Natural Language Processing (NLP) techniques to analyze abstract user requirements—such as “cozy atmosphere” or “cost-effective”—and identifies their core intentions. Based on these intentions, it employs NL2SQL techniques to directly query spatial and vector data stored in PostGIS and pgVector, enabling precise retrieval of relevant objects. Through the organic integration of these components, the proposed architecture goes beyond simple information retrieval—performing multi-step reasoning on user intent and delivering high-level semantic spatial search that recommends the optimal location. --- This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00143336, NTIS Grant: 2610000396) PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/TBNEFD/ WG403 Hanjin Lee Jungin Yoon PUBLISH GSE9DT@@talks.osgeo.org -GSE9DT “Develop Traffic Simulator for Urban Planning” — Integrated 2D/3D Platform Workflow for Urban Planning en en 20251120T155500 20251120T160000 0.00500 “Develop Traffic Simulator for Urban Planning” — Integrated 2D/3D Platform Workflow for Urban Planning Urban traffic systems are rapidly becoming more complex due to urban expansion, new town developments, and the diversification of transportation modes. Therefore, a user-centered system is needed to examine various scenarios during planning and to intuitively understand the results. Purpose: - To support urban planners and policy makers with an end-to-end traffic simulation platform that enables interactive scenario editing, execution, and comparative visualization based on real city data. This system is built based on actual urban data, utilizing transportation network and station data from Seoul and Daejeon in GeoJSON and PostGIS formats as simulation input. Users can modify road networks and route structures through a scenario editor, configure various demand conditions, and execute simulations. Scenarios are edited and visualized in 2D and 3D using OpenLayers and CesiumJS, and the results can be explored in real-time through analysis tools such as scenario comparison, time filtering, and route change analysis. Key Features: - Scenario builder for editing roads, routes, and simulation parameters - Integrated 2D (OpenLayers) and 3D (CesiumJS) visualization per scenario - Time filter, route change analyzer, and side-by-side comparison UI - Workflow: Edit → Simulate → Visualize → Analyze The entire structure is implemented based on a microservice architecture, allowing each function to be independently scalable and flexibly integrated with external traffic modeling engines. This presentation shares the practical implementation experience of an open-source-based integrated workflow platform, applied to real urban data from Seoul and Daejeon. Open Source Technologies Used: - **CesiumJS** for immersive 3D scenario playback - **OpenLayers** for 2D route editing and spatial analysis tools - **PostGIS** and **GeoJSON** for simulation data input This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (RS-2024-00459703, Development of next-generation AI integrated mobility simulation and prediction/application technologies for metropolitan cities) PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/GSE9DT/ WG403 Hansang Kim Sungjo MIN PUBLISH XUFKY9@@talks.osgeo.org -XUFKY9 GIS for football en en 20251120T160000 20251120T160500 0.00500 GIS for football This will be a demonstration of a prototype mobile tool for generating football pitch geometries dynamically on location, that allows casual community football groups to make temporary line markings easily and accurately when combined with a suitable consumer-grade mobile location device. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/XUFKY9/ WG403 radhika PUBLISH 9NHRGH@@talks.osgeo.org -9NHRGH Tracing the Chaos: Observability for Web Applications en en 20251120T160500 20251120T161000 0.00500 Tracing the Chaos: Observability for Web Applications Observability isn’t just for big tech companies, large IT infrastructure or non spatial projects it’s for all web applications too. In this lightning talk, I’ll show how you can get immediate insights into the real-world behaviour of your applications by using monitoring tools such as Sentry. The talk will use real world examples using Sentry which is a source-available platform with a strong open core model. While not fully OSI open source, it offers transparency, self-hosting, and a generous license for individuals and small teams. In this talk Sentry is used as a practical example, the focus is on core principles of observability and monitoring that apply broadly. We’ll start with a quick look at what observability means when working with modern web applications, and why traditional logging alone doesn’t cut it. Then we will show examples of how monitoring integrates into applications, capturing uncaught exceptions, tracking performance issues like slow API calls or render delays, and tying everything back to real user sessions. I’ll highlight how observability fits into your development workflow and surfacing issues in real time, grouping similar errors,. You’ll also see how performance monitoring works out of the box and might you might need to tweak to get useful information, for example retrieving what is displayed on a spatial map. Whether you’re debugging spatial maps not showing, flaky frontends or mysterious server errors, this talk will give you a fast, practical intro to making your apps more observable using open source tools you can start with today. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/9NHRGH/ WG403 Jamie Sherriff PUBLISH TJTC3X@@talks.osgeo.org -TJTC3X Beyond Square Pixels: H3 Spatial Indexing for Global Raster Data en en 20251120T161000 20251120T161500 0.00500 Beyond Square Pixels: H3 Spatial Indexing for Global Raster Data This lightning talk demonstrates a practical workflow for converting raster data (like 20GB+ DEM datasets such as GEDTM30 global 1-arc-second (~30m or ~900m²) Digital Terrain Model) to H3 Discrete Global Grid Systems (DGGS) with resolution 12 (around ~300m²),enabling global spatial analysis minimising projection distortions. H3 provides much more consistent spatial partitioning compared to traditional raster projections, especially at global scales, with less proportional distortion (closer to the poles) compared to square pixels in Web Mercator or other commonly used projections. The presentation showcases a complete pipeline: H3 hashes parquet file → Raster reading to H3 parquet → Parquet storage and processing (using GDAL + DuckDB) → Parquet to Geopackage conversion → QGIS visualization. Using the GEDTM30 global DEM as example data, pixel values are mapped to H3 cells at specified resolution and stored efficiently in columnar format, then converted to GeoParquet for seamless visualization workflows. When deployed as a OGC REST API, single H3 hash requests achieve near-constant time O(1) cell data retrieval, enabling the system to scale to massive datasets while maintaining consistent per-request performance. Key benefits include eliminated projection edge effects, consistent global hexagonal coverage, hierarchical multi-scale analysis, and compatibility with modern cloud-native geospatial tools. Attendees will see live demonstrations of H3 tools, practical code examples, performance comparisons and usage of a H3 REST API as data source on a local calculation workflow. This approach transforms how we handle large-scale geospatial raster data, making global analysis more accessible and accurate. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/TJTC3X/ WG403 Jorge S. Mendes de Jesus PUBLISH UEAYPT@@talks.osgeo.org -UEAYPT Safe n Redi: Community-Led Resilience Mapping through Open Source Tools in the Pacific en en 20251120T161500 20251120T162000 0.00500 Safe n Redi: Community-Led Resilience Mapping through Open Source Tools in the Pacific In the Pacific, when disaster strikes, people turn first to places they trust: churches, community halls, and schools. Many of these act as informal evacuation centers—spaces that communities rely on but are rarely documented in formal preparedness systems. The Safe n Redi (SnR) platform was created to change this. Developed through the CAN DO Network and implemented by agencies like ADRA Fiji, SnR is a platform used across Fiji, Tonga, Solomon Islands, and Vanuatu to assess how prepared local buildings are to serve as safe spaces. Whether a church or community hall, each compound is mapped and evaluated on structural integrity, water and sanitation, electricity, accessibility, and evacuation capacity. What makes this initiative unique: It combines faith-based coordination (churches still make up the majority of compounds) with inclusive community infrastructure. It is open to anyone—enumerators, project officers, and church or community leaders—who receives training on KoboToolbox, mobile-based data collection, and geospatial assessment tools. The data is visualized using QGIS, integrated into Power BI, and hosted on a web-based dashboard that supports decision-making at national and regional levels. This session will walk through: The registration and vetting process for using Safe n Redi across countries. Field implementation: how enumerators are trained in open-source tools to assess buildings on disability suitability, water access, ventilation, structural safety, and more. Examples from Fiji and beyond, where communities used the platform to advocate for safer evacuation infrastructure. How church and community leaders use this platform not just as a technical tool, but as a conversation starter for resilience. Safe n Redi is an open, adaptable, and locally owned solution for countries that rely on community-driven action in the face of disaster. Built with trust, backed by data, and powered by open tech—it’s a model for resilience from the Pacific to the world. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/UEAYPT/ WG403 Maloni Vakacavu Siga Silovate Batirerega PUBLISH VNEHDC@@talks.osgeo.org -VNEHDC Rebuilding TerriaMap UI: A Minimalist Approach Without Forking TerriaJS en en 20251120T162000 20251120T162500 0.00500 Rebuilding TerriaMap UI: A Minimalist Approach Without Forking TerriaJS TerriaMap wraps the powerful TerriaJS engine, but it comes with a pre-built UI that may not suit every project. In this talk, I’ll show how you can completely remove the standard UI in TerriaMap and build a simple custom interface, without touching TerriaJS core code or maintaining a fork. I’ll walk through how to hook into UserInterface.tsx, connect to the Terria instance, and wire up two minimalist controls: switching between 2D/3D view modes and toggling base maps. The talk will feature a few before-and-after screenshots, as well as a link to a public repository with the full implementation for anyone interested in diving deeper. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/VNEHDC/ WG403 Yuri Vyatkin PUBLISH RSS7NB@@talks.osgeo.org -RSS7NB Kart: Git for Geospatial - Version Control for Vector, Raster, and Point Cloud Data en en 20251120T090000 20251120T092500 0.02500 Kart: Git for Geospatial - Version Control for Vector, Raster, and Point Cloud Data Why Kart? - Native support for Postgres, SQL Server, MySQL, and GeoPackage working copies - Git under the hood - Works with Git hosting platforms - Built-in import/export for common geospatial formats - Unified management of vector data, tiled rasters and point clouds What you'll learn: - See Kart in action with real-world examples across vector, raster, and point cloud datasets - Architectural insights: How we adapted Git's model for spatial data - Lessons learned: Technical challenges and solutions in geospatial versioning - Hosting options for your Kart data Who should attend: GIS professionals, data engineers, and developers working with evolving spatial datasets who want better collaboration and change tracking. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/RSS7NB/ WG308 TE IRINGA Craig de Stigter PUBLISH BHNUHK@@talks.osgeo.org -BHNUHK Wait... QGIS Can Do What? en en 20251120T093000 20251120T095500 0.02500 Wait... QGIS Can Do What? *Did you know you can use QGIS from the command-line?* ... or *Create a map from your photos in a single click?* ... or *Do fuzzy data joins?* ... or *Create 3D fly-throughs?* .. Come and discover new features and tools you didn't know existed in QGIS and - help you work faster and smarter. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/BHNUHK/ WG308 TE IRINGA Ujaval Gandhi PUBLISH HGK9G7@@talks.osgeo.org -HGK9G7 State of UN Open GIS Initiative en en 20251120T100000 20251120T102500 0.02500 State of UN Open GIS Initiative The UN Open GIS Initiative enters its second decade of transforming geospatial technology within the United Nations system. Since 2016, the initiative has developed open source GIS bundle for UN peace operations. This presentation introduces key activities including hybrid GIS development, capacity building, and drone mapping while exploring future directions for community collaboration. Launched in 2016 under the Partnership for Technology in Peacekeeping initiative of the United Nations Department of Operational Support (DOS), the UN Open GIS Initiative emerged from the critical need to efficient geospatial technology across UN operations. Over the past nine years, the initiative has established itself through several key activity areas. Hybrid GIS development pioneered a pragmatic approach to GIS implementation, integrating open-source and proprietary solutions based on operational needs. Comprehensive capacity building programs were developed recognizing that technology alone cannot solve complex geospatial challenges. These programs range from basic GIS literacy for field staff to advanced technical training for geospatial specialists. The UN Open GIS Initiative now operates through a federated structure that includes multiple Domain Working Groups (DWGs), each focusing on specific aspects of geospatial technology and application. Among these is DWG 7, the UN Smart Maps Group, which specializes in testing emerging technologies for future geospatial operations. The initiative maintains strategic partnerships with the OSGeo Foundation to ensure alignment with global open-source geospatial development. The second decade brings new challenges that require innovative approaches. Scaling across diverse contexts involves adapting solutions that work in peacekeeping contexts for humanitarian, development, and specialized agency needs. Technology evolution requires keeping pace with rapid technological change while maintaining stability and reliability in operational environments. Capacity sustainability ensures that capacity-building efforts create lasting institutional change rather than temporary improvements. Data sovereignty and security considerations balance open data principles with legitimate security concerns and national sovereignty requirements. Looking ahead, the UN Open GIS Initiative is positioning itself to address emerging global challenges through several strategic directions. Enhanced integration involves deeper integration with UN reform initiatives and the UN's digital transformation agenda. Community-centric development increases focus on community-driven development and local ownership of geospatial solutions. Emerging technology adoption includes systematic exploration and adoption of emerging technologies including distributed web systems, Internet of Things integration, and advanced AI applications. Global partnerships expand partnerships with national governments, academic institutions, and civil society organizations to create a truly global geospatial commons. The UN Open GIS Initiative's first decade demonstrates the transformative potential of open geospatial technology in international cooperation. As the initiative enters its second decade, it seeks to deepen engagement with the global FOSS4G community, leveraging collective expertise to address increasingly complex global challenges. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/HGK9G7/ WG308 TE IRINGA Hidenori Fujimura PUBLISH 9UQEWT@@talks.osgeo.org -9UQEWT Empowering Urban Planning With Open Geospatial Technologies: The I-Plan Experience In Malaysia en en 20251120T110000 20251120T112500 0.02500 Empowering Urban Planning With Open Geospatial Technologies: The I-Plan Experience In Malaysia Urban planning increasingly relies on accurate, accessible, and up-to-date geospatial information to support sustainable development and informed decision-making. However, traditional planning systems often suffer from fragmented data, inconsistent standards, and limited stakeholder engagement. This presentation shares Malaysia’s experience in addressing these challenges through the development and implementation of I-Plan — an integrated planning land use information system built on open geospatial technologies. I-Plan consolidates multi-source planning data into a centralized, interactive web-GIS platform that empowers planners, agencies, and the public to access, analyze, and utilize spatial information more effectively. The presentation highlights I-Plan’s architecture, key features, and the open-source tools that make it scalable and adaptable to various planning needs. It also discusses practical lessons learned in harmonizing diverse datasets, ensuring multi-level support, and building local capacity to manage and maintain the system. By showcasing the I-Plan experience, this session demonstrates how open geospatial solutions can drive better urban governance, promote data-driven planning, and foster greater transparency and collaboration among stakeholders. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/9UQEWT/ WG308 TE IRINGA Muhamad Ikhwan bin Saadon PUBLISH HQUFT7@@talks.osgeo.org -HQUFT7 vrpRouting: Vehicle-Routing Optimization inside PostgreSQL en en 20251120T113000 20251120T115500 0.02500 vrpRouting: Vehicle-Routing Optimization inside PostgreSQL Complex logistics—such as parcel delivery, curbside recycling, and emergency response—depend on Vehicle Routing Problem (VRP) solvers that can handle multiple stops, depots, and tight time windows. Most open solutions run outside the spatial database, forcing data to shuttle through files and scripts. vrpRouting eliminates that bottleneck. Distributed as a PostgreSQL extension on top of PostGIS and pgRouting, it embeds optimization algorithms—including tabu-search and other meta-heuristics—directly in database-native functions. Users store streets, depots, and orders where they already live in the database and receive optimized LINESTRING routes and per-stop attributes with no ETL overhead. Under the hood, vrpRouting streams network geometry to its engines, respects turn restrictions and one-way rules from PostGIS, and solves city-scale instances under tight time frames. A single query can pivot from capacitated one-depot scenarios to multi-depot pickup-and-delivery or strict time-window problems by toggling function parameters. We showcase this capability using OSM data for Auckland, routing parcel stops, scores of vehicles, and depot-specific time windows across the entire urban road graph, then visualizing driver sequences live in QGIS through a direct PostGIS connection. The presentation will unpack the extension’s architecture and current function set and walk through the Auckland case study. By keeping optimization within PostgreSQL, vrpRouting enables practitioners to transition from raw road graphs to production-ready routes with minimal database calls, thereby preserving the open-source ethos at the heart of FOSS4G. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/HQUFT7/ WG308 TE IRINGA Vicky Vergara Joseph Percival PUBLISH ANCEU3@@talks.osgeo.org -ANCEU3 GeoServer 3 Status Report en en 20251120T120000 20251120T122500 0.02500 GeoServer 3 Status Report We’ll first analyze the GeoServer 2.x status quo, and the effect of cascading changes that a “simple” Spring upgrade caused, turning the activity into a cross project overhaul, and how the large effort required got socialized and eventually brought to implementation via in-kind volunteering and a crowdfunding campaign driven by Camp2Camp, GeoCat and GeoSolutions. We will explore the planned milestones in the transition to GeoServer 3. These include critical refactorings, such as replacing aging libraries, adopting modern Java frameworks, and integrating support for the latest versions of GeoTools and GeoWebCache. Key technical advancements include the evolution and integration of ImageN for improved raster data processing, the migration from Wicket 7 to Wicket 10 for a modernized and more secure web user interface, and the adoption of Jakarta EE and Spring 6 to support enhanced security, scalability, and long-term compatibility with modern Java ecosystems. Join us to investigate progress, reflect on the lessons learned, and get inspired by what’s possible with GeoServer 3—a project that continues to empower geospatial professionals and organizations worldwide. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/ANCEU3/ WG308 TE IRINGA Jody Garnett PUBLISH PJFGBP@@talks.osgeo.org -PJFGBP Open Source Intellectual Property en en 20251120T133000 20251120T135500 0.02500 Open Source Intellectual Property Intellectual Property laws and rights are a "Boring but Important” part of the open-source software ecosystem. In this talk I'll cover a few topics: * The nascent EU Cyber Resilience Act, a new governing framework outlining some additional rights/restrictions/responsibilities for software producers. * Why you should care about Intellectual Property Rights. * Defining Copyright and Licensing * How to choose a Software License * How to apply a Software License PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/PJFGBP/ WG308 TE IRINGA Jonah Sullivan PUBLISH 9YJZE3@@talks.osgeo.org -9YJZE3 Fighting Invasive Predators with Open Source | How QField Empowers ZIP’s Mission for a Predator-Free New Zealand en en 20251120T140000 20251120T142500 0.02500 Fighting Invasive Predators with Open Source | How QField Empowers ZIP’s Mission for a Predator-Free New Zealand Zero Invasive Predators (ZIP), a New Zealand-based charity, is on a bold mission: to eliminate invasive predators and restore New Zealand’s native ecosystems. To meet this challenge, ZIP relies on a powerful open-source geospatial stack—QGIS, QField, and QFieldCloud—to bridge technical planning and field operations. This talk will showcase how QField and QFieldCloud enable ZIP’s field teams to efficiently capture accurate data in remote locations and instantly sync updates. This near real-time feedback loop allows for rapid, data-driven decisions in the fight against invasive species. We’ll explore ZIP’s field workflows, discuss the advantages of using open-source tools in conservation, and highlight how QFieldCloud’s collaboration capabilities are central to ZIP’s success in combining innovative thinking with boots-on-the-ground action. Join us to learn how geospatial open source software is helping protect biodiversity—one synced observation at a time. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/9YJZE3/ WG308 TE IRINGA Nicholas Braaksma PUBLISH 973KES@@talks.osgeo.org -973KES Open source software in turbulent times en en 20251120T143000 20251120T145500 0.02500 Open source software in turbulent times A talk about the challenges and opportunities Free and Open Source Software communities and related businesses face. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/973KES/ WG308 TE IRINGA Jeroen Ticheler PUBLISH CH9FK9@@talks.osgeo.org -CH9FK9 Getting Sentinel Data within seconds en en 20251120T153000 20251120T155500 0.02500 Getting Sentinel Data within seconds Satellite imagery is more accessible than ever—but getting the data you need quickly and efficiently can still be a challenge. In this talk, we’ll explore how STAC (SpatioTemporal Asset Catalog) and the Microsoft Planetary Computer simplify the process of accessing and analyzing Sentinel imagery at scale. You’ll learn how to use Python and modern geospatial libraries like `pystac-client`, `odc-stac`, and `xarray` to query, filter, and load Sentinel-2 and Sentinel-1 imagery—no downloading or unzipping required. We’ll demonstrate how to filter scenes by date, cloud cover, and region of interest, and then quickly calculate vegetation indices such as NDVI, EVI, and RVI for environmental or agricultural analysis. We’ll cover: What STAC is and why it matters How the Microsoft Planetary Computer provides cloud-hosted, analysis-ready data How to build fast, scriptable analysis pipelines using Python By the end of this session, attendees will understand how to go from a geographic query to insightful raster analysis in just a few lines of code—making remote sensing workflows faster, reproducible, and scalable. Perfect for data scientists, remote sensing professionals, and developers looking to cut through the complexity of traditional satellite data access. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/CH9FK9/ WG308 TE IRINGA krishna lodha PUBLISH BKGYKQ@@talks.osgeo.org -BKGYKQ A5: Rethinking Spatial Indexing en en 20251120T160000 20251120T162500 0.02500 A5: Rethinking Spatial Indexing For millennia, humans have been fascinated by tiling patterns, but when it comes to partitioning our planet, we've been limited by the assumption that regular polygons are the only option. A5 challenges this assumption and introduces a paradigm shift in spatial indexing through pentagonal cells that offer truly equal areas and superior accuracy. ### The Problem with Current Systems H3, developed by Uber and now the leading spatial indexing system used across industry, has revolutionized geospatial analysis with its hexagonal approach. However, even H3 faces fundamental mathematical limitations that create real-world challenges for spatial analysis. The most significant issue is cell area variation: H3 cells vary by a factor of nearly 2 across the globe, with the largest hexagons being around 2 times larger than the smallest ones. This variation introduces systematic bias in spatial analysis - identical densities appear different depending on location, and statistical comparisons across regions become unreliable. Another limitation is that H3's finest resolution offers cells around 1 square meter. As a result, the cells cannot be used to index positions with high accuracy. ### The A5 Solution: Embracing Pentagons A5 takes a radically different approach by embracing pentagons from the start - marking the first time pentagons have been used as the primary building block for a Discrete Global Grid System. The system partitions the world into pentagonal cells across 32 different resolution levels, from 12 cells covering the entire world down to cells smaller than 30mm². This extraordinary precision is encoded as a 64-bit integer, making A5 computationally efficient while maintaining millimeter-level accuracy - orders of magnitude finer than H3's smallest cells. ### Key Advantages Over Alternative Systems - **Uniform Cell Sizes**: Unlike H3 and other DGGSs, A5 provides completely equal area cells within each resolution level - 0% error thanks to a novel polyhedral projection based on the Snyder projection. This eliminates bias in spatial analysis and ensures perfect statistical validity across all geographic regions. - **Minimal Distortion**: A5's geometric construction based on a dodecahedron - the platonic solid with the lowest vertex curvature - results in minimal distortion when projecting onto the sphere - **High Resolution**: With cells as small as 30mm² at the finest resolution level, A5 enables applications requiring extreme precision, from precision agriculture to infrastructure monitoring. - **No Special Cases**: Every A5 cell is a pentagon, eliminating the complex special case handling required by systems with mixed polygon types. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/BKGYKQ/ WG308 TE IRINGA Felix Palmer PUBLISH MRPVGL@@talks.osgeo.org -MRPVGL Accelerating GeoTIFF readers with Rust en en 20251120T163000 20251120T165500 0.02500 Accelerating GeoTIFF readers with Rust How can we compose together a modern library to decode Cloud-optimized GeoTIFFs (COGs) efficiently? By using a programming language called Rust, with bindings to Python, WebAssembly and more, our goal is to enable applications that demand high-performance reads, such as web-based COG tilers or machine learning workflows leveraging Graphical Processing Units (GPUs). For CPU workflows, we delegate the network/disk transfer handling to the [`object_store`](https://crates.io/crates/object_store) crate, use various Rust-based algorithms for decompressing raw bytes, and let the [`async-tiff`](https://crates.io/crates/async-tiff) crate do the actual TIFF tag metadata and pixel data parsing. For GPU workflows, we swap the decompression library for [`nvCOMP`](https://developer.nvidia.com/nvcomp), and do the TIFF parsing using [`nvTIFF`](https://developer.nvidia.com/nvtiff), with the resulting pixel data decoded directly into CUDA device memory. Come and see how these asynchronous and GPU-accelerated GeoTIFF readers compare against GDAL's [`libertiff`](https://gdal.org/en/release-3.11/drivers/raster/libertiff.html) driver, and find out how we're making these performant low-level Rust-based readers more accessible by integrating with the [xarray](https://xarray.dev) ecosystem and beyond! PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/MRPVGL/ WG308 TE IRINGA Wei Ji Leong PUBLISH MKP7QY@@talks.osgeo.org -MKP7QY Bathymetry Data Wrangling en en 20251120T090000 20251120T092500 0.02500 Bathymetry Data Wrangling While all of Earth’s land surfaces have been mapped at 30 m resolution or finer, the topography of the seabed is still largely a black box, with only 26.1% of the seabed mapped to “adequate resolution” thus far. In practice, this means that compiling a gridded bathymetric dataset often requires “filling in” areas of missing data, “smoothing” conflicts between overlapping datasets, and “blending” information from multiple sources. This talk discusses several strategies for fusing and interpolating datasets such as coastal lidar, high resolution multibeam echosounder data, chart depths, and the globally available GEBCO grid in real world application. The “fuzzy” boundary between land and sea is emphasized, as terrestrial and marine datasets often disagree when their coverage overlaps. Interpolation and smoothing methods are explored as well. The techniques discussed are implemented in Python using geospatial libraries and thus open source, easily scaled, and reproducible. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/MKP7QY/ WG126 Ellorine Carle PUBLISH RCZXKS@@talks.osgeo.org -RCZXKS "Chef's Kiss" Webmaps with Svelte, MapLibre & PMTiles en en 20251120T093000 20251120T095500 0.02500 "Chef's Kiss" Webmaps with Svelte, MapLibre & PMTiles Since the dawn of interactive webmaps in the mid-1990s, map developers have cycled through many generations of technologies. From the early days of vanilla JS with native DOM and server-side tile rendering, to the modern days of Virtual DOM-based frameworks with WebGL and vector tiles, each generation of technology has utilized the cutting-edge to build the best possible map applications. Just like the evolution from candles to gas lamps to electric bulbs, each iteration of map technology has brought an overall improvement in functionality while it matures and stabilizes over time. And while the current paradigm of React + ${Map Library} works pretty well, what if we could do it better? In pursuit of elegant, highly functional, “chef’s kiss” interactive web maps, this talk presents a pattern of building applications with vanilla MapLibre, Svelte, and PMTiles, and compares the approach to the ways of old. Using the interactive Auckland map integrated into the FOSS4G conference website as an example, it gives heaps of practical advice for developers new and old. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/RCZXKS/ WG126 Rami DV PUBLISH KUPAWC@@talks.osgeo.org -KUPAWC eo-tides: Open-source tide modelling tools for large-scale satellite Earth observation analysis en en 20251120T100000 20251120T102500 0.02500 eo-tides: Open-source tide modelling tools for large-scale satellite Earth observation analysis Freely available Earth observation (EO) satellite data is a powerful resource for mapping and monitoring dynamic coastal environments over time and across large areas. However, the influence of ocean tides means satellite data is often acquired at vastly different tidal stages. This can make it difficult to distinguish true patterns of coastal change from short-term tidal variability, leading to inaccurate or misleading insights into coastal processes. To address this challenge, there is a pressing need for scalable open source tools that can account for tidal variability and make tides an explicit part of coastal EO analysis. The new Geoscience Australia `eo-tides` package (https://github.com/GeoscienceAustralia/eo-tides) offers powerful open-source tools for integrating satellite EO data with ocean tide modelling. It provides a flexible Python toolkit for attributing modelled tide heights to satellite data time series, based on each satellite image's spatial extent and acquisition time. eo-tides builds on advanced tide prediction capability from the open-source `pyTMD` library, combining this with spatial analysis tools from the Open Data Cube (ODC)'s `odc-geo`. This enables efficient, parallelised modelling using over 50 supported tidal models, with outputs returned in standardised pandas and xarray formats for further analysis. `eo-tides` can be applied to petabytes of freely available satellite data accessed via the cloud using ODC’s `odc-stac` or `datacube` packages (e.g. using Digital Earth Australia or Microsoft’s Planetary Computer). Additional functionality supports validation with external tide gauge data and the assessment of potential satellite-tide biases - critical considerations for ensuring the reliability and accuracy of coastal EO workflows. These open-source tools support the efficient, scalable and robust analysis of coastal EO data for any time period or location globally. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/KUPAWC/ WG126 Robbi Bishop-Taylor PUBLISH RE8VMR@@talks.osgeo.org -RE8VMR Exploring urban form in New Zealand en en 20251120T110000 20251120T112500 0.02500 Exploring urban form in New Zealand This talk will showcase how open data from OpenStreetMap and other resources combined with open source tools from Python and R can help us build a deep understanding of our urban environment. The talk will be linked to a Github repo with reproducible code (and environment) for any interested enthusiast who wants to delve deeper into this topic. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/RE8VMR/ WG126 Shrividya Ravi PUBLISH TK8K3B@@talks.osgeo.org -TK8K3B Vector Tile Deployment with the United Nations Vector Tile Toolkit (UNVT) en en 20251120T113000 20251120T115500 0.02500 Vector Tile Deployment with the United Nations Vector Tile Toolkit (UNVT) Vector tile technology is becoming more popular due to its various advantages. Vector tiles are smaller in size, allow for dynamic styling without regenerating the tiles, and improve performance for end users. These features make vector tiles useful for base maps in the United Nations. The United Nations Vector Tile Toolkit (UNVT) was established in 2018 under the UN Open GIS Initiative, and has provided various tools to support the use of vector tiles. UNVT has grown with the help of many contributors, including the UN Smart Maps Group, which works under the initiative to develop a wide range of features. In the United Nations, UNVT plays a key role in three main processes: Produce, Style, and Host. The following is an overview of each process. Produce: Vector tiles are generated by extracting geospatial data from a PostGIS database and converting it using Node.js scripts and Tippecanoe. By selectively removing unnecessary attributes and simplifying geometries, we reduce data size, leading to faster rendering and improved performance for web users. In addition, we update our priority areas daily and run the scripts as a scheduled task to minimize manual effort. Style: unvt/charites is an easy-to-use, intuitive, and efficient command-line tool for managing vector map styles. With Charites, a long style JSON file can be split into multiple smaller YAML files—one per layer—making it much easier to edit styles. It also provides a convenient live style viewer in a local browser, where changes are reflected immediately. Host: We have developed a simple Node.js-based vector tile hosting server that delivers PBF files derived from MBTiles in response to each request. The map is rendered using MapLibre GL JS. Additionally, we have created an interface for ArcGIS Online to enable the map to be displayed within the UN system. This session will showcase practical use cases, technical implementation details, and lessons learned through the adoption of vector tile technology in the UN context. It will be relevant to GIS professionals, developers, and organizations looking to modernize their web mapping infrastructure using open source tools. Our toolkit is available on our GitHub account: https://github.com/unvt PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/TK8K3B/ WG126 KOJI OSUMI PUBLISH MEHRQF@@talks.osgeo.org -MEHRQF State of GRASS en en 20251120T120000 20251120T122500 0.02500 State of GRASS Join us for a lively overview of the current state of the GRASS project, where community meets cutting-edge geospatial technology. Whether you're a longtime power user or a newcomer curious about GRASS, this talk will highlight the major strides the project has made in the past year – from revitalized governance and community growth to technical breakthroughs – and offer a glimpse into what's next. During the talk, we will address how GRASS has strengthened its governance and support structure by bringing in new members to bolster sustainable leadership and new fiscal sponsorship with NumFOCUS. We will also review GRASS community-building initiatives, such as the NSF-backed efforts that allowed GRASS to establish a mentoring program for new contributors, support our Student Grant program, and hold the GRASS Developer Summit 2025 in Raleigh, NC. We will highlight this past summer's Google Summer of Code project, which demonstrates how community mentoring feeds innovation. The talk will also address GRASS's new logo and branding initiative over the past year, aiming to give the project a modern look while keeping its iconic elements. Notably, "GRASS GIS" is now officially just GRASS – a simpler name that the community has used colloquially for years. To celebrate, the team launched an online swag shop with GRASS-themed apparel, stickers, and more. We will also look at recent strides in community outreach and learning resources, such as a new tutorial website and the modernization of GRASS's documentation platform. On the development side, we will show off what the GRASS development team has been hard at work delivering in terms of new features, improved performance, and better integration as part of GRASS 8.5. Under the hood, the team made significant code quality and security improvements, fixing issues flagged by automated linters and code scanners. These efforts pave the way for stricter continuous integration checks and a more robust codebase. The build system is also being modernized: GRASS is transitioning to CMake for easier compilation and maintenance, and an official Conda package is on the way, simplifying installation for Python/R data scientists and lowering entry barriers. As we celebrate these achievements, we're also looking ahead. The GRASS roadmap outlines ambitious goals for the next few years. We plan to maintain annual releases (GRASS 8.6 is already on the horizon for 2026) and continue improving distribution and integration – think one-click installs via Conda, tighter bridges to QGIS and R, and refined Python and R APIs for smooth scripting. Sustainability remains a core focus: the project actively pursues new grants, sponsors, and community donations to ensure long-term development while spreading infrastructure knowledge and lowering maintenance overhead to avoid burnout. In short, the state of GRASS is strong and dynamic. This talk will offer an informative yet exciting tour of the project's recent milestones across community and technology. We invite everyone – from newbies to veteran developers – to see how far GRASS has come and to get inspired about where it's heading. Learn about the latest capabilities, meet the people behind the project, and discover how you can be part of the next chapter of GRASS! PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/MEHRQF/ WG126 Luca Delucchi Veronica Andreo Markus Neteler Alen Mangafić PUBLISH DUSEKB@@talks.osgeo.org -DUSEKB Developing a user-oriented data cube for biodiversity and carbon dynamics assessment in Estonia with remote sensing data en en 20251120T133000 20251120T135500 0.02500 Developing a user-oriented data cube for biodiversity and carbon dynamics assessment in Estonia with remote sensing data Introduction Addressing global environmental challenges like land use and climate change requires timely, accurate information. Earth Observation (EO) data, from satellites and UAVs, is essential for monitoring these dynamics. Thanks to open data policies and advancements in software and cloud computing, EO data enhances environmental management and policy assessment, contributing to sustainable development. However, there are technical challenges, including data storage and analysis, and the need for computational architectures that handle large datasets. Traditional data cubes often lack the readiness needed for advanced AI and machine learning techniques, which require structured, rich datasets. User-friendly platforms with intuitive access and customizable tools are crucial for researchers and policymakers. Our project aims to create a comprehensive data cube for Estonia, utilizing remote sensing and geospatial data and open-source tools to advance biodiversity and carbon dynamics research. The fusion of LiDAR, radar, and passive remote sensing offers untapped potential for modeling, and multi-temporal datasets can predict vegetation and environmental variables effectively. Data and Methods We incorporated data from Sentinel-1, Sentinel-2, Landsat, and high-resolution airborne LiDAR. We used Google Earth Engine and Python for data pre-processing. Additionally, digital elevation models and the Estonian soil map were used to prepare the data cube layers. The study area was divided into manageable tiles using a spatial grid, creating 10m resolution Cloud Optimized GeoTIFFs (COGs) to facilitate efficient processing and downloading. LiDAR data allowed us to calculate biodiversity-relevant indices, including ecosystem height, cover, and structural complexity. These were processed with tools like PDAL and laspy for precise classification and filtering. The data cube runs on a high-performance cloud platform, using S3 storage for COGs and libraries such as rasterio to gather metadata. This metadata is integrated into a STAC-compatible web service, enabling seamless access through platforms like QGIS and Python for efficient querying and processing. Data Cube Access Our data cube portal (https://geokuup.ee/estonia) , developed with the Phoenix framework, utilizes MapLibre for data visualization. This setup supports quick visualizations and queries, organizing datasets into collections for user convenience. Users can create custom collections, tailoring data sets to specific research needs, which enhances the system’s flexibility. By adopting best practices in geospatial data management, we leveraged open-source tools like GeoServer and pygeoapi, along with the Pangeo ecosystem, to streamline processing. The Phoenix framework offers a robust and efficient solution for managing concurrent users, ensuring stability and performance. Outcomes and Future Work The data cube provides high-resolution spatial data for academic and governmental purposes, with a strong focus on biodiversity and carbon research. It offers a scalable solution that can be extended to other research domains by incorporating additional data layers. Future work will focus on processing data into multiple resolutions and expanding the range of datasets and workflows to enhance data retrieval and analysis. This will further support informed decision-making and sustainable development initiatives, empowering researchers and policymakers with timely environmental information. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/DUSEKB/ WG126 Alexander Kmoch Evelyn Uuemaa PUBLISH S9EJGF@@talks.osgeo.org -S9EJGF From Collaboration to Action: Unlocking Ocean Information Through Pacific Ocean Portal 2.0 en en 20251120T140000 20251120T142500 0.02500 From Collaboration to Action: Unlocking Ocean Information Through Pacific Ocean Portal 2.0 The Pacific Ocean Portal 2.0 is a transformative platform advancing ocean science in the Pacific region by serving as a centralized hub for the dissemination and visualization of oceanographic information. Designed to empower National Meteorological and Hydrological Services (NMHSs) and support key sectors such as tourism, fisheries, ocean monitoring, sea level, and coral reef management, the portal provides seamless access to a comprehensive suite of datasets, including forecasts, near real-time and historical data, and in situ observations. At its core, the portal is powered by a robust GIS data management system, ensuring high-quality geospatial data handling and integration for visualization and analysis. Built with a modular, open-source architecture—including THREDDS, GeoServer, FastAPI, and a Next.js frontend—the portal enables developers to create country-specific products and tools that meet unique national needs. New functionalities include timeseries extraction from NetCDF files, on-the-fly map generation, near real-time in situ monitoring, a resource library, and a directory of regional ocean experts. Controlled access to restricted datasets, such as high-resolution wave and inundation forecasts, is also supported for designated countries. By fostering interoperability with platforms like PACIOOS and reducing dashboard fragmentation, the portal transforms collaborative ocean data efforts into actionable insights and services. Through unified access to information, expert resources, and advanced GIS capabilities, Pacific Ocean Portal 2.0 empowers communities, strengthens regional scientific capacity, and supports sustainable development and climate resilience across the Pacific. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/S9EJGF/ WG126 Divesh Anuj PUBLISH Q8C3FK@@talks.osgeo.org -Q8C3FK “How to Draw, Urban Traffic Data?” — From Trip Lines to OD Matrices: Visualization Techniques for Traffic Simulation en en 20251120T143000 20251120T145500 0.02500 “How to Draw, Urban Traffic Data?” — From Trip Lines to OD Matrices: Visualization Techniques for Traffic Simulation Urban traffic flow is becoming increasingly complex, and it is difficult to grasp its meaning with only numerical data. In particular, there is a need for technical approaches to visually interpret simulation data that includes temporal and spatial patterns. Purpose: - To provide an intuitive and visual way of understanding complex urban traffic simulation results by implementing various spatial visualization techniques using open-source tools. This system utilizes spatial data based on GeoJSON and PostGIS to generate various visualization layers, such as vehicle movement trajectories (trip lines), OD matrices, heatmaps, and time-based density changes. Simulation results are visualized in 2D and 3D using OpenLayers and CesiumJS, respectively, and by expressing the same data differently in 2D/3D, interpretability and communication are enhanced. Key Features: - Trip line animations to trace vehicle movement - OD matrix visualization with directional flow arrows - Heatmap layers for density and congestion hotspots - Time-series based map layers with playback - Dual 2D/3D rendering and synchronized view interaction As a result, users can explore simulation data over time, analyze congestion patterns in specific areas, and intuitively identify complex OD patterns. All components are designed based on a microservice architecture, allowing scalability and integration with various simulation engines. This project focuses on implementing various visualization techniques using only open-source technologies and expressing complex traffic flows in a form that anyone can understand. Open Source Technologies Used: - **CesiumJS** for 3D spatial visualization and animation - **OpenLayers** for 2D WebGL vector layer rendering - **PostGIS** for spatial data storage and analysis - **GeoJSON** as the primary transport and visualization data format This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (RS-2024-00459703, Development of next-generation AI integrated mobility simulation and prediction/application technologies for metropolitan cities) PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/Q8C3FK/ WG126 Hansang Kim Sungjo MIN PUBLISH KFSC9R@@talks.osgeo.org -KFSC9R Staying on Track: How we built turn-by-turn navigation for bus drivers with FOSS and Open Data en en 20251120T153000 20251120T155500 0.02500 Staying on Track: How we built turn-by-turn navigation for bus drivers with FOSS and Open Data Public transport operators have long struggled to provide bus drivers with accurate navigation. While commercial navigation systems are optimised for general motorists, they fail to take into account bus-specific requirements like bus stops and bus specific routing. Many operators continue to provide drivers with primitive paper navigation guidance which is hard to interpret whilst driving. At AnyTrip, we've built a turn-by-turn navigation and real-time bus tracking app tailored specifically for bus operators and bus drivers. Powered using open data, open source tools and free & open source software, the solution integrates: • GTFS data from operators describing services, stops and pathing • OpenStreetMap as the base map and routing graph • Valhalla and OSRM for routing and maneuver generation • MapLibre Native and Protomaps for custom vector basemaps • React Native for cross-platform mobile development This talk will cover the architecture and design of the app, including: • How we transform GTFS static schedule data into navigable paths • Choosing between Valhalla and OSRM for different routing needs • Handling tricky edge cases like loops, u-turns and busways • Deployment on consumer-grade mobile devices for low-cost and ease of deployment • Dealing with connectivity constraints and supporting offline use We'll also share some lessons learned from real-world deployments. This is a practical, field-tested example of how open source geospatial tools can be used to solve real world transport problems. If you’re interested in GTFS, routing, or building geospatial apps for public good, this talk is for you. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/KFSC9R/ WG126 Ken Tsang PUBLISH F3ZL7C@@talks.osgeo.org -F3ZL7C Saving lives with GIS: engineering our open-source mapping stack en en 20251120T160000 20251120T162500 0.02500 Saving lives with GIS: engineering our open-source mapping stack Abley’s open-source mapping stack powers its SafeSystem suite of road safety applications and data APIs, helping US transportation agencies optimise investments and reduce deaths and serious injuries on their road networks. The suite delivers consistently fast performance and smooth user interaction, even under heavy data loads. The architecture comprises a PostGIS database, GeoServer (serving cached vector tile operational layers and managing security), MBTileserver for contextual data layers, and Nginx as a reverse proxy. Core road safety applications use MapLibre GL JS, plus a standalone API Explorer app enables seamless integration with desktop GIS tools via open standards like WMS and WFS. Docker Compose orchestrates the stack, ensuring consistent environments and enabling robust and scalable deployments, and simplifying testing and debugging. Topics discussed in this presentation include pros/cons of alternative architectures considered, techniques for securing GeoServer within Docker, challenges and solutions when integrating secure services with desktop GIS and contributing back to open-source communities. The presentation also outlines a pragmatic, agile engineering approach that balances requirements, stability and system security while avoiding speculative over-engineering . Spatial developers will gain insights into performant spatial data hosting and discover practical guidance for getting started with GeoServer, as well as vector tiles and integrating with desktop GIS. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/F3ZL7C/ WG126 Stacy Rendall PUBLISH YND7DA@@talks.osgeo.org -YND7DA Raster processing on HPC without coding? Sure! en en 20251120T163000 20251120T165500 0.02500 Raster processing on HPC without coding? Sure! Wrangling large and/or many raster datasets on a laptop or small workstation is no fun! Unfortunately, parallelising models or workflows to run on distributed memory compute clusters requires more than entry level coding skills. In this presentation I introduce the parallel extension to LUMASS' high-level visual raster processing framework. It enables the development of complex models and workflows without writing a single line of code! Check out this short playlist for getting a better idea: https://youtube.com/playlist?list=PL_CsDVZ4IPO-D87TgO0awddJGl2gUYIaD&si=siH6lT_3pwdN3Q-W LUMASS provides a range of processing components, including map algebra, zonal summaries, terrain attributes, and more. It uses GDAL for 2D raster I/O and NetCDF for data cubes. It supports SQLite-based raster attribute tables alongside any supported raster format. Furthermore, it enables the integration of any external command line program or script to be included into the pipeline to extend processing capabilities. Programmers find a Python interface for writing ‘moving window’ functions without having to worry about multi-threading or streaming, which comes out of the box! LUMASS is free and open-source software ( https://github.com/manaakiwhenua/LUMASS ). PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/YND7DA/ WG126 Alex Herzig PUBLISH H9KKF9@@talks.osgeo.org -H9KKF9 QField participatory mapping integration into Digital Earth Pacific open data cube environments for rapid co-creation of classification models en en 20251120T090000 20251120T092500 0.02500 QField participatory mapping integration into Digital Earth Pacific open data cube environments for rapid co-creation of classification models Introduction Earth and Ocean observation technologies have advanced rapidly over the past decades, becoming not only more detailed in terms of spectral and temporal coverage but also increasingly accessible for a wide range of users. Yet there have been ongoing barriers to the uptake and adoption of earth observation analytics needed to inform policy makers. Often these barriers have included complex and overly technocratic language and workflows whereby obstructing access and obscuring insights from satellite data. This has resulted in most analysis of satellite data being limited to academic and research oriented groups. Other options have recently emerged with the potential to support a wider range of users to gain access to insights from earth observations. However, many of these work workflows remain obscured due to technical and emerging cost barriers. Others may rely on specific packages and libraries that sometimes become deprecated overtime, reducing the overall long-term replicability of these workflows for wider users. Recent advancements in cloud computing infrastructure in the Pacific region have the potential to enable wider access for various users to access standardized and customised replicable workflows in the long term without cost. This paper highlights the datasets, analytical tools, computational capacity and insights made possible through Digital Earth Pacific (DE Pacific). This is a public technology infrastructure which has learned from the models of Digital Earth Africa, and Digital Earth Australia. Land cover monitoring: Land Use Land Cover (LULC) models shed light on the proportions and distributions of different natural and man-made environments across landscapes at given points in time. When multiple land cover model maps are generated for different points in time, the results can be analysed to detect changes in different land use and land cover classes over time. This analysis is commonly applied to the monitoring and management of a wide range of sectors including, but not limited to, forestry, agriculture, urban planning, infrastructure, water management and mining (Topuz and Deniz, 2023). Yet, there have been persisting challenges for machine learning approaches to meet thresholds of accuracy while generating LULC classification models at scale. Some of these challenges have included: Generating LULC models that provide an accurate prediction of land use and land cover distribution at the local scale (meeting accuracy assessment thresholds of X) Scaling of LULC models across diverse ecosystems and geographies while continuing to still meet accuracy assessment thresholds. Reconciling between local needs inputs including groundcover observation points and globally standardised LULC classes and models (for reporting purposes). There is no one ideal classification of land use and land cover, and it is unlikely that one could ever be developed. There are different perspectives in the classification process, and the process itself tends to be subjective, even when an objective numerical approach is used (USGS, 1976). Aims The aim of this paper is to provide an overview of the Digital Earth Pacific and some of its recent scientific benchmarking. This paper focuses on the used case of nationally driven land service for land cover machine learning classification models. This participatory workflow may be of interest to other stakeholders in the Pacific and more widely who are interested in replicating this for other use cases and sectors. In doing so, the paper may shed some light on best practices within the Pacific region for land use land cover model calibration and validation. The paper will also seek to highlight the long-term replicability of these open-source workflows that are not subject to pay walls or commercial platforms. The paper is also intended to raise greater awareness of the current datasets, regional products as well as the methods and workflows used in Digital Earth Pacific. 2. Materials and methods 2.1. Study area(s) Study areas included PICTs that have participated in past DE Pacific Land Cover Assessment Skills Transfer (LCAST) workshops: Tonga, Fiji, The Republic of the Marshall Islands (RMI), Palau, Tuvalu and the Cook Islands. 2.2. Data and processing 2.2.1. Satellite imagery and the Digital Earth Pacific GeoMAD Men of the analysis ready data products are made possible through the DE Pacific GeoMAD (Leith, forthcoming). 2.2.2. Participatory field data calibration and validation using QField QField is an open access and open source mobile application that is connected to quantum geographic information systems (QGIS) software. This mobile app allows for the collection of Geotagged data points, transects, polygons, and other field data features. In this use case, these data sets are crucial for the calibration and validation of machine learning land cover classification models. The participatory elements of these workflows are supported through open-source approaches. The country driven surveys involved use of QField to GPS points that provide a range of different values for the different land cover classes. There are six standardized land cover classes as defined by the IPCC in the Chapter 2 of Chapter 2: Generic Methodologies Applicable to Multiple Land-Use Categories. Through country-driven workshops and surveys, local participants are able to contribute to a spectral database that allows for the training of machine learning land cover models. There are options to collect more detailed classes, including other land uses and land cover types outside of the standardized six classes. By including Traditional Ecological Knowledge (TEK) there is also greater room for localization and customization of capabilities with greater room for local inputs into capturing more complexity in terms of Land Use and Land Cover (LULC) changes . A longer list of land cover classes can also be aggregated into a simpler list, including for compatibility with the IPCC classes. 2.2.3. Participatory post-processing This process involved collating, cleaning and validating all of the data sets collected through the field surveys. Participants were then able to ingest these datasets into Digital Earth Pacific through a Jupyter Notebooks environment to then run the random forest classification and other classifier models through SciKit-Learn libraries. The results are shared in this paper including: 1) LULC model results maps and tables 2) Models trained at a national-scale 3) Skills transfer for ongoing replication There are also areas of Intrinsic value and ongoing capacity building in the region. The paper also shows feedback on the results of pre-and post survey and workshop capacity building shared by the workshop and survey participants. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/H9KKF9/ WA220 Nicholas Metherall - SPC PUBLISH SNHT8D@@talks.osgeo.org -SNHT8D Unlocking the Treasure Trove of Government Data: How LINKS Veda is Advancing Data-Driven Governance en en 20251120T093000 20251120T095500 0.02500 Unlocking the Treasure Trove of Government Data: How LINKS Veda is Advancing Data-Driven Governance LINKS Veda is an initiative by MLIT to build a centralized and reusable data infrastructure by automatically structuring administrative information using AI technologies such as LLM, NLP, and image recognition. ### **Key features include:** - **Automated Structuring of Unstructured Data** Tasks traditionally done manually, such as organizing PDFs and Excel files, are now automated using AI technologies, significantly improving operational efficiency and data usability. - **No-Code Data Processing** Enables intuitive operations using data processing templates, even for non-specialists. It also promotes the reusability and continuous improvement of structured processes. - **Implementation Through EBPM and Open Data** Facilitates quantitative analysis for policy evaluation and operational improvement, with use cases spanning areas such as regional tourism trends, logistics, and productivity. - **Support for Geospatial Data** Outputs in GeoJSON/CSV formats enable high compatibility with GIS tools and web applications for effective data visualization. This talk will share real-world use cases to illustrate how previously untapped government data is being transformed into a vital resource for open innovation. ### **Who Should Attend:** - Government and municipal officials interested in digital transformation and data infrastructure - Researchers and think tank professionals involved in EBPM - Data engineers, GIS developers, and no-code/low-code developers - Startups and corporate representative working on projects that utilize open or public data PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/SNHT8D/ WA220 Kazuma Tsuchiya PUBLISH ABAUC9@@talks.osgeo.org -ABAUC9 Utilizing Free and Open-Source Software to Better Share Local Data for Improved Community Decision Making en en 20251120T100000 20251120T102500 0.02500 Utilizing Free and Open-Source Software to Better Share Local Data for Improved Community Decision Making Recognizing the need for improved access to data vital for addressing vexing community challenges, Iowa State University Extension and Outreach’s Community and Economic Development (CED) unit developed the Community Indicators Program in 2013 and the Data Science for Public Good (DSPG) Young Scholars summer outreach program (https://dspg.iastate.edu/) in 2020. These programs have been successful at producing and sharing demographic, economic and other state data through the curation of meaningful and timely data and informative publications and dashboards. The programs have also worked with communities to utilize the Community Learning through Data Driven Discovery (CLD3) https://cld3.org/our-approach/ framework to address local issues and provide local stakeholders with the ability to make data-supported decisions. While these efforts have been a good start to improve issue awareness and decision-making in smaller and rural communities, many of the indicators that are informative measures in larger communities are not readily available or are at a granularity that is not suitable for local decision-making. Additionally, access to software and the skills to operate this software can be a barrier to working with this data once it is identified. To address this issue, a program focused on Science Education and Workforce Development was piloted in 2024. This program prioritized 1) Increasing local capacity for data literacy 2) Increasing technical skills to access data 3) Increasing ability to share local data. The program developers recognized the need for utilizing Free and Open-Source software to limit the cost and access barriers to visualizing and sharing local data with the community. Software skills are developed though hands-on-training using local data examples. The current suite of free and open-source software is accessible and scalable to meet the needs of many small communities. The software included in the program consists of QGIS, GeoJSON.io, Google Sheets, GitHub, and Tableau Public. Used together, this software allows participants to easily make their local community data publicly available at little to no cost. Examples of some of the data projects include: the number of recurring community events, use frequency of park shelter rentals, number of volunteer organizations, number of youth/adult parks and recreation participants, attendance rates at council/supervisor meetings, number of permits for home improvements, library check outs, or even the number and spatial location of trees planted by the city. During this presentation, the presenter will share several of these examples demonstrating how this software can be used by those new to data science or geospatial technology. Additionally, the presentation will include some techniques that can be used to increase the utility and delivery of spatial data visualizations created with these tools. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/ABAUC9/ WA220 Christopher J. Seeger PUBLISH ACSJKW@@talks.osgeo.org -ACSJKW PyForestScan: A Python Library for Large-Scale LiDAR Forest-Structure Metrics en en 20251120T110000 20251120T112500 0.02500 PyForestScan: A Python Library for Large-Scale LiDAR Forest-Structure Metrics Airborne LiDAR now blankets landscapes with up to sub-centimeter-scale detail. Yet, researchers and land managers have lacked a fully open, Python-native workflow to transform billions of points into actionable information on forest structure. Existing solutions are either proprietary or anchored in R, leaving the rapidly growing Python geospatial stack without a scalable counterpart. PyForestScan bridges this gap. Built on PDAL’s streaming I/O, the library reads traditional LAS/LAZ alongside hierarchical octree formats such as COPC and EPT, tiles point clouds automatically, and exports GeoTIFF layers of canopy height, canopy cover, foliage-height diversity (FHD), plant-area density (PAD), plant-area index (PAI), and digital-terrain models in a single command. We demonstrate these capabilities by generating island-wide canopy height, canopy cover, FHD, and PAI mosaics for Hawai‘i Island - an area with over 1 million hectares and over 1.8 billion points, in under 24 hours of wall time on commodity hardware, producing multi-resolution (1 m–30 m) grids ready for carbon accounting, biodiversity assessment, and geoAI training. The talk will outline PyForestScan's architecture, highlight performance benchmarks, and invite contributions via its open governance model, Docker images, and CI-tested notebooks. By placing robust LiDAR analytics squarely within the free and open Python ecosystem, PyForestScan equips the FOSS4G community to move seamlessly from raw point clouds to island- and continent-scale forest insights. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/ACSJKW/ WA220 Joseph Percival PUBLISH 8D7WZ8@@talks.osgeo.org -8D7WZ8 Spatiotemporal Analysis of Forest Disturbance Dynamics in Maharashtra Using Remote Sensing Techniques en en 20251120T113000 20251120T115500 0.02500 Spatiotemporal Analysis of Forest Disturbance Dynamics in Maharashtra Using Remote Sensing Techniques Abstract Forests in Maharashtra are undergoing significant changes due to a combination of natural and anthropogenic factors. This study employs multi-temporal remote sensing data from 2014 to 2024 to analyze forest disturbances across the state. By integrating vegetation indices like NDVI and NDWI along with Land Surface Temperature (LST), we map disturbance hotspots and quantify temporal forest change. The results highlight key patterns in forest degradation and provide valuable insights for conservation planning and sustainable land use policies. Forests are vital for maintaining ecological balance, regulating climate, and supporting biodiversity, and Maharashtra, located in western India, hosts diverse forest types ranging from dry deciduous to moist deciduous, particularly in the Sahyadri (Western Ghats), Satpura, and Vidarbha regions; however, these ecosystems are increasingly under pressure from urbanization, infrastructure expansion, mining, logging, and climatic stress such as droughts and rising temperatures. In this study, forest disturbances across Maharashtra were assessed over a decade (2014–2024) using spatiotemporal remote sensing data and open-source platforms, where Landsat imagery was primarily used for the pre-2017 period and Sentinel-2 data was incorporated after its availability, thereby ensuring temporal continuity; to address the differences in spatial resolution (30 m for Landsat vs. 10–20 m for Sentinel-2), all datasets were harmonized to a common scale through resampling techniques to ensure comparability across time. Vegetation indices including NDVI (for greenness) and NDWI (for vegetation moisture) were computed from both datasets, while MODIS-derived Land Surface Temperature (LST) was integrated to capture thermal anomalies, and preprocessing steps included cloud masking, generating seasonal composites (pre-monsoon, monsoon, and post-monsoon), and applying zonal statistics at the district level. A composite Forest Disturbance Index (FDI) was developed by combining NDVI, NDWI, and LST trends, with thresholds applied to detect significant vegetation decline alongside thermal rise, and temporal anomalies were analyzed using Mann-Kendall and Sen’s slope methods. Results revealed widespread declining trends in NDVI and NDWI, particularly in forest-rich districts such as Gadchiroli, Chandrapur, and Thane, where vegetation decreased by 5–15%, with sharper drops during dry seasons, while LST rose by 1.5–2.2°C in disturbed patches. Spatial patterns showed fragmentation in the Western Ghats due to encroachment and road development, recurrent disturbances in Vidarbha linked to mining and shifting cultivation, and localized degradation in Marathwada, where seasonal stress appeared not only from forested patches under drought but also due to edge effects from agricultural expansion adjacent to forests, leading to canopy thinning and conversion pressures. Seasonally, pre-monsoon months exhibited maximum stress and fire risk, monsoon months showed vegetation recovery, and post-monsoon periods marked the re-emergence of degradation signals. These disturbances were driven by anthropogenic pressures such as urban growth, illegal logging, and mining, compounded by climatic anomalies and occasional natural events like cyclones and landslides in the Ghats. Overall, the integration of multi-source satellite datasets, after careful harmonization, proved effective in detecting disturbance hotspots and tracking temporal changes, and the findings underscore significant degradation in Maharashtra’s forests over the past decade, highlighting the urgent need for early detection, mitigation, and informed conservation planning by policymakers and forest managers. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/8D7WZ8/ WA220 Komal Rai PUBLISH KDTE9Z@@talks.osgeo.org -KDTE9Z Identifying Forest Invasive Species in Fiji and Tonga Using Machine Learning en en 20251120T120000 20251120T122500 0.02500 Identifying Forest Invasive Species in Fiji and Tonga Using Machine Learning Introduction In the Pacific, the spread of invasive species has been a result of direct anthropogenic impacts of land use modifications as well as indirect anthropogenic impacts of natural occurrences like tropical cyclones. Such disturbances have accelerated the spread of invasive species, particularly over degraded and exposed landscapes, by clearing the land and changing it. Data were collected via GPS surveys in the field to identify confirmed invasive species sites, which were then processed with time-series satellite data over Digital Earth Pacific. Phenological characteristics and seasonal trends in plant vegetation were used to train the models to detect and track invasive species over an extent of time. The results demonstrate the feasibility of region-wide, large-scale monitoring of invasive plant species. The methods are a valuable tool to interpret spatial invasion patterns since 2017 until the present moment, contributing to more accurate ecosystem management and informing policy responses to land degradation and biodiversity loss across PICTs. Two of the key objectives of the project are to assess two high-priority invasive species: Spathodea campanulata (also referred to as African tulip tree) and Cordia alliodora (also referred to as Cordia, Salmwood, or Spanish Elm). They are targeted since they are extensively found and ecologically affecting native forests in both countries. Other invasive plant species, such as Leucaena leucocephala, Merremia peltata (Cook's Glory), Hevea brasiliensis (Para Rubber tree), and Acacia mangium (Black Wattle), have been identified as secondary problems in several Pacific Island Countries and Territories (PICTs). Their invasion is a daunting task due to the intensity and speed of invasion. This study proves the use of Earth observation technology and machine learning for mapping and monitoring the invasive tree species distribution across the Pacific. Methodology Field surveys were conducted using QField, an open-source mobile GIS that is integrated with QGIS, to collect georeferenced data on invasive species in Fiji and Tonga. Collecting the data was done through filling out custom forms to input date, species, landcover category, and location with line-of-sight mapping or locked GPS points, using high-accuracy devices (e.g. TDC 650), with accuracy ranging from 3 m to sub-metre. To complement model training, data on non-native species and other vegetations were also obtained to be utilized as machine learning (ML) classification training data. Upon deployment in the field, the data were compiled into a geodatabase, cleaned, validated, and overlaid on Sentinel-2 satellite Geo-Median composites (10 m resolution, 2024). These vector training points were loaded into the Digital Earth Pacific platform and used to sample Sentinel-2 image spectral and vegetation index values including NDVI, EVI, SWIR, chlorophyll index, and other band ratios. The data were then used to train a Random Forest machine learning model that was initially tested against small parcels and continuously improved. Phenological data (e.g., blooming period) from local witnesses informed seasonal satellite image filtering to optimize classification accuracy. STAC architecture enabled multi-sensor data integration and simplified access to spectral features. Validation for accuracy was conducted in collaboration with indigenous forestry and botany experts. Their input improved model reliability and contextual validity. Further ground-truthing was used to validate results and collect more data, further calibrating model projections. Once acceptable accuracy had been achieved, the workflows were reconciled into Python notebooks and disseminated to Forestry Ministries for further use and replication. Preliminary Results The attempts at classifying invasive species in Tonga and Fiji used machine learning (ML) models that were trained from ground-collected data and Sentinel-2 satellite images. Preliminary classifications in the Toloa Forest of Tonga overestimated the amount of Cordia salmwood due to unbalanced training data and the lack of seasonal filtering. Improvements in the third iteration enhanced the detection of Cordia, particularly at forest edges. Atele Forest model using the Toloa-trained classification identified African Tulip spread without ground points, verifying the prediction capability of the model. In Fiji, there were some forests that had widespread invasive species. Wainibuka Forest had a 43% African Tulip cover, aided by cyclone seed dispersal. Nadarivatu and Korotari Forests had widespread Cordia Alliodora invasions from previous plantations, with Korotari also having 40% African Tulip. Lololo and Bua Forests, both owned by Fiji Pine Limited, had widespread Acacia mangium invasions in Bua with 77% Acacia cover, hindering pine regeneration Capacity building was a main component in place to offer sustainability. There were two workshops held in Tonga with the Ministry of Agriculture, Food, and Forests. The initial one trained officers in QGIS and QField for data collection. The second workshop demonstrated ML principles through ground-truth points and Sentinel-2 images, and hands-on Python sessions. Participants collected additional Cordia and African Tulip data to improve model accuracy. In Fiji, a compressed one-week workshop in Nadi engaged 15 individuals from forestry, agriculture, and land management. Training was conducted on QGIS, QField, and ML processes using Sentinel-2 data. Booklets were provided to participants with guided activities so that they could replicate invasive mapping in their local area. Overall, these efforts strengthened national capacity to detect, monitor, and manage invasive plant species using advanced geospatial technologies and participatory learning approaches. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/KDTE9Z/ WA220 Nicholas Metherall - SPC Elenoa Biukoto PUBLISH GLLESU@@talks.osgeo.org -GLLESU Network Analysis at the continental scale, determining new measures for accessibility. en en 20251120T133000 20251120T135500 0.02500 Network Analysis at the continental scale, determining new measures for accessibility. The Centre for Australian Research into Access (CARA) has been utilising the fast-calculating methods of the Pandana Python library in combination with Geopandas methods to perform continental-scale network analysis. CARA's objective is to provide address-level accessibility calculations for health and education with a particular focus on rurality, to assist academic researchers and to inform policymakers. The work we are producing is a result of an Australian Research Council Linkage Infrastructure, Equipment and Facilities (LIEF) grant, which has recently finalised building a spatially detailed infrastructure for a more equitable nation, representing a digital twin for modelling the patterns and processes impacting the Australian population. CARA has adapted open-source Python libraries, file formats, and data to develop network analysis methods that operate on continental scales that have previously been too costly to process. The Pandana open-source Python library enables fast network calculations to find shortest paths using contraction hierarchies (Foti & Waddell 2012). We have used methods from Pandana to perform ‘number of nearest’ calculations (n-nearest), matrix calculations, and catchment area calculations for both time and distance impedances. Calculations have been carried out using origin data for approximately 10.6 million residential addresses across Australia to various health and education destination datasets. Spatial methods from the Geopandas library were also used to account for the times and distances between origin/destination points and the network, and also to aggregate outputs to spatial units such as those in the Australian Statistical Geography Standard (ASGS). Outputs from our n-nearest network calculations using the Pandana nearest_pois method (3.7 million nodes, 7.8 million edges, two impedances, and 7,073 destinations) have been completed in less than 3.5 minutes. Outputs from our matrix and catchment network calculations using the Pandana shortest_path_lengths method have fluctuating run times due to the varying number of input origin and destination points with each calculation. However, they are robust in handling a large number of inputs. These methods have recently been adapted to use Overture Maps data that will allow them to be applied to a wide variety of countries throughout the world. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/GLLESU/ WA220 Sam Quinsey PUBLISH REYQCT@@talks.osgeo.org -REYQCT From Raw to Ready: Rapid Sentinel-2 Index Workflows for Time-Sensitive Use Cases en en 20251120T140000 20251120T142500 0.02500 From Raw to Ready: Rapid Sentinel-2 Index Workflows for Time-Sensitive Use Cases This presentation explores the urgent recovery of missing Sentinel-2 index data using the ESA UTM tiling grid. It details the preprocessing pipeline, including cloud and shadow masking, null-value extraction, and optimized insertion into geospatial databases. Emphasis is placed on a real-world agriculture use case—monitoring crop health during a narrow seasonal window—where fast, accurate index data is critical for timely decision-making. Attendees will gain insights into handling remote sensing data under pressure, including code examples and workflow strategies tailored for rendering the recovered index data as CovJSON, enabling efficient visualization and analysis within agricultural decision-support systems. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/REYQCT/ WA220 Siriwat Suttipanyo Natpakal Maneerat PUBLISH CPM8TS@@talks.osgeo.org -CPM8TS From Thousands to Seamless: The Power of Virtual Tilesets in Thailand's 3D Landscape en en 20251120T143000 20251120T145500 0.02500 From Thousands to Seamless: The Power of Virtual Tilesets in Thailand's 3D Landscape This presentation explains how large-scale 3D geospatial data for Thailand is managed efficiently. It covers the dataset’s scale and the reasoning behind organizing data using Google’s S2 grid system to improve management and processing. The talk explores the differences between using a single tileset versus multiple tilesets, and how virtual tilesets enable the efficient combination of thousands of 3D assets. Attendees will gain insights into the data and the key benefits of virtual tilesets, including enhanced performance, flexibility, and improved 3D visualization quality. Whether your work involves urban planning, disaster management, or geospatial mapping, this session offers practical strategies for handling complex 3D geospatial datasets. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/CPM8TS/ WA220 Siriwat Suttipanyo Siriya Saenkhom-or PUBLISH UNCDTV@@talks.osgeo.org -UNCDTV From Data to Decisions: Harnessing Open Geospatial Data and GeoAI for Urban Predictive Analytics en en 20251120T153000 20251120T155500 0.02500 From Data to Decisions: Harnessing Open Geospatial Data and GeoAI for Urban Predictive Analytics The emergence of open geospatial data from governmental sources regulated by Hong Kong’s open data policy (formalized in 2018) has transformed the methodology for geospatial analysis and predictive modeling. This presentation will highlight the importance of open geospatial data (CSDI Portal) and introduce how innovative projects could leverage the open data to develop near real-time predictive models and analyses using Artificial Intelligence of Things (AIoTs) and Geospatial Artificial Intelligence (GeoAI) technology. Utilizing a variety of open-source libraries and tools for machine learning algorithms, integrating both static and dynamic datasets through API functionalities, to enhance the understanding of urban dynamics in GeoAI applications. The showcased innovative projects will illustrate how AIoTs and GeoAI techniques can provide actionable insights for urban planning and public health initiatives. Furthermore, the outputs of these projects will be shared publicly through a map-based dashboard, allowing users and the general public to interact with and visualize the output in real time. Participants will gain insights into the methodologies employed, the challenges faced, and the implications of the findings for smart city development. This presentation hopes to demonstrate the efficacy of open geospatial data and open-source technologies in fostering collaboration and innovation in geospatial research, ultimately contributing to more resilient and adaptive urban environments. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/UNCDTV/ WA220 Paulina Wong PUBLISH 7Z3DTA@@talks.osgeo.org -7Z3DTA Awesome Cloud-based Open-source GIS apps from RMIT students en en 20251120T160000 20251120T162500 0.02500 Awesome Cloud-based Open-source GIS apps from RMIT students This presentation shares the experience of designing and delivering a university course centered on Scaffolded Project-Based Learning (SPL) in the context of cloud-based open-source GIS. Drawing from Cloud-based Open-source GIS Solutions at RMIT University, the session highlights how SPL can be used to equip students with both foundational geospatial knowledge and cutting-edge technical skills, aligning their learning with the demands of a rapidly evolving geospatial industry. Cloud-based, open-source GIS represents an emerging paradigm in geospatial science. It goes beyond teaching students to operate individual tools by emphasizing GIS as a system for spatial thinking and decision-making. Through the SPL framework, students are guided from being tool users to becoming system designers and tool builders. The course structure integrates theory, new technologies, and iterative, scaffolded practice to help students design cloud-native geospatial solutions to real-world challenges. At the core of the course is a semester-long, individual project in which students conceptualize, develop, and deploy a functional GIS application. These projects are not just technical exercises—they are opportunities to design systems that perform spatial analysis, support decision-making, and address practical challenges such as urban heat islands, flooding, environmental degradation, and spatial inequality. Students use open-source tools and platforms such as PostGIS, QGIS, GitHub, Cesium Ion, Earth Engine Apps, Mapbox, Leaflet, and GeoAI frameworks, while learning deployment strategies using cloud infrastructure and serverless technologies. The SPL pedagogy ensures students build confidence and competence through structured phases: Early-stage assignments introduce essential tools, spatial data formats, and cloud workflows; Mid-semester milestones guide system architecture, backend/frontend integration, and data sourcing; Peer feedback and mentoring support project refinement and encourage collaborative learning. This pedagogical strategy fosters not only technical fluency but also critical thinking about how to design geospatial systems that are scalable, interoperable, and user-focused. It reinforces the understanding that spatial technologies are most powerful when they are purposefully assembled to solve complex problems—something increasingly demanded by industry, government, and community sectors. The presentation will feature lightning talks by five students whose applications reflect creativity, technical rigor, and engagement with real-world issues. While the applications are in progress, each talk will emphasize the problem addressed, the open-source stack used, and the design decisions made. This format aims to share process over product—highlighting learning outcomes, problem-solving strategies, and reflections on using open-source technologies in a cloud-native environment. Additionally, this session will present a broader reflection on how scaffolded project-based learning can transform geospatial education. It will explore questions such as: How can SPL help students move from passive users to active developers of spatial solutions? What competencies are most critical for graduates entering cloud-native geospatial workforces? How can open-source and academic communities better support each other in this transition? Preliminary evaluations of the course indicate that this approach promotes student engagement, deepens understanding of geospatial systems architecture, and encourages students to see themselves as contributors to the open-source ecosystem. Several students have expressed interest in continuing their projects post-course or using them as part of a professional portfolio. Real-world case studies—including the development of virtual flooding digital twins and visualizations for climate resilience—illustrate the alignment between classroom learning and industry needs. These examples demonstrate how SPL, combined with cloud-native open-source GIS, offers a scalable and adaptable model for preparing students to work at the intersection of spatial science, digital technologies, and societal challenges. By sharing the design, philosophy, and outcomes of this course, the presentation aims to contribute to broader discussions on how to teach the next generation of geospatial professionals in ways that are future-ready, open, and grounded in systems thinking. It will be of particular interest to educators, developers, curriculum designers, and anyone engaged in building capacity in the FOSS4G ecosystem. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/7Z3DTA/ WA220 Qian (Chayn) Sun Ryan Turner Wenhui CAI Shinjita Das PUBLISH MVXVTC@@talks.osgeo.org -MVXVTC Ploughing the Digital field: Redefining farming with Customer-Centric Digital Design en en 20251120T163000 20251120T165500 0.02500 Ploughing the Digital field: Redefining farming with Customer-Centric Digital Design In the rapidly evolving world of agriculture, the integration of digital technology has become imperative for advancing productivity, sustainability, and customer satisfaction. Join us to discover how placing farmers at the heart of digital innovation is revolutionizing agriculture, making it more responsive, efficient, and sustainable. Our presentation will show how customer-centric design principles can be applied to create digital mapping solutions that directly address the challenges faced by farmers. By prioritising the end-user experience, we've ensured that the tools are not only effective, but also intuitive and accessible for end users with varying levels of digital literacy. We will explain the journey we went through to identify and analyse the specific pain points and requirements of farmers, to both provide solutions to their needs, and drive towards a leading, simple customer experience. The session will also provide insights into how we blended proprietary GIS capabilities with open-source technologies to deliver a seamless, customer-focused experience. We’ll discuss how we integrated ESRI Maps technology within a React web application, incorporated ArcGIS Enterprise, and implemented performance and error monitoring to support a stable, responsive user experience. We found that by focusing on the real needs and challenges of farmers, we can create digital solutions that are not only technologically advanced but also practical, user-friendly, and capable of delivering tangible benefits in terms of efficiency, productivity, and sustainability. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/MVXVTC/ WA220 Jamie Sherriff PUBLISH 88WPNL@@talks.osgeo.org -88WPNL DigiAgriApp: development of a free and open source app for digital agriculture en en 20251120T090000 20251120T092500 0.02500 DigiAgriApp: development of a free and open source app for digital agriculture **DigiAgriApp** is a free and open-source client-server application built on PostgreSQL/PostGIS, Django (backend), and Flutter (frontend), with an integrated QGIS plugin. The development was started and carried out by the Digital Agriculture Unit of the Fondazione Edmund Mach with the support of a small number of companies. Over the past year, the platform has consolidated its architecture and expanded its features to better support precision agriculture. Its primary focus is the monitoring of agricultural fields and its sub-elements: subfields, rows, until individual plants. A major advancement was the integration of the Pest Patrol module, which leverages computer vision algorithms to automatically analyze images from chromotropic traps and identify insect vectors such as Scaphoideus titanus and Orientus ishidae. This sets the stage for a robust decision support system for pest and disease management. In addition to ongoing development work, significant efforts were made toward multilingual support—DigiAgriApp is now fully available in four languages—and in project outreach, seeking collaborators and institutions interested in adopting or contributing to the platform. A major milestone in 2025 has been the launch of two public instances of the DigiAgriApp server, both released with the support of the Fondazione Edmund Mach. One instance, in Italian (https://digiagriapp.fmach.it), is tailored to viticulture and fruit production. The second, in English (https://digiagriapp-en.fmach.it), offers broader crop support and allows the addition of new species. These public instances are limited in functionality, as they currently do not provide access to remote sensing or weather station data. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/88WPNL/ WG404 Luca Delucchi PUBLISH M8UM98@@talks.osgeo.org -M8UM98 Building an Ocean Data Ingestion and Discovery system with Open Source Software en en 20251120T093000 20251120T095500 0.02500 Building an Ocean Data Ingestion and Discovery system with Open Source Software The new Australian Ocean Data Network system which has been built over the last three years uses a host of open source technologies including: - Elastic Search and Geonetwork for metadata management and search - Zarr, Parquet and NetCDF for data storage and retrieval - Prefect and dask for data ingestion and optimisation pipelines - Terraform for infrastructure management - SpringBoot and FastAPI for backend apis following modern OGC standards - React for frontends - Jupyter Notebooks for data access tutorials for advanced users Our open data discovery portal launches in November 2025. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/M8UM98/ WG404 Alex McKeown PUBLISH HA8UPS@@talks.osgeo.org -HA8UPS Surveys for Infrastructure Resilience and Geospatial Exposure: Applying Open-Source Tools and Methods in the Pacific. en en 20251120T100000 20251120T102500 0.02500 Surveys for Infrastructure Resilience and Geospatial Exposure: Applying Open-Source Tools and Methods in the Pacific. This presentation will showcase the innovative application of open-source geospatial and participatory methods in the "National surveys for infrastructure resilience geospatial databases to support exposure and hazard modelling for Kiribati, Vanuatu, and Tonga" project. This project addresses urgent needs in the Pacific, in terms of the lack of comprehensive, up-to-date data on buildings, roads, and critical infrastructure, and the need for robust hazard and exposure models to inform disaster risk reduction and climate adaptation. Through extensive field surveys, local teams from various government ministries were trained and supported by SPC, with the assistance of existing technical staff in the country, to collect detailed geotagged data on infrastructure assets, buildings, and information on roads, bridges, and utilities. These datasets are validated, cleaned, and uploaded to the country level or regional open-source geodatabases, ensuring accessibility and long-term sustainability. Open-source tools, including QGIS, QField, and Kobo Toolbox, were utilized for data collection. The approach emphasizes capacity building, with a strong focus on gender equality and social inclusion. The data collected and the results of the geospatial databases and hazard models (for cyclone, floods, sea-level-rise) directly support the government in planning resilient infrastructure, developing early warning systems, and advocating for loss and damage funding. By leveraging open-source methods, the project promotes sustainable, scalable, and locally adaptable approaches to resilience planning in the Pacific. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/HA8UPS/ WG404 Merelita Lewabete PUBLISH QTSDAP@@talks.osgeo.org -QTSDAP Research on the Display of Ultra-Large Point Cloud Data Using a 3DWebGIS Distributed Rendering System en en 20251120T110000 20251120T112500 0.02500 Research on the Display of Ultra-Large Point Cloud Data Using a 3DWebGIS Distributed Rendering System Introduction Recently, many software applications have transitioned to cloud-based platforms, with web browsers serving as the primary user interface. In the field of geographic information, 3DWebGIS applications have also gained widespread adoption in both commercial and open-source software, thanks to the ease of 3D rendering on browsers using WebGL. However, web browsers have limitations on the heap memory available to user programs, which can cause instability or crashes when rendering large-scale 3D data that consumes a significant amount of memory. The scalable display system we propose, ChOWDER, enables the distributed rendering of 3DWebGIS across multiple displays (web browsers) by utilizing iTowns [1], an open-source 3D WebGIS as middleware. This mechanism breaks through the heap memory limit of web browsers, offering the advantage of ultra-high-resolution display. The method for distributing and rendering a massive amount of 3DTiles-format building polygon data across a tiled display composed of nine 4K displays using ChOWDER, along with its actual memory consumption distribution performance, was reported at FOSS4G ASIA 2024 [2]. In this presentation, we introduce a method for converting cloud data captured by the weather satellite over the Pacific Ocean into approximately 500 million 3DTiles-format 3D point cloud data and then distributing and rendering this data at ultra-high resolution across multiple displays (browsers) using ChOWDER. Additionally, we discuss the current challenges that arise from unnatural artifacts in rendering for earth-sized point clouds and propose solutions for this approach. Proposed method Japan's weather satellite HIMAWARI uses multiple optical sensors to capture images of half of the globe, centered on the Asia-Oceania region, at 10-minute intervals. The grid data, which has undergone precise geometric correction, is publicly available from the Solar Radiation Consortium [3]. The publicly available grid data includes not only raw data but also calculated data such as cloud top height and cloud thickness, derived from the raw data. Using these, we define voxels within each grid, with the cloud top height as the maximum height and the height obtained by subtracting the cloud thickness from the cloud top height as the minimum height, by randomly generating multiple points within these voxels, we represent the 2D observation data as 3D cloud shapes in point cloud data. This converter program was developed as an open-source program distributed on GitHub [4]. The generated point cloud data is converted to a binary LAS format using txt2las in LAStools [5] and then converted to 3DTiles using py3dtiles [6]. In this study, satellite-captured data at 00:00 UTC on October 10, 2019, were used. The generated 3DTiles data has an 8-level octree structure, with a total data size of approximately 6.1 GB. This data is of a size that can be visualized using standard 3DWebGIS such as CesiumJS. However, when displaying the entire area on a browser in a standard desktop environment, i.e., a display with a maximum resolution of approximately 4K, the point cloud is displayed with coarse granularity. To observe the detailed structure of the clouds, it is necessary to zoom in, making it impossible to view the entire area and observe details simultaneously. However, by using ChOWDER, it is possible to distribute the rendering of a 3DWebGIS across multiple displays (browsers), enabling ultra-high-resolution display that cannot be achieved on a single display. An example of the display is shown in Figure 1 (https://github.com/SIPupstreamDesign/ChOWDER/blob/master/IMG_2998.jpg). Challenges and Discussion When visualizing this data, triangular artifacts are observed, as shown in Figure 2 (https://github.com/SIPupstreamDesign/ChOWDER/blob/master/IMG_2995.jpg). This is due to the following reasons. The primary reason is that the coordinate system of the generated point cloud is a Cartesian coordinate system with the Earth's center as the origin. Since the point cloud covers nearly half of the Earth's surface, converting it into 3DTiles in a single batch results in bounding voxels that encompass approximately half of the Earth's surface. When this is divided into an Octree structure, the voxels are split along orthogonal coordinate axes. This results in triangular-shaped intersections between the split voxels and the Earth's surface at certain locations. Additionally, in 3DWebGIS, there is a phenomenon where tiles corresponding to the zoom level to be displayed do not render for some reason. A similar issue occurs with 3DTiles data, and in this case, it manifests as triangular-shaped areas. Typically, 3DTiles are generated for relatively limited areas, and the geographical coordinate system is used, so the bounding voxels follow the shape of the Earth's surface. Even if rendering artifacts occur, they do not appear. Based on the above considerations, when converting Earth-scale data, such as the cloud data in this study, to 3DTiles, it is expected that applying the method of pre-dividing the data into multiple regions using the geographical coordinate system and performing 3DTiles conversion for each region can suppress the occurrence of unnatural artifacts. Future work We will verify whether the method proposed in the previous section, which involves pre-dividing the area during 3DTiles generation, can suppress the occurrence of unnatural artifacts. Additionally, while we currently estimate the 3D structure of clouds based on 2D observation data, the next-generation weather satellite is expected to provide 3D observation data from an infrared sounder, enabling a scientifically accurate, high-resolution display of 3D structures. References [1] iTowns, providing 3D geospatial data management and visualization software: https://github.com/iTowns [2] KAWANABE, T., et al. On the Performance of Distributed Rendering System for 3DWebGIS Application on Ultra-High-Resolution Display. International Journal of Geoinformatics, 2025, 21.1: 15-25. [3] HIMAWARI 8/9 gridded full-disk (FD)data Version 02 (V20190123) release note: https://www.cr.chiba-u.jp/databases/GEO/H8_9/FD/index_en_V20190123.html [4] Point cloud converter program: https://github.com/SIPupstreamDesign/ChOWDER/blob/master/server/parser/amaterass_convert_test.js [5] LAStools: https://lastools.github.io/ [6] py3dtiles: https://py3dtiles.org/ PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/QTSDAP/ WG404 Tomohiro KAWANABE PUBLISH KZPVUC@@talks.osgeo.org -KZPVUC Icechunk 2.0 en en 20251120T113000 20251120T115500 0.02500 Icechunk 2.0 [Icechunk](https://icechunk.io/) is an open-source, cloud-native transactional storage engine for multi-dimensional arrays, designed to manage massive geospatial datasets. Building upon the foundational features of data versioning and schema evolution presented in its first iteration, Icechunk 2.0 introduces a more stable and performant on-disk format that unlocks powerful new capabilities for managing large-scale array data. This presentation will introduce the new features of Icechunk 2.0 and demonstrate their application to common challenges in geospatial data analysis. Managing petabyte-scale geospatial data, such as satellite imagery time-series, gridded weather forecasts, and climate model outputs, requires tools that can handle evolving data and complex operational pipelines efficiently. Icechunk 2.0 directly addresses these needs with several key innovations: - **Efficient Array Manipulation:** A new indexing capability allows for cheap appends, prepends, and inserts into an array without rewriting existing data chunks. This is transformative for managing growing time-series datasets. - **Flexible Data Organization:** Users can now rename or move arrays and groups within the [Zarr](https://zarr.dev/) hierarchy without costly data duplication, simplifying the curation and organization of large data repositories. - **Enhanced Data Governance:** The introduction of an amendable commit option simplifies the version history, while a comprehensive operation log and support for repository-level metadata provide crucial data provenance. - **Improved Performance and Stability:** The new format enables significantly faster and safer garbage collection and more efficient queries of a repository’s history. We will demonstrate how Icechunk 2.0 integrates seamlessly into the Scientific Python ecosystem ([Xarray](https://xarray.dev/), [Dask](https://www.dask.org/)) via its Zarr store interface and how we have built Icechunk support into the managed [Earthmover platform](https://earthmover.io/platform). Through real-world examples, we will showcase how these new features can be used to build robust, high-performance data pipelines for cloud-based geospatial analytics and machine learning. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/KZPVUC/ WG404 Joe Hamman PUBLISH WLGPSY@@talks.osgeo.org -WLGPSY UGRID and you - an unstructured journey en en 20251120T120000 20251120T122500 0.02500 UGRID and you - an unstructured journey Not for academic track. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/WLGPSY/ WG404 Bryan Hally PUBLISH ETJADY@@talks.osgeo.org -ETJADY Knowledge sharing and boundary conditions within the virtual community: an examination of free and open-source communities en en 20251120T133000 20251120T135500 0.02500 Knowledge sharing and boundary conditions within the virtual community: an examination of free and open-source communities Introduction The purpose of this paper is to examine knowledge-sharing practices among professionals in the free and open-source software community. It emphasizes the concept of knowledge community by applying Bona Fide Group Theory, and explores the mechanism of knowledge sharing across boundaries within the community. Following a cross-cultural perspective, this research identified several key contextual and social factors that influence knowledge sharing and community development within the FOSS4G community. Theoretical framework This research project adopts the Bona Fide Groups Theory (Putnam & Stohl, 1990) which features two elements of small groups, including permeable boundaries and group interdependence within its contexts. It provided a framework for understanding the interactions and collaborations within naturally formed groups with special characteristics, such as fluctuations in group member commitment and a shared sense of boundaries (Putnam & Stohl, 1996; Frey, 2003; Stohl & Putnam, 2005). Built upon this framework, this research delineates the mechanism of knowledge sharing and how the knowledge transfer practices impact member identification within the free and open-source community. Particularly, it examines the impact of recent advancements in AI technology on daily communication and collaboration within the community, and further explains the interdependence within this shared context (Galanes, 2003). This research provides empirical data to advance the current understanding of bona fide groups by examining novel boundary features defined by the advancement of AI technologies within the free and open-source community. Methodology The authors conducted 30 semi-structured, in-depth interviews with professionals working within free and open-source software communities worldwide. The first round of interviews is conducted in face-to-face settings during the Euro Foss4G conference in Mostar, Bosnia and Herzegovina. The second round of interviews are conducted online. Each interview lasts from 60-80 mins. The interview transcripts are analyzed with the software of MAXQDA. Interview questions are asked about the communication and collective experiences of collaborating with working professionals working with free and open-source software for geospatial (Foss4G) technologies. Particularly, the individual motives and collaboration experiences from the cross-cultural perspectives are examined. Questions around the impact of recent advancements in AI technologies on virtual work collaboration and community building are explored. Further, interviewees were also asked to describe their experiences in the free and open-source community and how their personal experiences impacted their perception of community and community culture. Findings and results The research results contribute to the literature on knowledge transfer and, in particular, to our understanding of boundary conditions and knowledge transfer approaches in virtual communities. The results highlight several contextual and social factors which impact knowledge sharing across the boundary in the context of free and open-source communities. The findings explain the mechanisms of the shared sense of boundary and the impact of contextual and social factors that impact knowledge transfer in free and open-source communities from a cross-cultural perspective, which has not been thoroughly studied due to its unique nature and emerging complexity. It offers fresh insights looking into task interdependence with a virtual community shaped by the advancement of AI technology. The research findings also lend further support to understanding the motivates and commitment of members within the community and how their commitment impacts the personal perceptions of permeate boundaries within the community, as well as the impact on task interdependence and knowledge-sharing practices within the free and open-source community. Practical implication and future studies This research offers several important practical implications for professionals in free and open-source community work. First, it highlights the important communication mechanism and its impact on members’ commitment and identification within the community. Understanding this mechanism will contribute to developing better commitment of members within the free and open-source community. Second, it suggests the need to establish an effective process with communication tools to accommodate the novelty of boundaries, particularly from a cross-cultural perspective. Third, the understanding of permeate boundaries helps community members to better collaborate and engage in knowledge-sharing practices and collectively contribute to the community culture. Last but not least, it also suggests community leaders to better maintain and monitor the interactions within the community and embrace a positive community culture. For future studies, researchers could adopt different data collection approaches to examine the interaction patterns of knowledge sharing within free and open-source communities, such as a survey. References Frey, L. R. (2003). Group communication in context: Studies of bona fide groups. Mahwah, New Jersey: Lawrence Erlbaum Associates, Publishers. ISBN 0805831495. Galanes, G. J. (2003). In their own words: An exploratory study of bona fide group leaders. Small Group Research, 34(6), 741-770. Putnam, L.& Stohl, C. (1990). Bona fide groups: A reconceptualization of groups in context. Communication Studies.41(3):248–265.doi:10.1080/10510979009368307. Putnam, L. & Stohl, C (1996). Bona fide groups: An alternative perspective for communication and small group decision making. In Randy Y. Hirokawa; Marshall Scott Poole (eds.). Communication and group decision making (2nd ed.). Thousand Oaks, Calif: SAGE Publications. pp. 147–178. ISBN 0761904611. Stohl, C., & Putnam, L. L. (2005). Communication in bona fide groups: A retrospective and prospective account. In Group Communication in Context (pp. 399-414). Routledge. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/ETJADY/ WG404 Juana Du PUBLISH EUNJRT@@talks.osgeo.org -EUNJRT Centering Communities in Early Action Local Knowledge: Open Data, and OSM in Practice en en 20251120T140000 20251120T142500 0.02500 Centering Communities in Early Action Local Knowledge: Open Data, and OSM in Practice Dar es Salaam is a region where urban flooding disproportionately affects informal settlements. The power to act early often lies not with technology, but with people. This talk showcases how OpenMap Development Tanzania (OMDTZ) has worked to place communities, not just tools, at the center of anticipatory action. By training and mobilizing youth, local leaders, and elders based on data collection, OMDTZ enabled vulnerable communities to lead flood preparedness efforts from within. Participants were trained to use the simple, open tool ODK to map risk prone areas such as blocked drains and report on water level on the river during the rain . But more than just mapping, they became part of the decision making process, identifying: Safe shelters and evacuation paths, Impassable roads during floods, and areas with inadequate drainage or past flood impact through participatory mapping. Beyond the data, this process built a sense of ownership, Local leaders and community participants during the group discussion and training are now ambassadors in their community on why they need to take early action, such as clearing their blocked drains and making sure all trash is collected before the rainy season, to reduce the impact of flooding in their communities We’ll share: The model for engaging different community groups in anticipatory action Lessons from working in both urban and peri-urban settings The transformation of mappers into advocates and preparedness leaders Real-world action: how local leader in informal settlements inform their community on early warnings Attendees will walk away with a community first approach to preparedness, useful for any context where local knowledge is rich but formal warning systems are weak. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/EUNJRT/ WG404 Asha Mustapher PUBLISH ZMECUX@@talks.osgeo.org -ZMECUX pygeometa project status en en 20251120T153000 20251120T155500 0.02500 pygeometa project status pygeometa provides a lightweight and Pythonic approach for users to easily create geospatial metadata in standards-based formats using simple configuration files (affectionately called metadata control files [MCF]). Leveraging the simple but powerful YAML format, pygeometa can generate metadata in numerous standards. Users can also create their own custom metadata formats which can be plugged into pygeometa for custom metadata format output. For developers, pygeometa provides a Pythonic API that allows developers to tightly couple metadata generation within their systems and integrate nicely into metadata production pipelines. The project supports various metadata formats out of the box including ISO 19115, the WMO Core Metadata Profile, and the WIGOS Metadata Standard. pygeometa has minimal dependencies (install is less than 50 kB), and provides a flexible extension mechanism leveraging the Jinja2 templating system. This presentation will provide an update on recent enhancements, use in high profile projects as well as future plans and roadmap. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/ZMECUX/ WG404 Tom Kralidis PUBLISH BSQLEH@@talks.osgeo.org -BSQLEH A Scalable Open-Source System for Impervious Land Mapping Using GRASS and the Python Ecosystem en en 20251120T160000 20251120T162500 0.02500 A Scalable Open-Source System for Impervious Land Mapping Using GRASS and the Python Ecosystem Introduction Impervious surface expansion significantly contributes to environmental degradation. Accurate and efficient mapping of these changes is essential in environmental monitoring and spatial analysis. While many tools exist for land cover classification, few provide a fully open-source, scalable solution capable of continuous Sentinel-2 monitoring, ingesting both orthophotos and multispectral data, performing temporal analysis, and supporting integration with spatial databases. This paper presents a scalable system for detecting and monitoring impervious land using orthophotos and Sentinel-2 data. The workflow integrates GRASS for spatial processing, HDF5 for high-performance raster storage, and Python-based machine learning libraries for classification and change detection. Sentinel-2 imagery is pulled automatically from the Planetary Computer after publication and classified with TorchGeo using pre-trained backbones fine-tuned on the EuroSAT dataset. The Sentinel-2 time series flags disturbance areas; when very high resolution aerial imagery becomes available the system triggers semantic segmentation there to produce detailed impervious maps. Predicted outputs are postprocessed, vectorized, and stored in spatial database. Results are further compared with ancillary datasets, including agricultural and forested lands, to inform end users of the environmental implications. The system is designed to support rapid prototyping, reproducibility, and high-resolution monitoring across varying spatial and temporal domains. Performance is evaluated using standard classification metrics, and results demonstrate the applicability of this workflow in operational land change detection contexts. 2 Materials and Methods 2.1 Data Sources and Management The system ingests multiple raster inputs: Sentinel‑2 multispectral bands from the Microsoft Planetary Computer for continuous monitoring; very high resolution orthophotos from the Mapping Authority of the Republic of Slovenia with near infrared, red, and green channels at 50 cm, used where the Sentinel‑2 time series indicates change; reference impervious layers from earlier periods to generate labeled training data and establish baselines for change detection; and ancillary datasets such as agricultural and forest land boundaries for comprehensive environmental assessments. Orthophotos are preprocessed in GRASS and exported to HDF5. Image features and labels are stored as chunked arrays to enable efficient parallel processing and patch based classification. After classification, prediction maps are imported into GRASS as a space-time raster dataset. Results of time-series analyses are converted to vector layers, and loaded into a PostgreSQL/PostGIS database for downstream operations and integration. 2.2 Machine Learning Pipeline Machine learning models for the orthophotos are trained using RAPIDS cuML (GPU-accelerated Random Forest) on the HDF5 tiles to produce detailed impervious maps. Sentinel-2 classification uses TorchGeo models with ResNet backbones that are initialised with Sentinel-2 weights provided by TorchGeo and fine-tuned on the EuroSAT dataset; the operational maps are produced by the fine-tuned models over the study area and follow the same land cover class scheme. Inference is executed using joblib. Evaluation metrics (F1 score, accuracy) guide model selection and validation. 2.3 Temporal Analysis Change detection is performed by comparing Sentinel-2 based land cover prediction maps across years and by maintaining a Sentinel-2 time series in a GRASS space-time raster dataset. The TorchGeo model, fine-tuned on the EuroSAT dataset, provides land cover classes, and the temporal analysis focuses on transitions into highway, industrial and residential classes from other land cover types as indicators of areas with new impervious surfaces. New Sentinel-2 scenes are downloaded automatically after publication, and GRASS spatio-temporal aggregation and change-detection tools are used to derive spatial differences that identify newly developed impervious areas. When new very high resolution orthophotos are available, the system automatically triggers segmentation only over tiles flagged by the Sentinel-2 disturbance signal. 2.4 Postprocessing and Dissemination Postclassification filtering reduces noise. Cleaned rasters are vectorized and simplified within GRASS. Final geometries are stored in PostGIS, generalized, enriched with auxiliary land cover data, and disseminated as WFS services, enabling spatial queries, external integration, and comparison with land-use data for assessing impacts on soil and potential for restoration. 3 Results and Discussion The system was applied to multi-year orthophoto and Sentinel-2 datasets. Continuous Sentinel-2 monitoring produced timely impervious updates. The HDF5-based chunking significantly reduces I/O bottlenecks when performing machine learning tasks. Targeted segmentation of orthophotos over Sentinel-2 disturbance areas improved efficiency while preserving detail. Change maps accurately identified new impervious areas. Vectorized outputs supported further spatial analysis, visualization, and comparative evaluation against agricultural and forested land-use data. WFS dissemination enabled direct use in client applications. These comparisons underscore the importance of monitoring soil sealing and highlight opportunities for targeted land restoration initiatives. 4 Conclusion This study demonstrates a reproducible, open-source pipeline for high-resolution impervious land mapping with continuous Sentinel-2 monitoring. Integration of GRASS, HDF5, RAPIDS cuML and TorchGeo enables efficient classification, temporal analysis, event-driven segmentation of orthophotos, spatial output management, and environmental impact assessment. The architecture is adaptable to diverse data sources and modeling approaches, thus suitable for research, operational applications, and informed land management and restoration practices, with outputs shared through WFS services. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/BSQLEH/ WG404 Alen Mangafić Tomaž Žagar PUBLISH JJEYWH@@talks.osgeo.org -JJEYWH From Edits to Impact - TomTom’s Journey with Open Communities en en 20251120T163000 20251120T165500 0.02500 From Edits to Impact - TomTom’s Journey with Open Communities TomTom and the Open Map Community share a common vision: building a map that reflects and serves the world, powered by local knowledge and collective action. In this session, We will be taking you behind the scenes of TomTom’s partnerships with OSM communities worldwide, demonstrating how collaborative efforts are strengthening the quality, freshness, and inclusiveness of OpenStreetMap data. From hosting mapathons with universities and YouthMappers chapters to supporting humanitarian mapping during crises, TomTom’s initiatives have connected 3,397+ OSM contributors across 189 countries. We will share real examples of how these collaborations are transforming local mapping ecosystems while providing technical resources, training, and data that support contributors in making impactful edits. The session will also cover learnings on We will also discuss scaling corporate-community collaborations responsibly, challenges faced in diverse regions, and opportunities for co-creating projects that benefit both OSM and local communities. Participants will leave with ideas and inspiration on how to replicate or adapt these approaches to engage more mappers, foster skill-building, and deepen OSM’s reach. Join us to discover how mapping together can empower communities and create a richer, more inclusive map of the world. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/JJEYWH/ WG404 Natasha Klinghardt PUBLISH 98KXXL@@talks.osgeo.org -98KXXL Embracing Cloud-Native Geospatial en en 20251120T090000 20251120T092500 0.02500 Embracing Cloud-Native Geospatial Cloud-native geospatial represents a paradigm shift in how organizations handle location-based data and spatial analytics. This modern approach combines the power of cloud computing with geospatial technologies to create scalable, efficient, and cost-effective solutions. This session will be an introduction to what it means to be "cloud-native". Key components of cloud-native geospatial include storage solutions, compute services, analytics capabilities, and visualization. The benefits of this approach include: - Bring compute to the data, not the other way around - Scalability to handle varying workloads - Pay-as-you-go pricing model - Reduced infrastructure management overhead - Global availability and low latency - Built-in security and compliance features PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/98KXXL/ WG802 Dave Bianco PUBLISH JGH8PK@@talks.osgeo.org -JGH8PK Weavingspace: a new way to make multivariate maps en en 20251120T093000 20251120T095500 0.02500 Weavingspace: a new way to make multivariate maps The weavingspace python module enables creation of multivariate thematic maps by generating and overlaying a periodic tiling layer with polygon data. Geometries in the tiling symbolise data by choropleth colouring. Well over one hundred tiled patterns are supported, including a highly configurable tilings that give the appearance of biaxial or triaxial woven materials. Complex tilings with as many as twenty distinct elements yield maps with textures that convey combinations of attribute values as textures, while maps with up to around eight elements make it possible to read maps with that number of variables simultaneously. In this presentation I will discuss the motivation for this work, challenges in implementation, and also present a web app that allows code-free creation of such maps. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/JGH8PK/ WG802 David O'Sullivan PUBLISH PDXQ7A@@talks.osgeo.org -PDXQ7A pgRouting feature frenzy en en 20251120T100000 20251120T102500 0.02500 pgRouting feature frenzy We will cover: - What is pgRouting? - Where can pgRouting be used? - pgRouting function classification. - Frenzy of pgRouting features from A to Z PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/PDXQ7A/ WG802 Vicky Vergara PUBLISH ASNWTC@@talks.osgeo.org -ASNWTC State of STAPI: A community tasking standard en en 20251120T110000 20251120T112500 0.02500 State of STAPI: A community tasking standard Community standards, created through collaborative grassroots efforts before being widely adopted, play a crucial role in geospatial interoperability as exemplified by specifications like SpatioTemporal Asset Catalog (STAC) and Cloud-Optimized GeoTIFFs (COGs). Efforts like these not only enable seamless data interoperability but also form the backbone of robust, scalable systems that support critical geospatial operations. The Sensor Tasking API (STAPI) is an emerging community standard aiming to standardize sensor tasking and spatiotemporal data ordering through a unified API and an ecosystem of tooling. Five community sprints have been held across the US and Europe, most recently this past April in Lisbon, where the spec reached the major milestone of a version 0.1.0 release. Featuring collaboration amongst government groups, commercial satellite operators, data integrators, and other community members, these iterative and collaborative sprints have worked and continue working toward developing a robust API specification and tooling. A sixth sprint is in the works and expected to take place in Europe in the first half of 2026. This talk will showcase the concrete achievements of the STAPI community, demonstrating how a collaborative approach can lead to a tangible and impactful standard for accessing future geospatial data. We will delve into the specification, highlighting its key features and recent developments, including the upcoming version 0.2.0 release. We will also look at the open-source ecosystem growing out of this effort, including projects like stapi-fastapi, stapi-pydantic, and pystapi-client that are empowering the community to create their own STAPI-compliant services and tooling, and several practical implementations from commercial providers. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/ASNWTC/ WG802 Matthew Hanson Jarrett Keifer PUBLISH BWGXHK@@talks.osgeo.org -BWGXHK How to use data from the AWS Open Data program in Amazon Bedrock and Amazon SageMaker AI en en 20251120T113000 20251120T115500 0.02500 How to use data from the AWS Open Data program in Amazon Bedrock and Amazon SageMaker AI Sharing data in the cloud lets data users spend more time on data analysis rather than data acquisition. Amazon Web Services (AWS) provides a catalog of publicly available datasets on AWS through the Registry of Open Data on AWS (https://registry.opendata.aws/). The registry has over 650 datasets open to the public, such as government data, scientific research, life sciences, climate, satellite imagery, geospatial, and genomic data. First, we will cover the Registry of Open Data on AWS, how to search for datasets and how to search for data objects. Second, we will cover how to use datasets in Open Data as Knowledge Bases in Amazon Bedrock with a vector embedding store. Then we will cover how to use certain datasets in Open Data as Knowledge Bases with a structured dataset, and the trade offs between each type. Last, we will have take-home information to try this at home. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/BWGXHK/ WG802 Chris Stoner PUBLISH Z8STHQ@@talks.osgeo.org -Z8STHQ How to Complete Japan’s OSM Building Data in One Year using the citygml-osm Converter en en 20251120T120000 20251120T122500 0.02500 How to Complete Japan’s OSM Building Data in One Year using the citygml-osm Converter — Strategies for Integrating PLATEAU Data into OpenStreetMap Since 2020, Japan’s Ministry of Land, Infrastructure, Transport and Tourism (MLIT) has led Project PLATEAU, developing and releasing 3D city models in CityGML format. By 2025, over 236 municipalities have published detailed building datasets compatible with the Open Database License (ODbL), making them suitable for integration into OpenStreetMap (OSM). These high-precision, semantically rich datasets are valuable for urban planning, research, and civic technology. Building upon previous FOSS4G and SotM presentations, this talk highlights the progress made since 2022 by OpenStreetMap Japan volunteers in importing PLATEAU’s Level of Detail 1 (LOD1) building data into OSM. As of May 2025, imports have been completed for 13 cities. In addition to these imports, numerous volunteer mappers continue to enhance OSM’s building data through aerial imagery tracing and other methods. Despite these efforts, only about 62% of Japan’s estimated 38 million building polygons are currently mapped in OSM, leaving approximately 14 million polygons (38%) yet to be integrated. Addressing this gap requires a clear numerical target and a strategic approach. This presentation outlines an ambitious yet achievable goal: to complete the mapping of Japan’s building data using the CityGML-OSM converter within one year. We will provide a quantitative analysis of current progress and outline a roadmap to achieve this objective. Key strategies include optimizing the importation of PLATEAU data, ensuring data consistency between PLATEAU and OSM, and strengthening collaboration within the mapping community. By sharing these insights and methodologies, we aim to accelerate the integration of PLATEAU data into OSM and serve as a model for other countries seeking to enhance their building mapping efforts. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/Z8STHQ/ WG802 Taichi Furuhashi PUBLISH 8EUHDY@@talks.osgeo.org -8EUHDY QGIS and Democracy - Defining New Zealand’s Electoral Boundaries en en 20251120T133000 20251120T135500 0.02500 QGIS and Democracy - Defining New Zealand’s Electoral Boundaries This year the Representation Commission reviewed and updated New Zealand’s electoral boundaries. This talk will give a high-level overview on the process and describe how a QGIS plugin was the core decision making tool that enabled the Commission to quickly investigate different boundary scenarios and ensure electorates were within legislated population tolerances. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/8EUHDY/ WG802 Ian Harrison PUBLISH TZUBTN@@talks.osgeo.org -TZUBTN Participatory GIS Methods for Urban Redevelopment en en 20251120T140000 20251120T142500 0.02500 Participatory GIS Methods for Urban Redevelopment Public Participatory Geographic Information Systems (PPGIS) is a map-based survey method enabling citizens to contribute geographic and non-geographic information for planning and research. Originating in the 1990s, PPGIS emerged to democratize GIS and integrate community participation. It promotes inclusive, transparent decision-making and policy development through visualization and interactive mapping. As government services increasingly shift to digital platforms, citizens’ voices and opinions must also transition digitally, influencing both decision-making and implementation. For instance, in urban redevelopment, PPGIS can be used to conduct map-based questionnaires, collect and classify data, and serve not only as a planning tool but also as a digital communication platform between citizens and planners. The Leafmap Python package meets these criteria by enabling interactive, low-code spatial analysis, surveys, and public engagement in planning processes. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/TZUBTN/ WG802 Uyanga Ankhbayar PUBLISH QFVNWU@@talks.osgeo.org -QFVNWU A Framework of GeoAI for Object Detection and Classification using OGC Standards - From Data Collection to Visualization en en 20251120T143000 20251120T145500 0.02500 A Framework of GeoAI for Object Detection and Classification using OGC Standards - From Data Collection to Visualization Object detection and feature classification are one of the most common applications of Geo-AI. It consists of multiple modules such as training data collection of geospatial data such as geo-referenced images and video, training deep learning model, object classification and detection, and visualization of the results. As diverse environments may be included in the system, the interoperability between modules becomes a critical challenge to develop an integral GeoAI solution. OGC UDTIP (Urban Digital Twin Interoperability Pilot) project aims to provide a GeoAI framework based on OGC standards as part of its requirements. The system is composed of four building blocks – data collection module for geo-reference image or video training datasets, GeoAI analytics module for deep learning model, visualization for presenting the result of detection or classification on a map, and a geospatial platform for coordinating these modules. Many OGC standards are applied such as GeoPose, TrainDML for AI, and OGC-API. The solution has been tested with UN VMC (Verification Mission in Colombia) for collecting the training data. While the first target application was to classify the road surfaces, it is extensible to other types of GeoAI applications such as object detections by simply modifying the code list of TrainDML for AI and replacing proper deep learning model. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/QFVNWU/ WG802 Ki-Joune Li PUBLISH BXYXV3@@talks.osgeo.org -BXYXV3 Open Source and Open Standard Offline Mobile Data Collection with KoboToolbox en en 20251120T153000 20251120T155500 0.02500 Open Source and Open Standard Offline Mobile Data Collection with KoboToolbox KoboToolbox (Kobo) is an open source, non-profit project based in the USA from the Harvard Humanitarian Initiative, dedicated to making data collection accessible to everyone, everywhere. We are one of the leading global open source tools for collecting, managing, and visualizing data. Our primary sectors include supporting humanitarian aid, environmental protection, human rights, public health, and social development, by providing real data to drive positive change. KoboToolbox is used across the globe for mobile data collection in some of the world's most remote locations, handling over 20 million form submissions per month. KoboToolbox is entirely open source and is freely available to anyone who wishes to run or customize it. Kobo also provides a free hosting service to non-profit organisations on its public servers, as well as offering dedicated private servers to humanitarian institutions, such United Nations, Red Cross, Save the Children, which helps subsidize our free services. KoboToolbox provides both a native Android app - KoboCollect - and a browser-based client - Enketo. All data collection in KoboToolbox can be performed entirely offline, with form definitions and new submissions permanently stored on the device until the user is back in coverage. In addition to a rich set of basic form controls, such as text, selections, photos, QR codes, etc, KoboToolbox also supports acquiring geospatial data in the form of points, paths, and polygons, and includes geo-centric functions like area and distance calculations as well as on-device geofencing. The KoboCollect app supports an extensive range of mapping options, including the ability to preload and display offline MapBox map tiles, and making selections from a map. All form elements can be further embellished with skip logic and validation checking to support custom workflows. Longitudinal studies can be undertaken by referencing previously acquired datasets whilst in the field, again, in an entirely offline manner. KoboToolbox is deployed globally and permits extensive internationalization with over 60 languages supported. All data collected can be visualized through rich dashboards - including geospatially - and is fully accessible to other integration platforms via KoboToolbox’s extensive API and export interfaces. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/BXYXV3/ WG802 Dr. Gareth S. Bestor PUBLISH UXPNJZ@@talks.osgeo.org -UXPNJZ Geomatics and open-source for road infrastructure management: standards, technologies, and future directions en en 20251120T160000 20251120T162500 0.02500 Geomatics and open-source for road infrastructure management: standards, technologies, and future directions Following the introduction of guidelines for documenting road assets in Europe, infrastructure asset management is undergoing a new digital transformation. The reuse and transparency principles are fostering the adoption of open standards and open-source technologies [1]. This paradigm shift toward digitising is evident in the monitoring and maintenance of critical infrastructure, such as bridges and road networks. New guidelines led to the proliferation of proprietary solutions, which, however, contributed to a fragmentation of data environments. Custom and licence-dependent formats limited the interoperability, scalability and transparency of infrastructure management workflows [2]. In contrast, the open-source approach fosters collaboration, enhances accessibility to information, and supports more resilient, cost-effective and democratic decision-making processes. To strategically develop operational solutions answering the guidelines needs, it is then needed to address the state-of-the-art of open standards, technologies and their potentials and limitations in documented applications. Standards overcoming proprietary limitations A core enabler of this transformation is the implementation of open standards for data interoperability. In the building and infrastructure sector, BIM standards such as Industry Foundation Classes (IFC, ISO 16739) offer a solid basis for representing built assets in a vendor-neutral format [3]. IFC enables the exchange and reuse of information throughout the design, construction and operational phases, incorporating essential elements such as geometric components, spatial relationships and functional systems. Recent IFC extensions specifically support bridges, and ongoing efforts are targeting tunnels and other infrastructure typologies [4]. In parallel, developments in the GIS domain have produced open standards such as CityGML and LandInfra, which enable the structured representation of infrastructure within its environmental context [5]. These standards facilitate the integration of semantic, spatial, and topological information across administrative and technical domains, especially when aligned with OGC specifications that adhere to the FAIR principles — Findable, Accessible, Interoperable, and Reusable [6]. However, the true digital integration of infrastructure assets requires more than the adoption of standalone standards. The convergence of BIM and GIS necessitates strategies that harmonise their respective models and semantics, especially for multi-scale, cross-domain applications. Projects are increasingly relying on Linked Data and Semantic Web technologies to achieve this integration. Using ontologies facilitates mapping sensor properties to models, embedding IoT data in the infrastructure’s digital representation [1]. Despite these advances, challenges persist. Semantic and schematic discrepancies between BIM and GIS standards continue to pose significant challenges. For example, efforts to map IFC to CityGML often involve trade-offs between semantic richness and geometric accuracy. Rather than creating new standards, the prevailing strategy is to extend and align existing ones to meet the evolving requirements of digital twin systems, particularly those focused on bridge management and road asset monitoring [7]. Technologies in operational systems Complementing this standards foundation is an expanding ecosystem of open-source technologies that support the practical implementation of digital infrastructure management systems. PostgreSQL, enhanced with the PostGIS extension, remains essential for storing spatio-temporal data [8][9]. Graph databases like Neo4j offer an intuitive and scalable solution for modelling relationships between infrastructure components, inspections, and maintenance workflows [10]. Open-source GIS platforms such as QGIS facilitate desktop spatial analysis and visualisation [11], integrating seamlessly with mobile tools such as MerginMaps, QField, and ODK Collect to enable efficient in-situ data collection. These solutions have proven effective for managing transportation assets at the municipal level, such as signage and road condition inventories [12]. The web-based visualisation and analysis of road networks and assets has also advanced significantly through libraries such as Cesium, Deck.gl and Xeokit. Cesium supports interactive, 3D globe-based visualisation and streams 3D tiles to render large-scale infrastructure environments [7]. Xeokit specialises in BIM visualisation, enabling examination of IFC models within web platforms. These libraries enable infrastructure stakeholders to interact with 3D models independently of proprietary software, thereby expanding access to asset information across organisations [10]. Dashboards built with frameworks such as ECharts and enhanced with WebSocket protocols support the integration of real-time data from sensor networks, ensuring that digital models are dynamically linked to their physical counterparts. In the field of road network analysis, open-source Python packages such as OSMnx, MovingPandas and Scikit-Mobility allow for a detailed examination of mobility patterns and accessibility [13]. Simulation tools such as SUMO and AequilibraE offer the ability to model multimodal traffic flows, test policy scenarios and evaluate infrastructure interventions [14]. When integrated with geodata from OpenStreetMap and coupled with routing engines like OSRM, these tools provide a comprehensive solution for network planning and optimisation [6]. Current potentials and limitations By eliminating licensing fees, open-source approaches reduce long-term software costs and provide full transparency into the computational models and assumptions behind analyses—an essential aspect for public infrastructure projects where trust, accountability, and reproducibility are critical [15]. Open tools also give public agencies and asset owners control over their data and workflows, enabling them to inspect, adapt, and extend codebases to meet evolving needs without vendor lock-in. The collaborative nature of open-source development further facilitates knowledge transfer and capacity building. Active communities, thorough documentation and modular architectures reduce the barriers to adoption, thereby fostering innovation [6]. Local governments and academic institutions can contribute to, and benefit from shared development to generate context-specific, sustainable solutions. Furthermore, open tools promote citizen engagement by making infrastructure data more accessible and easier to understand — a vital aspect of participatory planning and the development of inclusive smart cities [12, 15]. However, there are still challenges to overcome. For example, integrating different types of data, such as point clouds, GIS layers, BIM models and real-time sensor feeds, requires robust data pipelines. Ensuring data quality, particularly for legacy assets, remains challenging. Furthermore, performance and scalability issues arise with large-scale or high-resolution models, and efforts to optimise such workflows are ongoing. Equally critical are the human and institutional factors. Although many open-source tools now have user-friendly interfaces, a certain level of technical expertise is still needed for setup, customisation and maintenance. Capacity building and continuous training are necessary to ensure that practitioners can leverage these tools effectively. At the policy level, questions relating to data ownership, privacy and digital sovereignty must be addressed by robust legal and governance frameworks. The adoption of digital twins for public infrastructure also raises important ethical and epistemological questions, particularly with regard to the authority of data-driven models in decision-making contexts. Looking forward, research focus on enhancing semantic alignment between BIM and GIS models, improving the representation of defects and maintenance history within IFC schemas, and enabling greater automation in cross-platform integration. Further studies are needed to assess the comparative benefits and limitations of open-source versus proprietary solutions in different infrastructure contexts. Evaluating the long-term sustainability, replicability, and social impact of open-source digital twins will be key to their broader adoption. This contribution aims to provide an overview of the open-source and open-standard scenario in infrastructure management with case studies from literature. It offers a technical synthesis and a forward-looking perspective, with a particular focus on documented case studies that adopt OSGeo tools, OGC standards and open data. Leveraging the strengths of openness enables stakeholders to build more resilient infrastructure systems. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/UXPNJZ/ WG802 Federica Gaspari PUBLISH PHFBKG@@talks.osgeo.org -PHFBKG Fast Urban Digital Twin prototyping based on open data en en 20251120T163000 20251120T165500 0.02500 Fast Urban Digital Twin prototyping based on open data In recent years, the paradigm of the smart city has evolved towards the concept of digital twins, which are now being ever more discussed and implemented in cities from all around the world. Conceptually, a digital twin is a digital model representing a real-world object, process, or system that enables 2-way communication, simulations and what-if scenarios, revisit of past data, and monitoring of the modelled entity. Urban Digital Twins are the application of said Digital Twins to cities, urban areas and urban spaces, and are mostly used in urban planning as a tool for engaging with communities, perform simulations, manage multiple urban processes, and enable data-driven decision support systems. Geospatial data is central for the implementation of Urban Digital Twins, as their data requirements are inherently spatial. Street networks, sensor networks, granular meteorological and weather information, and remotely sensed imagery are all geospatially enabled data sources, prompting a need for robust spatial data infrastructures and standardised services that enable interoperability. However, the adoption of Urban Digital Twis is limited due to economic and technological factors, as such projects require large volumes of historical and constantly updated data, data streams, 3D models, and an infrastructure to store such data and run simulations. Consequently, the use of proprietary software for Urban Digital Twins is common practice. Open source software and open data offer a massive opportunity for the development of Urban Digital Twins. With, overall, robust and globally available data for multiple city systems, it is possible to create minimal prototypes of Urban Digital Twins that can scale up by appending authoritative information, replacing global open data with other high-resolution datasets, and creating tailored models for analysing specific aspects of urban areas. Global open data is also practical for deploying Urban Digital Twin prototypes in developing countries and economies that lack high-resolution data availability. This work is focused on proposing a methodology for assembling prototypes of Urban Digital Twins based solely on open data and open source software. Our research includes the proposal of an Urban Digital Twin architecture, the analysis of minimal data requirements for Urban Digital Twins, a selection of global, open data sources, and a city-scale test case. Our methodology strides to be applicable worldwide as a baseline and precursor of fully-featured Urban Digital Twins. The architecture for the Fast Urban Digital Twin prototype features three components: i) the Digital Twin engine where calculations and models run following a cloud-computing paradigm; ii) the data component where unstructured and structured data is stored; and iii) the extension system that enables the platform to connect with other standardized data sources and services. The Digital Twin engine component enables a fundamental part of the Digital Twin: processing and simulations. This component provides dashboard capabilities, visualization, and custom processing functionalities. As a critical part of Digital Twins is their purpose, the customisation of processing functionalities enables to steer a generic prototype towards a domain-specific solutions. Such processing capabilities also include AI and physical models for forecasting and simulating future scenarios. The data component provides storage and retrieval capabilities of structured, unstructured, and streamed data. Such solutions, generally related to data lakes and warehouses, are usually implemented in data spaces and Digital Twins, as they allow to store massive amounts of data and are optimised for its analysis and retrieval. The prototype solution includes minimal, pre-loaded, data that can be used for urban analyses. Examples of such data include street networks, 3D buildings, socioeconomic information, and any other minimal information found during the research phase, which are useful for general-purpose Urban Digital Twins. Finally, the extension system is the way in which the Urban Digital Twin prototype can scale up and become a fully-fledged solution by enabling the connection with standardised geospatial services and the insertion of standard data formats (e.g., CityGML, GeoPackage, GeoTiff, GeoParquet, etc.). This extension system allows the inclusion of new data and the possibility to modify existing one, enabling the use of high resolution and authoritative information instead of default global data. To test our methodology, a test case will be presented, featuring a city-scale Urban Digital Twin prototype based solely on open data. Other functionalities as the extension system, custom functionalities, and dashboard visualization are also tested. The city to be studied for the test case is Bologna, Italy. In conclusion, the usage of open data and open source software is beneficial for the implementation of Urban Digital Twins as it enables fast prototyping and baseline solutions that are cost-effective and open. We propose and implement an Urban Digital Twin architecture and solution for the creation of minimal and usable prototypes, based on open data, that enable processing, simulations, data insertion, and extension through custom functionalities, such as AI models, and standardised data formats and services. We test our approach with a test case, featuring a city-scale study for the city of Bologna, combining multiple global, open data, and a set of custom functionalities. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/PHFBKG/ WG802 Juan Pablo Duque Ordoñez PUBLISH 3W8Y9W@@talks.osgeo.org -3W8Y9W Registration en en 20251121T080000 20251121T170000 9.00000 Registration FOSS4G 2025 Auckland Conference Registration PUBLIC CONFIRMED Registration https://talks.osgeo.org/foss4g-2025/talk/3W8Y9W/ WG306 Foyer PUBLISH WDUCCK@@talks.osgeo.org -WDUCCK Morning Break en en 20251121T103000 20251121T110000 0.03000 Morning Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Morning Tea https://talks.osgeo.org/foss4g-2025/talk/WDUCCK/ WG201 Exhibition & Catering PUBLISH MFNPZ8@@talks.osgeo.org -MFNPZ8 Lunch Break en en 20251121T123000 20251121T133000 1.00000 Lunch Break This is the time where you can have a 60min break to recharge with Tea, Coffee, Juice, Water and Food as well as visit our exhibitors and sponsors. PUBLIC CONFIRMED Lunch https://talks.osgeo.org/foss4g-2025/talk/MFNPZ8/ WG201 Exhibition & Catering PUBLISH 9S9U8F@@talks.osgeo.org -9S9U8F Afternoon Break en en 20251121T150000 20251121T153000 0.03000 Afternoon Break This is the time where you can have a 30min break to recharge with Tea, Coffee, Juice, Water and Food PUBLIC CONFIRMED Afternoon Tea https://talks.osgeo.org/foss4g-2025/talk/9S9U8F/ WG201 Exhibition & Catering PUBLISH JRR7YJ@@talks.osgeo.org -JRR7YJ Knowledge sharing and building a sustainable community culture en en 20251121T091000 20251121T091500 0.00500 Knowledge sharing and building a sustainable community culture The authors conducted 30 semi-structured, in-depth interviews with professionals working within free and open-source software communities worldwide. The first round of interviews is conducted in face-to-face settings during the Euro Foss4G conference in Mostar, Bosnia and Herzegovina. The second round of interviews is conducted online. Each interview lasts from 60-80 mins. The interview transcripts are analyzed with the software of MAXQDA. Interview questions are asked about the communication and collective experiences of collaborating with working professionals working with free and open-source software for geospatial (Foss4G) technologies. Particularly, the individual motives and collaboration experiences from the cross-cultural perspectives are examined. Further, interviewees were also asked to describe how their personal experiences impacted their perception of community and a sustainable community culture. Insights and findings from the interviews are discussed. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/JRR7YJ/ WG403 Juana Du PUBLISH NCQSEN@@talks.osgeo.org -NCQSEN ImageN for GeoSpatial en en 20251121T091500 20251121T092000 0.00500 ImageN for GeoSpatial I am *so* excited to share this one, I have been planning it literally for over a decade - and it is finally happening! Attend this talk and buy me a beer. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/NCQSEN/ WG403 Jody Garnett PUBLISH FRJJ7D@@talks.osgeo.org -FRJJ7D Feed your spreadsheet to the Pandas! en en 20251121T092000 20251121T092500 0.00500 Feed your spreadsheet to the Pandas! The Pandas library for Python streamlines data analysis by making number crunching both fast and intuitive. It excels at handling common “load-calculate-save” workflows with concise, readable code—ideal for everyday data tasks. In this practical lightning talk, we’ll walk through a beginner-friendly workflow using Python notebooks, designed for rapid experimentation and iteration. Attendees will learn how to: • Load datasets • Filter and transform data • Perform basic calculations • Export results — all using Pandas. To help participants get started quickly, a GitHub repository will be shared containing a minimal working setup. This ready-to-clone configuration will enable attendees to dive into their own analyses with ease. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/FRJJ7D/ WG403 Stacy Rendall PUBLISH AUJALV@@talks.osgeo.org -AUJALV Advancing Spatial Demographic Insights through GIS: Results of Fiji Population Grid for 2023 and Implications en en 20251121T092500 20251121T093000 0.00500 Advancing Spatial Demographic Insights through GIS: Results of Fiji Population Grid for 2023 and Implications The Fiji Bureau of Statistics, in collaboration with SPC’s Statistics for Development Division, has developed the 2023 Fiji Population Grid—a high-resolution spatial dataset modeled from the 2017 Census and projected to 2023. Represented in 100m x 100m cells, the grid consists of 66 datasets disaggregated by sex, major ethnicity, and 5-year age groups. This experimental GIS-based approach marks a significant step in spatial demographic analysis, offering detailed insights into population distribution, demographic shifts, and inter-ethnic dynamics across Fiji. The 2023 update reveals evolving patterns in gender and age distributions, supporting more informed policy-making and planning at national and subnational levels. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/AUJALV/ WG403 Fiu Penjueli PUBLISH KTYGVZ@@talks.osgeo.org -KTYGVZ High-resolution, large-scale inundation mapping with basic python libraries en en 20251121T093000 20251121T093500 0.00500 High-resolution, large-scale inundation mapping with basic python libraries Bathtub modelling is a simple approach to flood mapping, whereby inundation depths are computed via differencing a water height layer with the terrain elevation. However, bathtub models often overestimate flood depth and extent, as they fail to resolve underlying processes including hydraulic connectivity, attenuation, fluid flow direction, and structural barriers. Following concepts outlined in Kasmalkar et al. (2024), a bathtub inundation model was developed which accounts for hydraulic connectivity and path-based attenuation to improve the accuracy of flood mapping. The model was applied for multiple inundation scenarios over a large extent (approx. 15,000 sq. km) at high spatial resolution (4 m) which presented numerous challenges in terms of compute capacity, runtime, and generation of useable output data and maps. To address these challenges, the workflow was implemented in Python and involved segmenting the study area into smaller regions, relying on only numpy/cupy (for GPU) in the processing script, and using gdal for reading/writing raster and vector inputs/outputs. This talk will briefly outline the goals of this project, the hurdles encountered along the way, and the solutions designed to overcome them. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/KTYGVZ/ WG403 Ellorine Carle PUBLISH KGTFHY@@talks.osgeo.org -KGTFHY GeoTools Update en en 20251121T093500 20251121T094000 0.00500 GeoTools Update GeoTools is the backbone of the Java Geospatial community, implementing a lot of the GIS functionality behind GeoServer, GeoNetwork and more. Check in with this project as we update to Java 17, a new image processing engine, and general map-happy-ness. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/KGTFHY/ WG403 Jody Garnett PUBLISH XSHWYR@@talks.osgeo.org -XSHWYR Confessions of a Natural Intelligence Vibe Coder? en en 20251121T094000 20251121T094500 0.00500 Confessions of a Natural Intelligence Vibe Coder? An examination of FOSS4G in academica from a personal perspective. Vibe coding ain't new. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/XSHWYR/ WG403 Martin Tomko PUBLISH EMWHHZ@@talks.osgeo.org -EMWHHZ Make It Easier to View GTFS : Building a GTFS Timetable Viewer with SvelteKit en en 20251121T094500 20251121T095000 0.00500 Make It Easier to View GTFS : Building a GTFS Timetable Viewer with SvelteKit Public transit agencies publish schedule data in GTFS format, but accessing this information remains challenging for most users. This application provides a web-based GTFS timetable viewer that transforms complex transit data into readable, interactive timetables with ease of use. Users simply drag and drop GTFS ZIP files directly into their browser, where data is processed entirely client-side without requiring server infrastructure.This application makes viewing GTFS data significantly quicker and easier. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/EMWHHZ/ WG403 Xinmiao Qu PUBLISH Y7W78H@@talks.osgeo.org -Y7W78H Stop Baking the Same Cake: Instant Spatial Data Reporting with Open Source and AI en en 20251121T095000 20251121T095500 0.00500 Stop Baking the Same Cake: Instant Spatial Data Reporting with Open Source and AI Reporting on spatial data shouldn't require you to "bake the same cake" repeatedly. I spent years collecting and processing environmental data, only to get bottlenecked at the reporting stage where it matters most. So, what did I do? I built a web platform to transform this workflow: leveraging open-source tools for powerful visualizations, cloud-optimised geospatial formats for scalable data access, Python's geospatial ecosystem for processing pipelines, and carefully constrained LLMs to automate report generation while preserving expert methodology. I'll share my journey and demonstrate how open-source tools made it all possible. PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/Y7W78H/ WG403 Phil Clunies-Ross PUBLISH KMFTL7@@talks.osgeo.org -KMFTL7 Mapping Storm en en 20251121T095500 20251121T100000 0.00500 Mapping Storm The story of how a cat became famous after being GPS tracked PUBLIC CONFIRMED Lightning Talk https://talks.osgeo.org/foss4g-2025/talk/KMFTL7/ WG403 Simon Nitz PUBLISH EZVHQQ@@talks.osgeo.org -EZVHQQ GeoArrow on Web; Can We Live Without GeoJSON? en en 20251121T110000 20251121T112500 0.02500 GeoArrow on Web; Can We Live Without GeoJSON? We all love binary data. In the world of WebGIS, binary vector tile quickly became the de-facto standard. However, when it comes to non-tiled data, the king of text data format, GeoJSON, still rules. GeoJSON is nice and handy, but not very suitable for big data. To unleash the potential of FOSS4G ecosystem, we need an efficient binary data format. GeoArrow, a specification based on Apache Arrow data format, is the most promising candidate for this. The Apache Arrow ecosystem is already very rich. Apache Arrow officially provides libraries and tools for various programming languages. Many FOSS tools support reading and writing in the format. Thanks to the infrastructure, GeoArrow works very smoothly in many use cases. However, web-related technologies provide relatively less support for GeoArrow. At least, we haven't reached to the point where we can replace GeoJSON with GeoArrow freely. This talk reviews the current status of FOSS4G software related to GeoArrow. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/EZVHQQ/ WG403 Hiroaki Yutani PUBLISH KMKEFP@@talks.osgeo.org -KMKEFP From Forest Types to Trees: A Web-based Digital Twin of Individual Trees Using OGC 3D Tiles en en 20251121T113000 20251121T115500 0.02500 From Forest Types to Trees: A Web-based Digital Twin of Individual Trees Using OGC 3D Tiles ### 🌲 From Forest Types to Trees: A Web-based Digital Twin of Individual Trees Using OGC 3D Tiles Can we implement a large-scale forest—down to individual trees—as a digital twin on the web? We asked this question, and then we did it. The answer: **yes, it works beautifully.** In this project, we converted Korea Forest Service’s *forest type map* into **OGC 3D Tiles** for web-based service. However, rather than stopping at generalized forest polygons, we focused on **individual tree-level data**—which includes species, height, crown diameter, and other attributes per tree. This level of detail is often considered impractical to visualize on the web due to data volume and complexity. So we challenged that assumption by rendering **3D models of real tree species**, each placed in their **actual location with correct height and size**, not just symbolic representations. And yes—it works smoothly, in-browser. All of this is made possible through the use of the **OGC 3D Tiles** standard. This means that once the data is converted, it becomes interoperable across multiple platforms: web, mobile, desktop GIS (QGIS, ArcGIS), and game engines (Unreal, Unity, O3DE, Omniverse) with no additional conversion required. This is the power of international open standards—**true “write once, use anywhere.”** Despite Korea's rich geospatial data, usage is often hindered by spatial data security policies. However, this project demonstrates how even highly detailed and sensitive datasets like individual tree inventories can be made accessible and useful—securely and efficiently. With this, **digital twin forest management at the individual tree level** is no longer a distant future—it’s already happening. --- ### 📝 Acknowledgements This study was carried out with the support of 'R&D Program for Forest Science Technology '(Project No. "RS-2025-25404070")' provided by Korea Forest Service(Korea Forestry Promotion Institute). PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/KMKEFP/ WG403 Kim Jinho Heejin Ha PUBLISH 3YLPQV@@talks.osgeo.org -3YLPQV Who Pays Your Bills? Sustainability, Community and Business: The Open Source Triangle en en 20251121T120000 20251121T122500 0.02500 Who Pays Your Bills? Sustainability, Community and Business: The Open Source Triangle "Who pays your bills?" was the first question my now-CTO asked me—back when we didn’t even know each other. Years later, we co-founded OPENGIS.ch, a company that thrives on open-source software. In this talk, I’ll share our journey building a sustainable business model around QGIS and QField, explain how the QGIS.org community works, and show why open source is not just a philosophy, but a real business opportunity. We’ll explore sustainability, community, and entrepreneurship—the triangle that allows open-source software to flourish and its contributors to make a living from it. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/3YLPQV/ WG403 Marco Bernasocchi PUBLISH PJDVGR@@talks.osgeo.org -PJDVGR STAC 101: What You Really Need to Know en en 20251121T133000 20251121T135500 0.02500 STAC 101: What You Really Need to Know The SpatioTemporal Asset Catalog (STAC) is an open standard that’s reshaping how we find, share, and use geospatial and Earth observation data. This session gives new users and data providers the practical essentials for getting started with STAC and using it well. We’ll break down the core concepts—Catalogs, Collections, Items, and Extensions—and explain how they fit together to organize spatiotemporal data in a modern, cloud-native way. For users, we’ll cover how to search, filter, and work with STAC data efficiently. For data providers, we’ll share best practices for publishing well-structured metadata, choosing extensions, and lessons learned from real-world implementations. Attendees will leave with clear, actionable tips to avoid common pitfalls and get the most out of the growing STAC ecosystem—whether you’re building your first Collection or just trying to find and use data more effectively. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/PJDVGR/ WG403 Matthew Hanson PUBLISH PPJD3Y@@talks.osgeo.org -PPJD3Y The Problem in Open Data Is Not the Data, but the Operations — and the Role of Re:Earth CMS en en 20251121T140000 20251121T142500 0.02500 The Problem in Open Data Is Not the Data, but the Operations — and the Role of Re:Earth CMS As Open Data initiatives continue to grow across Japan, the volume and diversity of public datasets are increasing rapidly. Many municipalities now operate their own data catalogs and are working toward greater accessibility and transparency. However, once Open Data enters everyday operation, a set of common global challenges emerges: - Inconsistent structures and formats - Maintenance and updates relying on individual effort - Difficulty coordinating across departments - Limited connection between catalogs and databases - Data that is published but not easily consumable by systems or applications These issues are not shortcomings of any single organization—they reflect the broader operational challenges faced by Open Data worldwide. Data can be published, but sustaining, organizing, and integrating it remains difficult. This talk reframes Open Data through the lens of operations rather than publishing and introduces the role of Re:Earth CMS: an open-source, multi-user, model-based, schema-driven tool designed to support lightweight, sustainable, and integrable data operations. By enabling structured datasets, collaborative editing, and API-ready outputs, Re:Earth CMS helps bridge the gap between Open Data catalogs and real-world digital applications, including urban systems, visualization tools, and civic technology. We hope this session offers a new perspective on what it means to “operate” Open Data and how we can collectively move from publishing files to managing data as a continuous practice. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/PPJD3Y/ WG403 RED (XU CONG) Kazuma Tsuchiya Maher Alhamoui PUBLISH 8JFWXD@@talks.osgeo.org -8JFWXD deck.gl State of the Union 2025: Globe View, React Widgets, and WebGPU Readiness en en 20251121T143000 20251121T145500 0.02500 deck.gl State of the Union 2025: Globe View, React Widgets, and WebGPU Readiness This State of the Union talk will showcase how deck.gl continues to evolve as the premier open-source solution for high-performance geospatial visualization on the web. Some highlights: ### Globe View Integration with MapLibre The most visually striking advancement is seamless integration with MapLibre's globe view. This collaboration between the deck.gl and MapLibre teams represents a major milestone for open-source geospatial visualization. The `GlobeView` has been updated to match MapLibre's camera positioning at equivalent zoom levels, ensuring consistent user experiences. The `MapboxOverlay` component now works effortlessly with maplibre-gl globe maps, eliminating previous integration complexity. This advancement opens new possibilities for global-scale data visualization, from climate modeling to satellite imagery analysis. ### Powerful UI Widget System deck.gl's widget system provides developers with reusable UI components that integrate seamlessly with the visualization pipeline. Users can simply add widgets to their applications without being forced into any particular framework, while maintaining full compatibility with React applications when needed. The comprehensive widget ecosystem includes ResetViewWidget for navigation controls, ScaleWidget for map scale indication, GeocoderWidget for address search integration, ScreenshotWidget for image export capabilities, LoadingWidget for data loading states, ThemeWidget for consistent styling, InfoWidget for contextual information display, and SplitterWidget for comparative visualizations. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/8JFWXD/ WG403 Felix Palmer PUBLISH QLJWL8@@talks.osgeo.org -QLJWL8 The Space Between: Where Open Data Matures Into Infrastructure en en 20251121T153000 20251121T160000 0.03000 The Space Between: Where Open Data Matures Into Infrastructure Open data has delivered access, but not connection. Datasets are frequently released in isolation, without the shared foundations that make them interoperable or enduring. This talk explores the space between those silos, the overlooked disconnect where open data must evolve to become infrastructure. In that space, openness shifts from publishing files to maintaining shared identifiers, schemas, and governance, the connective framework that allows data to work together. Drawing on the experience of the Overture Maps Foundation, this talk examines how open data can mature into open systems, and how treating data as infrastructure can transform fragmented efforts into something stable, trustworthy, and collaborative. The goal is not simply more openness, but a more connected foundation: data that is open, usable, and part of the world’s common infrastructure. PUBLIC CONFIRMED Keynote https://talks.osgeo.org/foss4g-2025/talk/QLJWL8/ WG403 Amy Rose PUBLISH LFFVJ7@@talks.osgeo.org -LFFVJ7 Scaling Impact: Enabling Open Source Adoption in Business and Government en en 20251121T160000 20251121T163000 0.03000 Scaling Impact: Enabling Open Source Adoption in Business and Government Open source has become foundational to modern technology, yet businesses and public institutions often treat it with suspicion or uncertainty. For open source communities, this represents both a challenge and an opportunity: how do we adapt our practices to meet the expectations of organizations that require predictability, accountability, and clear value? This session is designed for developers, project maintainers, and advocates who want to see their work adopted more widely in enterprise and government contexts. We’ll cover: - Understanding orgnisational barriers - Improving project readiness for adoption - Aligning with compliance and legal requirements - Building trust and communicating value. PUBLIC CONFIRMED Keynote https://talks.osgeo.org/foss4g-2025/talk/LFFVJ7/ WG403 Ana Belgun PUBLISH RMCL9D@@talks.osgeo.org -RMCL9D Closing Ceremony en en 20251121T163000 20251121T173000 1.00000 Closing Ceremony The Closing Ceremony of FOSS4G 2025 Auckland PUBLIC CONFIRMED Closing Ceremony https://talks.osgeo.org/foss4g-2025/talk/RMCL9D/ WG403 PUBLISH HPMPYE@@talks.osgeo.org -HPMPYE AGM - OSGeo en en 20251121T173000 20251121T181500 0.04500 AGM - OSGeo OSGeo Global Annual General Meeting PUBLIC CONFIRMED AGM https://talks.osgeo.org/foss4g-2025/talk/HPMPYE/ WG403 Vicky Vergara Tom Kralidis Angelos Tzotsos Marco Bernasocchi Jeroen Ticheler PUBLISH UY3SBT@@talks.osgeo.org -UY3SBT Lonboard: Fast, interactive geospatial vector data visualization in Python en en 20251121T090000 20251121T092500 0.02500 Lonboard: Fast, interactive geospatial vector data visualization in Python Visualization, especially interactive visualization, is often the initial step in extracting meaningful insights from data. But it's too hard to quickly and interactively visualize large geospatial vector data in Python. [Ipyleaflet](https://ipyleaflet.readthedocs.io/en/latest/) and [Folium](https://python-visualization.github.io/folium/latest/) are great for small datasets, but their performance quickly suffers as data sizes grow into the tens of thousands. [Pydeck](https://deckgl.readthedocs.io/en/latest/) supports slightly larger datasets, but it, too, struggles with data sizes above 100,000 coordinates. This presentation introduces [Lonboard](https://developmentseed.org/lonboard/latest/), a cutting-edge open-source Python library designed to address this challenge by enabling fast, interactive geospatial vector data visualization within Jupyter notebooks. Lonboard's performance stems from its innovative architecture, built on four key technologies: [deck.gl](https://deck.gl/) for GPU-accelerated rendering, [GeoArrow](https://geoarrow.org/) for efficient in-memory representation, [GeoParquet](https://geoparquet.org/) for optimized data transfer to the browser, and [anywidget](https://anywidget.dev/) for easy Jupyter integration. On a dataset with 3 million points, Ipyleaflet crashed after 3.5 minutes, Pydeck crashed after 2.5 minutes, but Lonboard successfully rendered in 2.5 **seconds**. This talk will give a brief overview of the internal innovations that make Lonboard so fast, then detail how to make the most of Lonboard's high-level APIs for visualizing large data. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/UY3SBT/ WG308 TE IRINGA Kyle Barron PUBLISH A8EV98@@talks.osgeo.org -A8EV98 Building Spatial APIs in PostgreSQL with PostgREST en en 20251121T093000 20251121T095500 0.02500 Building Spatial APIs in PostgreSQL with PostgREST In this talk, we’ll explore how you can create powerful, production-ready spatial APIs directly from your PostgreSQL database using PostgREST. PostgREST is an open-source tool that automatically generates a RESTful API from your database schema, eliminating the need for custom backend code. By integrating PostGIS, the geospatial extension for PostgreSQL, we’ll see how spatial data like points, polygons, and raster layers can be exposed through clean, performant REST endpoints. We'll walk through the setup process, including enabling PostGIS, configuring PostgREST, and writing SQL views or functions to expose spatial operations. You'll learn how to use PostGIS functions—like distance calculations, spatial joins, and bounding box filters—directly through the API. We’ll also discuss how to secure, paginate, and filter spatial data, making your API both powerful and user-friendly. Whether you're a GIS developer, a backend engineer, or just curious about spatial data, this session will give you practical tools to build and scale spatial APIs using only SQL. By the end, you'll have a clear roadmap to go from a PostgreSQL database to a fully-featured geospatial API ready for frontend integration or data sharing. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/A8EV98/ WG308 TE IRINGA krishna lodha PUBLISH DMQZ3Z@@talks.osgeo.org -DMQZ3Z [Re]discover QField[Cloud] en en 20251121T100000 20251121T102500 0.02500 [Re]discover QField[Cloud] The talk will go over QFieldCloud functionalities and refinement that have been released in the last year or so, including shared datasets to better manage storage space, on-demand attachments, near-real time positioning of field workers, and more! PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/DMQZ3Z/ WG308 TE IRINGA Berit Mohr Mathieu Pellerin PUBLISH N3KH8B@@talks.osgeo.org -N3KH8B In CesiumJS & Deck.gl, AI-based Digital Twin Service with Conversational Interface en en 20251121T110000 20251121T112500 0.02500 In CesiumJS & Deck.gl, AI-based Digital Twin Service with Conversational Interface In this presentation, we introduce an innovative AI-based conversational Digital Twin service that enables users to control a 3D map and easily explore geospatial information through voice or text commands. This service combines AI technology with S-Map, a 3D platform that integrates all spatial information of Seoul. It leverages open-source technologies such as CesiumJS for high-precision geospatial 3D rendering and Deck.gl for advanced data visualization, providing a rich geospatial interface. In the previous of S-Map, users had to go through up to 11 steps using a mouse and keyboard to retrieve specific spatial data—such as apartment rental statistics by district. Now, with a simple command like "Show me the apartment rental transaction statistics by district", the desired results appear instantly. The service supports a wide range of functions, including real estate search, building design simulation, city comparison, location search, map manipulation, and layer management—all through a conversational chat interface. By combining AI technologies with open-source tools, the system supports complex queries, real-time 3D visualization, and multi-turn interactions—making geospatial data more accessible for users. This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00143336, NTIS Grant: 2610000396). PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/N3KH8B/ WG308 TE IRINGA Sanghee Shin JaeSeon, Kim Hyeeun Ahn PUBLISH GJSSXJ@@talks.osgeo.org -GJSSXJ Openness and Collaboration as Strategic Choices – case National Land Survey of Finland en en 20251121T113000 20251121T115500 0.02500 Openness and Collaboration as Strategic Choices – case National Land Survey of Finland The National Land Survey of Finland has been an active user, and increasingly a developer, of FOSS4G solutions related to our core tasks for nearly two decades now. We also made a strategic decision in 2020 to leverage open source solutions for our major business critical IT systems. Following the strategic decision, the National Land Survey of Finland started building a new topographic data production system on open source components in autumn 2020. Core components of the system include QGIS, PostgreSQL/PostGIS and QField for field work. During spring 2025, the system was taken into full production use. Now it is time to put even more focus on the collaborative aspects: how we collaborate with other authorities, the QGIS user community and with IT companies to ensure continuity of the solution. This talk will describe - On what grounds we made the strategic decision of using open source - What are our most recent experiences with the open solution and collaboration - How we are organizing collaboration in Finland with other stakeholders - How we are looking at engaging in further collaboration with open source communities to ensure continuity of the solution - Most importantly, what benefits every public sector organization can gain by using and investing in open solutions. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/GJSSXJ/ WG308 TE IRINGA Jani Kylmäaho PUBLISH JUTGUD@@talks.osgeo.org -JUTGUD OSGeo and OGC MoU update en en 20251121T120000 20251121T122500 0.02500 OSGeo and OGC MoU update Open Software and Open Standards are complementary pieces of the geospatial ecosystem. In January 2022, OSGeo and OGC signed a new and updated version of the Memorandum of Understanding (MoU) that aims to maximize the achievement of the mission and goals of both organizations. Execution of joint Code Sprints, identifying free and open source technologies that could be used as Reference Implementations for OGC Standards and validating OGC compliance tests are examples of activities that can take place within the scope of the agreement. The current MOU can be found at https://www.osgeo.org/wp-content/uploads/MOU_OGC_OSGeo_2022_signed.pdf PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/JUTGUD/ WG308 TE IRINGA Tom Kralidis PUBLISH SMF9HS@@talks.osgeo.org -SMF9HS Vector tiles and GeoServer: dynamic vector tiles server, XYZ services, and base maps en en 20251121T133000 20251121T135500 0.02500 Vector tiles and GeoServer: dynamic vector tiles server, XYZ services, and base maps Mapbox vector tiles have emerged as a popular format for delivering geospatial data, offering dynamic rendering and interactivity for modern web maps. While not an official OGC standard, this open specification has been widely adopted, making it a staple of web cartography. This presentation delves into GeoServer's evolving capabilities to serve Mapbox vector tiles, emphasizing recent enhancements and best practices. We will explore how GeoServer leverages SLD and CSS to define the contents of vector tiles, ensuring tailored and efficient data delivery. New configuration options, such as label point generation, attribute selection and geometry coalescing, will be highlighted as tools to control and optimize tile outputs. Practical advice will also be provided for streamlining vector tile generation, helping users create seamless and scalable workflows. The session will conclude with a look at how vector tiles can serve as an input for generating high-quality base maps in various coordinate reference systems. Using OpenMapTiles styles and Planetiler, we will demonstrate how to produce visually appealing, multi-projection base maps, unlocking the full potential of vector tiles for diverse applications. Whether you're building interactive maps or generating custom base maps, this talk will equip you with the knowledge and tools to make the most of GeoServer's vector tile capabilities. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/SMF9HS/ WG308 TE IRINGA Jody Garnett PUBLISH TEFJBU@@talks.osgeo.org -TEFJBU Geospatial Cloud-Native at Scale - LINZ’s Path from Legacy Stacks to a National Data Lake en en 20251121T140000 20251121T142500 0.02500 Geospatial Cloud-Native at Scale - LINZ’s Path from Legacy Stacks to a National Data Lake Land Information New Zealand (LINZ) is transforming its systems by replatforming a complex web of point-to-point feeds and isolated data stores in favour of a single, cloud-native geospatial data lake hosted on AWS. This shift involves pushing all datasets, including vector, tabular, raster, and point cloud data, into object storage and utilising STAC metadata and cloud-optimised formats. This approach decouples data producers from consumers, making the data more accessible, reusable, and self-describing from day one. We are utilising open formats like PMTiles, COG, GeoParquet, and FlatGeobuf, enabling web applications, analytics, and external APIs to access the files through standard cloud tools. This eliminates the need for repetitive or ad-hoc ETL processes and reduces reliance on fragile bespoke solutions. This presentation will discuss the progression from concept to pilot to production. Key topics will include metadata, versioning, incorporating data quality and lineage into our catalogue, and for both open and internal data. We will also share the organisational benefits, such as faster product creation, stable access patterns for both teams and customers, and a significant reduction in integration workloads. Additionally, we’ll address the cultural shifts necessary to move away from outdated thinking. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/TEFJBU/ WG308 TE IRINGA Jeremy Palmer PUBLISH AFFDDE@@talks.osgeo.org -AFFDDE 12 billion tiles later..... en en 20251121T143000 20251121T145500 0.02500 12 billion tiles later..... Five years after launch, the Toitū Te Whenua - Land Information New Zealand Basemaps service continues to grow and find new ways to make NZ's vast collections of open geospatial data simple for users to explore and build upon. This talk will provide a quick demo of the product, then dive into some of the trickier problems the Basemaps team has solved to keep the service dirt cheap, beautiful, and lightning-fast. You should leave with a clear idea of the potential outcomes and benefits of building something similar for your geography. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/AFFDDE/ WG308 TE IRINGA Jonathan Ball PUBLISH G8NLJY@@talks.osgeo.org -G8NLJY Open Source GIS for Placemaking Education: A Gamified Framework for Community-Driven Urban Learning en en 20251121T090000 20251121T092500 0.02500 Open Source GIS for Placemaking Education: A Gamified Framework for Community-Driven Urban Learning This study introduces an innovative educational framework that integrates open-source tools, geospatial data, and gamification to enhance the teaching of urban planning and placemaking. It is specifically designed to provide young learners (particularly in schools, youth workshops, and community-based learning environments) with accessible, engaging, and interactive experiences that foster spatial awareness, critical thinking, and civic participation. At the core of this framework lies a reproducible pipeline built upon three key open-source technologies: MLIT PLATEAU (open GIS-based 3D city models), Blender (a 3D modeling and animation suite), and Godot (a lightweight, scriptable game engine). Together, these tools form a complete workflow for visualizing, simulating, and manipulating urban space through an open and participatory lens. The methodology begins with the use of MLIT PLATEAU, Japan’s national-scale open 3D geospatial dataset, which offers highly detailed, standardized models of buildings, land use, infrastructure, and terrain for dozens of cities across the country. These datasets are downloaded in CityGML or OBJ formats and imported into Blender using specialized plugins or preprocessing tools. Within Blender, the imported GIS data is converted into structured, editable 3D models that students can navigate, analyze, and reconfigure. For example, students can model new public spaces, add greenery, modify building forms, or simulate small-scale tactical interventions. Blender's node-based material system and animation capabilities also allow learners to visualize temporal changes (e.g., seasonal effects, traffic flows, construction stages), adding dynamic and narrative dimensions to their urban design proposals. Blender also plays a central role as a translation layer between GIS and game engines. After modeling and scene setup, assets are exported (typically in glTF, FBX, or OBJ formats) and imported into Godot, where the interactive and gamified layer of the project is developed. In Godot, students can script interactions using GDScript (a Python-like language), design basic user interfaces (UI), and simulate real-world phenomena such as walkability, visibility, accessibility, or user behavior. Example applications include: - creating a first-person walkthrough of a redesigned neighborhood; - developing a resource-management game to balance green space and development; - building a collaborative multiplayer simulation for participatory urban decision-making. The integration of real-time simulation through Godot allows learners to test design hypotheses, observe user reactions, or iterate urban scenarios based on feedback—all within an immersive, game-like environment. Importantly, this approach does not require high-end hardware or costly licenses, making it ideal for public schools, NGOs, or municipalities with limited digital infrastructure. The entire pipeline (GIS data (PLATEAU), 3D modeling (Blender), and simulation (Godot)) is fully open-source, ensuring transparency, reproducibility, and adaptability to local contexts worldwide. Pedagogically, the framework introduces a novel mapping between Levels of Detail (LOD) (a concept borrowed from GIS and 3D modeling) and stages of learning progression. For instance, LOD1 models are used for early-stage orientation and spatial comprehension; LOD2/3 data facilitates site-specific design challenges; and custom-built LOD4 scenarios simulate detailed human-scale interventions. This layered approach supports differentiated instruction and curriculum design, enabling instructors to tailor activities to students’ cognitive levels and technical skills. The framework is situated within a broader discourse of open-source urbanism, tactical urbanism, and participatory design. It challenges traditional, expert-driven planning paradigms by promoting tools that empower communities and students to engage directly with spatial planning through iterative, visual, and interactive means. It also contributes to the field of geospatial education, offering a replicable methodology for integrating spatial literacy, creative modeling, and digital civic engagement into urban curricula. Ultimately, this study argues that a gamified, open-source approach to urban learning not only makes complex planning tools accessible but also nurtures the next generation of urban thinkers capable of co-creating inclusive and resilient urban futures. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/G8NLJY/ WG126 Léo Martial PUBLISH FPGJ7J@@talks.osgeo.org -FPGJ7J How HOT and communities are building together the free & open humanitarian mapping suite en en 20251121T093000 20251121T095500 0.02500 How HOT and communities are building together the free & open humanitarian mapping suite How do we move from having no maps to using them effectively? We're building a set of open mapping tools for humanitarian use, developed with the help of communities around the world, drawing on shared experiences in disaster response, humanitarian relief, and community development. Imagine taking a simple drone and quickly generating your own aerial imagery, then publishing it on an open repository. You can use this imagery for mapping, even creating your own AI models if you like. Then, go to the field and enrich your data with local knowledge, leveraging super-accessible citizen mapping tools for assistance. Combine all your data on a single map. HOT tools are already being used in a wide range of situations, not only in disaster response, but also in local business development, environmental projects, and innovative applications as communities adapt and make these tools their own. Let's explore how we’re building this free and open mapping suite together, by people, for people. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/FPGJ7J/ WG126 Emilio Mariscal Leen D'hondt PUBLISH DMDEVQ@@talks.osgeo.org -DMDEVQ Fast and Free: High-performance WebGL geospatial visualisation using Lonboard and Jupyter Notebooks en en 20251121T100000 20251121T102500 0.02500 Fast and Free: High-performance WebGL geospatial visualisation using Lonboard and Jupyter Notebooks Climate modelling generates massive raster datasets that analysts must explore to identify critical patterns like flood risk zones, temperature anomalies, and adaptation pathways. However, meaningful analysis requires overlaying large vector datasets, such as roads, infrastructure points, and building footprints, to understand the real-world impacts. Geospatial analysts traditionally face a costly bottleneck: these vector datasets must be tiled and stored in databases before meaningful visualisation can occur. This preprocessing step incurs significant computational and storage costs while creating friction in exploratory workflows. This presentation demonstrates how Lonboard, a high-performance WebGL-based vector visualisation library, eliminates this barrier entirely, reducing workflow costs to zero while delivering superior rendering performance for the critical vector overlays. Through demonstrations using Urban Intelligence's climate analysis workflows, we'll showcase how analysts can visualise massive vector datasets directly in Jupyter Notebooks without preprocessing. Lonboard's WebGL rendering achieves speeds orders of magnitude faster than traditional packages like ipyleaflet by leveraging cutting-edge technologies, such as GeoArrow and GeoParquet, in conjunction with GPU-based map rendering. Key benefits include elimination of costly vector preprocessing steps, instant visualisation feedback for rapid exploration, seamless Jupyter integration, and superior performance with large vector datasets essential for large datasets. The presentation features fully reproducible notebooks showcasing production-ready implementation practices. This approach represents a fundamental shift from expensive, preprocessing-heavy workflows to cost-effective, exploration-first methodologies that accelerate workflows. By leveraging open-source tools, organisations achieve better analytical outcomes while reducing infrastructure costs and complexity. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/DMDEVQ/ WG126 Sam Archie PUBLISH RZN87X@@talks.osgeo.org -RZN87X pygeoapi project status en en 20251121T110000 20251121T112500 0.02500 pygeoapi project status 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. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/RZN87X/ WG126 Tom Kralidis Angelos Tzotsos Just van den Broecke PUBLISH QS8TSD@@talks.osgeo.org -QS8TSD OGC APIs with GeoServer: implementation, availability, and next steps en en 20251121T113000 20251121T115500 0.02500 OGC APIs with GeoServer: implementation, availability, and next steps 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, 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. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/QS8TSD/ WG126 Jody Garnett PUBLISH 9Y9PEA@@talks.osgeo.org -9Y9PEA State of GeoNetwork en en 20251121T120000 20251121T122500 0.02500 State of GeoNetwork The GeoNetwork opensource project is a catalog application that makes it easy to discover resources across any local, regional, national or global Spatial Data Infrastructure (SDI). GeoNetwork is a mature technology, an OSGeo Project and a proud member of the FOSS4G community for over 20 years. The GeoNetwork team invites you to hear what we’ve been up to in 2025! We will showcase the many community-driven projects that have added new features over the past twelve months. Our thriving ecosystem of schema plugins keeps growing, with national teams contributing fixes, enhancements and entirely new capabilities, now including DCAT-AP output support and integrations. You will also get an insider’s look at the GeoNetwork team’s plans: the current status of our 4.2.x and 4.4.x branches, our upcoming release schedule and an early preview of GeoNetwork 5. We’ll share a high-level roadmap for GeoNetwork 5 and updates on the crowdfunding effort to help bring it to life. Join us for the latest from the GeoNetwork community and this ever-evolving platform! PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/9Y9PEA/ WG126 Antonio Cerciello Jody Garnett PUBLISH DZEH9X@@talks.osgeo.org -DZEH9X A cloud based solution for Indigenous data sovereignty: protecting biodiversity management data in Aotearoa New Zealand en en 20251121T133000 20251121T135500 0.02500 A cloud based solution for Indigenous data sovereignty: protecting biodiversity management data in Aotearoa New Zealand How can Indigenous communities retain full control over their geospatial data in a world dominated by cloud platforms? This talk offers a guided walkthrough of a working prototype that protects Indigenous geospatial knowledge through privacy-first design. Attendees will see how technologies like geomasking, in-browser encryption, and blockchain notarization come together in a seamless tool for secure data sharing — all without relying on external intermediaries. The session will highlight real-world use in Māori-managed Biodiversity Areas in Aotearoa New Zealand and discuss broader ethical and technical considerations around Indigenous data governance. The talk is especially relevant for those interested in GIS, privacy tech, Indigenous rights, and open-source innovation. Link to article: https://onlinelibrary.wiley.com/doi/10.1111/tgis.13153 PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/DZEH9X/ WG126 Dr Pankajeshwara Sharma PUBLISH PCPQKJ@@talks.osgeo.org -PCPQKJ MapSafe QGIS plugin: a complete open-source geoprivacy tool for desktop GIS applications en en 20251121T140000 20251121T142500 0.02500 MapSafe QGIS plugin: a complete open-source geoprivacy tool for desktop GIS applications This session introduces MapSafe, a desktop geoprivacy plugin for QGIS that empowers users to secure sensitive spatial data directly within their existing GIS workflows. Through a live demonstration, attendees will see how the plugin anonymizes data using techniques like donut masking and hexagonal binning, protects original datasets via public-key encryption, and verifies dataset authenticity using blockchain notarization. The talk will highlight how MapSafe avoids reliance on third parties, scales to large datasets, and supports regulatory compliance. Designed for usability, the plugin includes tooltips, documentation, and video guides, making geoprivacy accessible to users across skill levels. Whether you're a GIS professional, data steward, or researcher, this talk will offer practical insights into integrating geoprivacy directly into your desktop environment. Link to article: https://www.tandfonline.com/doi/full/10.1080/17489725.2025.2497752?src=exp-la PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/PCPQKJ/ WG126 Dr Pankajeshwara Sharma PUBLISH UARSRW@@talks.osgeo.org -UARSRW Approaching Security with Kindness and Compassion en en 20251121T143000 20251121T145500 0.02500 Approaching Security with Kindness and Compassion This talk explores the tensions, expectations, terrors and triumphs on this hot button topic. We will look at a sensible response to calls for regulation and how GeoSever and GeoNetwork policies have been updated to address these concerns for developers, participating organizations and members of the public. This talk unpacks what this can look like for foss4g projects using real world examples. * Built around the experience of the GeoServer project, and the resulting security policy and practices that can serve as a template for our foss4g community. * Public institutions can attend this talk to learn how their security policies interact with and affect foss4g technologies. * Vendors and service providers can learn how open source supply chains affect their products. * FOSS4G projects can attend to learn how to approach security reports with compassion, and a bit of boundary setting, to take care of your codebase and community. Security is difficult with consequences being felt at all levels. Help meet this challenge by supporting yourself and each other. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/UARSRW/ WG126 Jody Garnett PUBLISH Z3ASEE@@talks.osgeo.org -Z3ASEE OGC CITE Runner: a Pythonic convenience runner for OGC compliance testing en en 20251121T090000 20251121T092500 0.02500 OGC CITE Runner: a Pythonic convenience runner for OGC compliance testing The Open Geospatial Consortium API family of standards (OGC API) are being developed to make it easy for anyone to provide geospatial data to the web, and are the next generation of geospatial web API standards designed with resource-oriented architecture, RESTful principles and OpenAPI. In addition, the OGC has the CITE compliance program, which aims at providing test suites that can be used to verify if web applications implement the standards correctly. Official CITE testing is done using OGC infrastructure and is subject to a review process which can be time consuming for implementations seeking compliance certification. The official CITE test suites and test runner (OGC TeamEngine) are made available by the OGC so that implementations are able to test their compliance beforehand. This process is however not very straightforward to automate. The ogc-cite-runner software aims at reducing the friction of running the mentioned official CITE test suites with the OGC TeamEngine test runner. It is a lightweight Python CLI application which can be used directly as a GitHub action or as a standalone tool, thus making it easy to include in Continuous Integration systems. This means that implementations become able to test their CITE compliance as part of their normal development workflow and gain near instant feedback on their compliance status. At its core, ogc-cite-runner implements a thin layer of automation over OGC TeamEngine, asking it to run CITE test suites. It then processes the output results into a number of output formats, including a Markdown report which is embedded directly in the GitHub actions workflow log. This presentation will provide an overview of ogc-cite-runner, demonstrating how it can be run both as a GitHub action and in standalone in order to test OGC CITE compliance of geospatial web applications. https://osgeo.github.io/ogc-cite-runner/ PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/Z3ASEE/ WA220 Tom Kralidis Ricardo Garcia Silva PUBLISH EP3CXX@@talks.osgeo.org -EP3CXX Introduction to the Discrete Global Grid Abstraction Library (DGGAL) en en 20251121T093000 20251121T095500 0.02500 Introduction to the Discrete Global Grid Abstraction Library (DGGAL) DGGAL provides a common interface to perform various operations on Discrete Global Grid Reference Systems (DGGRS), facilitating the implementation of Discrete Global Grid Systems (DGGS), including implementing Web APIs based on the [OGC API - DGGS Standard](https://docs.ogc.org/DRAFTS/21-038r1.html). DGGAL, including the `dgg` command-line utility as well as the Python bindings to the library, can be installed from [from PyPI](https://pypi.org/project/dggal/) with `pip install dggal`. DGGAL currently supports all three DGGRS described in [OGC API - DGGS Annex B](https://docs.ogc.org/DRAFTS/21-038r1.html#annex-dggrs-def), as well as additional DGGRSs: * [GNOSIS Global Grid](https://docs.ogc.org/DRAFTS/21-038r1.html#ggg-dggrs): An axis-aligned quad-tree defined in WGS84 latitude and longitude, with special handling of polar regions, corresponding to the [OGC 2D Tile Matrix Set of the same name](https://docs.ogc.org/is/17-083r4/17-083r4.html#toc58) * [ISEA3H](https://docs.ogc.org/DRAFTS/21-038r1.html#isea3h-dggrs): An equal area hexagonal grid with a refinement ratio of 3 defined in the Icosahedral Snyder Equal Area (ISEA) projection * [ISEA9R](https://docs.ogc.org/DRAFTS/21-038r1.html#isea9r-dggrs): An equal area rhombic grid with a refinement ratio of 9 defined in the ISEA projection transformed into a 5x6 Cartesian space resulting in axis-aligned square zones * **IVEA3H**: An equal area hexagonal grid with a refinement ratio of 3 defined in the Icosahedral Vertex-oriented Great Circle Equal Area (tentatively called IVEA) projection based on [Slice & Dice (2006)](https://www.tandfonline.com/doi/abs/10.1559/152304006779500687), using the same global indexing and sub-zone ordering as for ISEA3H * **IVEA9R**: An equal area rhombic grid with a refinement ratio of 9 defined in the IVEA projection transformed into a 5x6 Cartesian space resulting in axis-aligned square zones, using the same global indexing and sub-zone ordering as for ISEA9R * **RTEA3H**: An equal area hexagonal grid with a refinement ratio of 3 defined in the Rhombic Triacontahedron Equal Area (RTEA) projection, a configuration of the Slice & Dice great circle projection equivalent to applying Snyder's projection to the RT, using the same global indexing and sub-zone ordering as for ISEA3H * **RTEA9R**: An equal area rhombic grid with a refinement ratio of 9 defined in the RTEA projection transformed into a 5x6 Cartesian space resulting in axis-aligned square zones, using the same global indexing and sub-zone ordering as for ISEA9R * [rHEALPix](https://iopscience.iop.org/article/10.1088/1755-1315/34/1/012012): An equal area and axis-aligned grid with square zones topology and a refinement ratio of 9 defined in the rHEALPix projection (using same parameters as default [PROJ implementation](https://proj.org/en/stable/operations/projections/rhealpix.html)) with the original hierarchical indexing and scanline-based sub-zone ordering The API documentation can be [found here](https://dggal.org/docs/html/dggal/Classes/DGGRS.html). The `DGGRS` class provides most of the functionality of the library, allowing to resolve DGGRS zones by textual ID to a unique 64-bit zone integer identifier (`DGGRSZone`). The geometry and sub-zones of a particular zone can also be queried. The concept of [sub-zones](https://docs.ogc.org/DRAFTS/21-038r1.html#term-sub-zone) is key to encoding both vector and raster geospatial data quantized to a DGGRS. The DGGAL library also allows to resolve a sub-zone index at a particular depth from a parent zone, allowing to read DGGS-optimized data such as [DGGS-JSON](http://dggs-json.org) and [DGGS-JSON-FG](https://docs.ogc.org/DRAFTS/21-038r1.html#rc_data-dggs-jsonfg). The recommended method to obtain and build DGGAL and the `dgg` tool is to follow the instructions in [BUILDING.md](BUILDING.md), or running [fetchAndBuild.sh](fetchAndBuild.sh) / [fetchAndBuild.bat](fetchAndBuild.bat). While the library is written in the [eC programming language](https://ec-lang.org), object-oriented bindings for C, C++ and Python generated using the Ecere SDK's [`bgen` tool](https://github.com/ecere/bgen) are provided. Bindings for Rust are available as well. Support for additional languages may be added in the future. Example usage of the bindings to the different programming languages can be [found here](https://github.com/ecere/dggal/tree/eC-core/bindings_examples). The `dgg` tool provides the ability to perform various operations from the command-line, including generating grids at different refinement levels, querying information about a particular zone identifier, identifying the zone a particular set of geospatial coordinates, listing the zones within a certain bounding box, resolving sub-zone indices and converting compact DGGS-JSON data files to GeoJSON files. _Acknowledgement_ Financial support provided by GeoConnections, a national collaborative initiative led by Natural Resources Canada. GeoConnections supports the modernization of the Canadian Geospatial Data Infrastructure (CGDI). The CGDI is the collection of geospatial data, standards, policies, applications, and governance that facilitate its access, use, integration, and preservation. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/EP3CXX/ WA220 Jerome St-Louis PUBLISH SMBE7D@@talks.osgeo.org -SMBE7D pycsw project status en en 20251121T100000 20251121T102500 0.02500 pycsw project status pycsw is an OGC API - Records and OGC CSW server implementation written in Python. Started in 2010 (more formally announced in 2011), pycsw allows for the publishing and discovery of geospatial metadata via numerous APIs (CSW 2/CSW 3, OpenSearch, OAI-PMH, SRU), providing a standards-based metadata and catalogue component of spatial data infrastructures. pycsw is Open Source, released under an MIT license, and runs on all major platforms (Windows, Linux, Mac OS X).The project is certified OGC Compliant, and is an OGC Reference Implementation. The project currently powers numerous high profile catalogues such as EOEPCA, IOOS, NGDS, NOAA, US Department of State, US Department of Interior, geodata.gov.gr, Met Norway and WMO WOUDC. This session starts with a status report of the project, followed by an open question answer session to give a chance to users to interact with members of the pycsw project team. This session will cover how the project PSC operates, the current project roadmap, and recent enhancements focused on ESA's EOEPCA, Open Science Data Catalogue and OGC API - Records. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/SMBE7D/ WA220 Tom Kralidis Paul van Genuchten Angelos Tzotsos PUBLISH DTAD37@@talks.osgeo.org -DTAD37 Combining remote sensing data and geospatial datasets to improve a national wetlands inventory en en 20251121T110000 20251121T112500 0.02500 Combining remote sensing data and geospatial datasets to improve a national wetlands inventory _ PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/DTAD37/ WA220 Bex Dunn PUBLISH 7LQM3T@@talks.osgeo.org -7LQM3T Automatic map deconstruction using QGIS: Leveraging open-source algorithms such as map2loop and LoopStructural en en 20251121T113000 20251121T115500 0.02500 Automatic map deconstruction using QGIS: Leveraging open-source algorithms such as map2loop and LoopStructural The integration of geological knowledge from legacy maps into modern 3D geological models remains a critical challenge, particularly in regions where data is sparse or inconsistently digitized. This research presents an automated workflow for geological map deconstruction using QGIS, and 3D modelling, using open-source algorithms such as map2loop and LoopStructural. The aim is to streamline the transformation of 2D geological maps into structured data, reducing manual interpretation efforts and increasing reproducibility. The proposed methodology leverages map2loop to extract and pre-process from georeferenced maps geological features such as contacts, faults, and stratigraphic boundaries. Some of these features are then structured into formats compatible with the input of LoopStructural, a Python-based library that builds rule-based 3D geological models. The QGIS environment acts as a central platform for visualizing and manipulating these spatial data layers, while our plugins facilitate automation and interoperability between components. This approach underscores the value of open-source tools in geoscience workflows, fostering collaboration, transparency, and scalability. By automating the extraction and structuring of geological features, the workflow significantly reduces the subjectivity and time requirements traditionally associated with map deconstruction and 3D model builds. The results demonstrate the feasibility of automated map deconstruction within an open-source GIS environment, paving the way for rapid geological model development in underexplored or poorly mapped regions. This work contributes to the broader geoscience community by offering a replicable and extensible framework for geological data translation, bridging the gap between static to support systems for integration with additional data sources such as drillholes and geophysical data, enhancing the fidelity of downstream 3D models. Repository: Loop3D • https://github.com/Loop3D/map2loop • https://github.com/Loop3D/LoopStructural • https://github.com/Loop3D/qgis-loopplugin Field Mapping Tool • https://github.com/swaxi/GEOL-QMAPS • https://zenodo.org/records/13374088 Geophysical Processing Tool • https://github.com/swaxi/SGTool Tomofast-x Grav/Mag Inversion Tool • https://github.com/TOMOFAST/Tomofast-x • https://github.com/TOMOFAST/tomofast-x-q PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/7LQM3T/ WA220 Michel Nzikou Mamboukou PUBLISH BAQUZY@@talks.osgeo.org -BAQUZY Empowering Everyone with Satellite Data – Agile Development Powered by FOSS4G en en 20251121T120000 20251121T122500 0.02500 Empowering Everyone with Satellite Data – Agile Development Powered by FOSS4G ArkEdge Space Inc. is a system integrator specializing in ultra-small satellites, having successfully launched and operated thirteen spacecraft to date. In 2024, based on the "Free and Open" philosophy, we initiated development of a platform aimed at making satellite-derived geospatial data easily accessible to everyone. With the support of FOSS4G, within just one year, we developed multiple practical applications, including a forestry-management tool for local governments, an agricultural decision-support system for smallholder farmers and ministries in Global South countries, an environmental monitoring application, and applications for flood monitoring and water resource management. These tools enable non-experts to easily access, visualize, and analyze satellite data without requiring specialized knowledge. We have collaborated with government agencies in Global South countries during the development of these applications. Currently, many of these applications have been deployed free of charge to government agencies and primary-sector workers in Global South countries, as well as to local municipalities and educational institutions within Japan. Furthermore, we have actively provided the source code of our applications to other Web IT application companies. By doing so, we aim to enable a broader range of players—including ordinary IT companies without specialized satellite data expertise—to develop applications leveraging satellite data. By adopting a rapid iteration approach—frequently developing, testing, and refining mock-up applications based on user feedback—we quickly produced a wide range of solutions tailored to user needs. FOSS4G technologies played a crucial role in enabling this agile development process. Specifically, we actively utilized various FOSS4G technologies such as MapLibre GL JS, PMTiles, TiTiler, and GDAL. Additionally, we leveraged open-source image processing models such as Segment Anything Model 2. The combination of TiTiler and AWS Lambda proved particularly powerful, supporting not only fast raster data delivery, rapid time-series processing, and data download tools, but also more specialized use cases like rainfall analysis applications. In this way, we adopt the perspective of end users working with satellite-derived geospatial data. This hands-on experience gives us insight into the challenges of leveraging geospatial information—especially satellite data, which is large in size, stored in specialized formats, and requires expert knowledge for processing and analysis. Drawing on these insights, we are committed to further improving the usability of Free and Open geospatial data—including satellite datasets—and to providing an accessible data-delivery experience for all. In this presentation, we will introduce our journey, discuss the underlying technologies, and share our vision for creating a society where satellite data is effortlessly accessible to everyone. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/BAQUZY/ WA220 Ryo Suzumoto PUBLISH GQLRGC@@talks.osgeo.org -GQLRGC Can FOSS4G be utilized to support the post-mortem assessment process of large-scale chemical spills? en en 20251121T133000 20251121T135500 0.02500 Can FOSS4G be utilized to support the post-mortem assessment process of large-scale chemical spills? When toxic substances that are lethal to humans are accidentally released, for example, in a large-scale chemical plant that leaks gaseous toxic substances into the atmosphere, or in a car accident that causes liquid toxic substances to leak from a vehicle carrying toxic substances and seep into the soil or flow into a river, it is very difficult to assess the damage caused by the accident. This is because these large-scale incidents occur so infrequently that there is very little data available for engineering and mathematical analysis, it is difficult to re-create the incident for experiments, and it is difficult to identify the victims in the vicinity of the incident. The idea behind the study was that, aside from soil and water contamination, which requires long-term monitoring of both people and the environment, it may be possible to assess short-term damage caused by toxic gases leaking into the air in an environment like South Korea, where the number of mobile phone subscribers is high relative to the total population. If you have an atmospheric diffusion model backed by computing power, accurate information about the incident (type of leak, amount of leak, duration, location, etc.), weather information about the location at the time of the incident, and terrain and building model data that can be used as background data for the atmospheric diffusion model, you can obtain a three-dimensional grid with predictions of the spread of the leaked substance over time. And then you can add the prediction result data on the moving paths of the expected victims in the vicinity of the incident over time provided by the mobile service provider and the physical information of the expected victims (gender, age, weight, etc.) to create a model that can calculate the expected damage to each expected victim. Furthermore, if the results of the spread prediction and the estimated damage to the expected victims can be made available to decision makers involved in damage assessment on a digital twin basis, it can support the damage assessment process for chemical spills. In this talk, we will present a prototype of a system that can support damage assessment tasks by combining the results of atmospheric diffusion model-based spread predictions, location information of expected victims, and estimated damages assessed based on these predictions with digital twin-based web services, which we are working on in our fourth year of research. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/GQLRGC/ WA220 Hak joon Kim PUBLISH 9V37PB@@talks.osgeo.org -9V37PB Building Communities: This is broken, and WE'RE going to fix it en en 20251121T143000 20251121T145500 0.02500 Building Communities: This is broken, and WE'RE going to fix it The Lone Wolf Trap Many geospatial open source projects start with a brilliant developer saying "This is broken and I'm going to fix it." They build something amazing, gather users, and then... burn out. The project stagnates or dies with its creator. Sound familiar? The Community Alternative What if instead we built projects where the response to problems is "This is broken and WE'RE going to fix it"? Where multiple people can spot issues, propose solutions, and drive improvements? Where the project outlives any single contributor? PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/9V37PB/ WA220 Daniel ODonohue PUBLISH LCVGKG@@talks.osgeo.org -LCVGKG i.hyper: processing hyperspectral imagery in GRASS en en 20251121T090000 20251121T092500 0.02500 i.hyper: processing hyperspectral imagery in GRASS Introduction Hyperspectral remote sensing provides rich spectral information that enables advanced analysis across a wide range of environmental domains. Soil monitoring, biogeochemistry, vegetation dynamics, environmental conservation, and even marine and coastal remote sensing all benefit from the high spectral resolution of modern sensors. Although missions like EnMAP (DLR) and PRISMA (ASI) are already operational, and Hyperion (NASA EO-1) has left behind a valuable legacy archive, new missions such as ESA’s FLEX and CHIME, along with superspectral systems like Landsat Next, are expected to dramatically expand the volume and variety of data. However, integrating this data into open-source spatial workflows remains limited. To bridge this gap, we introduce i.hyper, a modular suite for GRASS that enables complete and reproducible hyperspectral workflows - from import and preprocessing to spectral analysis and export. The addon is available in the official GRASS Addons repository and can be installed directly from GRASS. Overview of i.hyper i.hyper is implemented in Python and tightly integrated into the GRASS environment, combining modern spectral processing with robust spatial analysis tools. The suite consists of five core modules: 1. i.hyper.import – Import of hyperspectral satellite products 2. i.hyper.preproc – Spectral preprocessing and transformation 3. i.hyper.composite – Plotting of false composite maps from already imported 3D hyperspectral rasters 4. i.hyper.explore – Interactive spectral analysis, plot and export of analyzed spectra 5. i.hyper.export – Conversion to external formats These modules form a pipeline that allows users to ingest, process, and explore hyperspectral cubes using GRASS-native structures. Module 1: i.hyper.import Supports: • PRISMA L2B–L2D • Tanager • EnMAP L2A It imports imagery and metadata into GRASS 3D rasters (raster_3d), including per-band attributes (wavelength, FWHM, units, validity). Invalid bands are flagged. Optional composite layers (e.g., CIR, SWIR) can be created automatically for visualization. Modular backends make extension to new missions straightforward. Module 2: i.hyper.preproc Provides in-place spectral processing with support for: • Savitzky-Golay filtering (configurable order, derivative, window size, NaN handling) • Dimensionality reduction methods as Principal Component Analysis (PCA) and Kernel PCA (RBF kernel) • Continuum removal using local convex hull normalization These transformations are applied spectrally per pixel, using garray.array3d, with preserved metadata throughout. Module 3 i.hyper.composite Provides tools to create false composite RGG maps from 3D hyperspectral cube. Users can choose predefined false composites combinations (eg. for agriculture, geology) or define custome ones by choosing the wavelenght centers of the bands they want to stack into composite maps. Module 4: i.hyper.explore Enables: • Interactive selection of pixels/regions • Visualization of spectral signatures • Export of spectra to JSON • Export of plots This module is particularly useful for validating preprocessing steps and preparing datasets for modeling. Module 5: i.hyper.export Supports export to: • GeoTIFF Users can select bands, apply scaling, and retain metadata during export. Reproducibility and Integration i.hyper is fully open-source (GNU GPL 2.0), integrated via the GRASS Python API, and leverages libraries like NumPy, scikit-learn and SciPy. It ensures reproducibility through: • Strict input structure and metadata validation • Logging of transformation parameters • Use of GRASS-native tools All modules include documentation, CLI help, argument checks, and consistent metadata propagation. Conclusion i.hyper is novel in its integration of hyperspectral processing within GRASS, unlocking a powerful combination of spectral and spatial tools. It facilitates comprehensive workflows - from raw data to analysis-ready layers - and supports both legacy and current missions with a roadmap for future expansion. As more hyperspectral and superspectral missions emerge - including FLEX, CHIME, and Landsat Next - the need for transparent, extensible, and scriptable pipelines becomes critical. i.hyper addresses this need by offering a scalable, modular, and reproducible solution that enables open-source geospatial research across domains such as soil science, geochemistry, vegetation monitoring, conservation planning, and marine ecosystems. The GRASS + Python + i.hyper stack provides a robust foundation for both research and operational remote sensing. PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/LCVGKG/ WG404 Alen Mangafić Tomaž Žagar PUBLISH 89FTBG@@talks.osgeo.org -89FTBG Benchmarking OGC Services: No More Surprises When 20 Students Try to Access Your WMS en en 20251121T093000 20251121T095500 0.02500 Benchmarking OGC Services: No More Surprises When 20 Students Try to Access Your WMS Picture this, Sicily 2025 : You've carefully set up a WMS service, tested it works perfectly, and then invited 20 students to use it while lecturing about OGC web services. Suddenly, your service crawls to a halt. This scenario highlights a critical gap in how we deploy OGC services - most organizations perform only functional testing, checking if services work but not how they perform under real user loads. Standard load testing tools like ApacheBench fall short for geospatial services because they are not oriented to deal with the geographic complexity of OGC protocols. Real users don't request the same map tile repeatedly - they pan across different bounding boxes, zoom through multiple levels, switch layer combinations, and explore various geographic extents. OGC services require specialized testing that can generate random bounding boxes for WMS GetMap requests, create dynamic tile coordinates for WMTS across zoom levels, handle proper CRS and format parameters, and simulate realistic navigation patterns. We developed an open-source solution using the Locust framework enhanced with OGC-specific Python code modules. Our approach leverages owslib.wms for proper capabilities parsing and request generation, custom bbox generators that create random geographic extents within service bounds, tile coordinate algorithms for realistic requests, and layer randomization from GetCapabilities responses. The framework includes seed-based reproducible testing for consistent benchmark comparisons and simulates authentic user behavior patterns including concurrent users requesting different geographic areas and dynamic layer combinations during sessions. This work addresses a fundamental challenge in the FOSS4G ecosystem where standard load testing misses the geographic complexity that makes OGC services unique. By providing specialized benchmarking tools that understand geospatial request patterns, organizations can systematically validate performance before deployment and avoid discovering critical issues when users start actually exploring their services geographically. The toolkit enables proactive capacity planning and helps prevent those embarrassing moments when carefully planned demonstrations fail under realistic load conditions. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/89FTBG/ WG404 Jorge S. Mendes de Jesus PUBLISH 8MQLNE@@talks.osgeo.org -8MQLNE Building a simple geospatial web app en en 20251121T100000 20251121T102500 0.02500 Building a simple geospatial web app I'll show you how i built a relatively simple web application that uses open source tools (Python, Dash, Leaflet, GeoParquet) and open data (NZ Census 2023, NZ building outlines) to estimate NZ census counts within custom geographies. I'll share some tips and tricks i learned along the way and hopefully inspire you to make your own geo web app. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/8MQLNE/ WG404 Alexander Raichev PUBLISH KBQPBJ@@talks.osgeo.org -KBQPBJ Mapping the World, Empowering People: QField’s Vision in Practice en en 20251121T110000 20251121T112500 0.02500 Mapping the World, Empowering People: QField’s Vision in Practice QField helps people map and understand the world—enabling them to solve everyday tasks and tackle global challenges. In this talk, we’ll dive into how QField is used in diverse contexts: from conservation and climate action to infrastructure planning and public service delivery. You’ll hear how the app’s open-source foundation, intuitive interface, and powerful features are making high-quality field data collection accessible to all. We’ll also showcase how QField supports the UN Sustainable Development Goals and fosters a global community of practitioners working for impact. Whether you're new to QField or a long-time contributor, this session will show how the vision of "mapping the world, empowering people" is being realized every day. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/KBQPBJ/ WG404 Marco Bernasocchi PUBLISH KQX3KA@@talks.osgeo.org -KQX3KA Use of Freeware to support Regional Capability Development en en 20251121T113000 20251121T115500 0.02500 Use of Freeware to support Regional Capability Development IIC have recently undertaken a series of unique projects where we have intentionally leveraged the benefits and cost savings of freeware and open source software to achieve greater outcomes for our clients and the community at large. These projects have included the delivery of Capacity Development Training in the Pacific to nearly 100 students maximising the use of freeware and online tools; the development of a Spatial Data Infrastructure and knowledge base for Pacific Islands nations that leverages opensource software to maximise its outcomes; a QGIS plugin that is available to the community that allows the automated conversion of bathymetric data to a chart-like product for use in Electronic Chart Systems (ECS), and finally the development of an advanced Electronic Chart System App that utilises National Authority official S-57 data, that is freely available for use by the public. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/KQX3KA/ WG404 DAVID CROSSMAN PUBLISH DZRYCJ@@talks.osgeo.org -DZRYCJ Earth Search: Expanding Open Access to Sentinel-2 and Beyond en en 20251121T120000 20251121T122500 0.02500 Earth Search: Expanding Open Access to Sentinel-2 and Beyond Earth Search is Element 84’s free, cloud-native catalog that makes public Earth observation data—like Sentinel-1 and Sentinel-2—discoverable, analysis-ready, and open to all. This talk will share an in-depth update on the current state of Earth Search, the pipelines that power it, and our recent work to ensure long-term stability and transparency. We’ll cover how the team has taken over the Sentinel ingestion pipeline on AWS, tackled metadata gaps and ingestion bugs, and improved the catalog’s coverage and accuracy. Attendees will learn how the pipeline generates Cloud-Optimized GeoTIFFs and rich STAC metadata that follows current best practices and extensions—making advanced cloud-native workflows possible. We’ll also discuss our plans to open source the entire Earth Search processing stack, explain how the community can benefit from and contribute to it, and share progress on addressing known gaps in the Sentinel-2 archive. From debugging broken asset links to filling missing scenes and deprecating legacy collections, this talk will give a transparent look at the challenges of maintaining a massive, open geospatial archive—and what’s next as we continue to expand reliable, standards-based access for everyone. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/DZRYCJ/ WG404 Matthew Hanson PUBLISH P97F9C@@talks.osgeo.org -P97F9C Look ma, no hands: Automating Open-Source Geospatial Infrastructure with AWS CDK (IaC) en en 20251121T133000 20251121T135500 0.02500 Look ma, no hands: Automating Open-Source Geospatial Infrastructure with AWS CDK (IaC) Modern geospatial applications demand robust, scalable, and repeatable infrastructure. At Abley, we’ve developed a mapping stack—built on open-source components like PostGIS, GeoServer, and MapLibre—deployed using a fully automated Amazon Web Services (AWS) infrastructure-as-code (IaC) pipeline. Central to our approach is a reusable AWS Cloud Development Kit (CDK) library that encapsulates best practices for naming, tagging, and resource retention, ensuring consistency and compliance across all environments. This talk will showcase how our infrastructure-as-code (Iac) strategy accelerates the deployment of Abley’s SafeSystem suite, a set of road safety applications and APIs. By centralizing infrastructure logic in a shared CDK library, we enable rapid provisioning, environment-specific configuration, and seamless integration with continuous integration and continuous deployment (CI/CD) pipelines. Our architecture supports high-availability and cost-effective geospatial services. Attendees will gain practical insights into: • Structuring a reusable CDK library for shared infrastructure code and multiple CDK applications. • Using the AWS CDK to enforce organizational standards and automate resource management. • Leveraging environment-driven configuration for safe, flexible deployments. • Integrating open-source geospatial tools with modern cloud infrastructure. Whether you’re building public mapping services or internal analytics, this session will provide actionable patterns for deploying and managing geospatial infrastructure at scale with AWS and the CDK. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/P97F9C/ WG404 Subodh Dangwal PUBLISH N8FMS9@@talks.osgeo.org -N8FMS9 Securing STAC APIs: Auth Patterns and a Proxy-Based Approach en en 20251121T140000 20251121T142500 0.02500 Securing STAC APIs: Auth Patterns and a Proxy-Based Approach As STAC APIs power more real-world applications, authentication (authN) and authorization (authZ) become essential. Yet, the STAC specification leaves these concerns to be addressed by implementers. In this talk, we outline common auth needs seen across STAC deployments, including: - **Route-level access control** - marking some or all endpoints as private - **Record-level filtering** - limiting collections or items by request context such as user, group, or role - **Asset-level access** - transferring our authN policies to the asset files themselves We’ll introduce [**stac-auth-proxy**](https://github.com/developmentseed/stac-auth-proxy), a backend-agnostic FastAPI-based proxy that integrates with any modern STAC API and OIDC authentication server (e.g., Keycloak, AWS, Cognito, Auth0). We will discuss how we utilize existing extensions such as the [Authentication Extension](https://github.com/stac-extensions/authentication), [Filter Extension](https://github.com/stac-api-extensions/filter), [Collection Search](https://github.com/stac-api-extensions/collection-search), and [Transaction Extension](https://github.com/stac-api-extensions/transaction) to build a secure and self-descriptive STAC API. Finally, we will discuss how stac-auth-proxy can be integrated with external policy engines, such as [Open Policy Agent](https://www.openpolicyagent.org/), to provide a comprehensive decoupled solution. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/N8FMS9/ WG404 Anthony Lukach PUBLISH Z88HKS@@talks.osgeo.org -Z88HKS The Pacific Data Hub, a federated data platform en en 20251121T143000 20251121T145500 0.02500 The Pacific Data Hub, a federated data platform The **Pacific Data Hub** platform is the one-stop-shop for anyone who seeks data about the Pacific region across multiple domains including population statistics, fisheries science, climate change adaptation, disaster risk reduction, public health, food security and human rights. It was designed as a set of highly-specialised, open-source based data management systems covering the different types of data (array-oriented scientific data, geospatial datasets, statistical data cubes, and documents) overarched by a catalog of metadata forming a **federated data platform**. The catalog also references data from national governements, regional organizations, and global partners. The datasets are indexed in the CKAN-based catalog via harvesters or entry forms. The metadata is then re-disseminated by CKAN with a centralised schema and taxonomy via Data Catalog Vocabulary (DCAT). Whenever it is possible, the catalog provides links to the data either as direct download link or OGC standards APIs. The federated data platforms includes: - [**Geonode**](https://geonode.org/) for Geospatial datasets (DEMs, bathymetry, administrative boundaries, risk assessment output models, ...) - THREDDS and [**TDS**](https://www.unidata.ucar.edu/software/tds/) for scientific data arrays (sea surface height, temperature, wave, current, tides, salinity, ...) - [**.Stat Suite**](https://sis-cc.gitlab.io/dotstatsuite-documentation/) for statistical indicators (SDGs, population, economy, trade, ...) Those platforms are completed by dashboards, tools and visualisation apps connected to the data via APIs to provide insights to inform better policy development and decision-making including: - the [Pacific Maritime Boundaries Dashboard](https://pacificdata.org/dashboard/maritime-boundaries) is the regional tool for sharing information about the Pacific island maritime boundaries and zones. Based on Drupal pages, it provides map views of EEZs and other maritime zones and links to their associated treaties. - the [Blue Pacific 2050 Dashboard](https://blue-pacific-2050.pacificdata.org/) is a NextJS dashboard that display charts of key development indicators pulling data from a SDMX APIs. - [PacificMap](https://map.pacificdata.org/) is a generic map viewer that can connect to the CKAN catalog to display the __Geonode__-hosted GIS datasets but also the .Stat statistical indicators as chloropeth maps. - the [Pacific Ocean Portal](https://oceanportal2.spc.int/) provides access to historical and forecast ocean conditions as well as in-situ observations provided by __TDS__. This presentation is about giving details on the standards, technologies and tools deployed within the platform, describing the API-based interconnections and the data workflows involved. It will also provide an overview of the challenges encountered when maintaining a platform with reliable and timely data coming from a wide diversity of providers. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/Z88HKS/ WG404 Thomas Tilak Stanislas Ozier PUBLISH 3NRE7Q@@talks.osgeo.org -3NRE7Q Development of an Information Extraction System for Mobile LiDAR Survey Data using Free and Open Source Technologies en en 20251121T090000 20251121T092500 0.02500 Development of an Information Extraction System for Mobile LiDAR Survey Data using Free and Open Source Technologies 1. Introduction • Rapid advancements in technology have improved 3D visualization techniques in Geographic Information Systems (GIS). Open-source solutions can provide the functionality to store and visualise large quantities of GIS data in multiple formats. • This paper describes a WebGIS application for visualizing Mobile LiDAR GIS data across various Levels of Detail (LoDs). 2. Mobile LiDAR Survey Technologies • Mobile LiDAR is a powerful geospatial technology for surveying complex urban landscapes including roads and roadside infrastructure. It provides accurate 3D data by integrating laser scanning, Inertial Measurement Unit (IMU), and Global Navigation Satellite System (GNSS) technologies to produce detailed point clouds. • A schematic truck-mounted Mobile LiDAR system is shown in Figure 1. Figure 1: Mobile LiDAR system (adapted from [1] • Mobile LiDAR Surveys (MLS) capture X, Y, Z coordinates of laser light reflecting objects along with attributes like intensity and RGB colour values. They also capture imagery that can be fused with the point cloud data to improve object classification. • To identify and semantically classify objects such as roadside features requires the use of Artificial Intelligence (AI) software which incorporates complex algorithms which may be tailored to particular types of object. Thus detecting the road surface may require a different algorithm from pole-like objects such as street signs. • This study is based on survey data captured by Cyvl LiDAR and 360-degree optical imagery sensors mounted on vehicles which travel on targeted roads at the same speed as other traffic. The Cyvl system also contains a suite of Artificial Intelligence (AI) software which is mainly used for road inventory recording, defect detection and road safety management. Road defects include road pavement damage and potholes. Road safety issues include street trees encroaching on the road, damaged signage, lighting fixtures and lane markings and driving obstacles on the road. The road infrastructure data relevant to this project are indicated in the diagram below. Figure 2: Taxonomy of elements for road surface and object identification (adapted from [2]). Features relevant to this project are indicated by orange boxes. • An additional type of infrastructure feature not shown above is powerlines, both roadside and cross country. The Cyvl system is capable of identifying powerlines as well as vegetation which may impinge on them, and the project may involve development of specialized algorithms to assess the risk and advise on vegetation removal action. • The Cyvl system identifies and semantically classifies roadside features captured by its optical imagery sensors and matches these to the point cloud data collected by its LiDAR sensor. This is a more accurate method of identifying roadside features than methods which rely on LiDAR data alone, see eg [3].   3. Information Extraction System Overview • Roadside objects are segmented using the Cyvl2 AI system. Each object is stored in an ontological format, reducing data size for efficient querying. The integration process results in a semantically rich master geo-database for various roadside feature classes. • The system will allow for various types of queries, including spatial location-based, attribute-based, and aggregate queries. • This approach will be an ensemble of (i) data pre-processing (ii) data integration, schema development and 3D database development and (iii) data extraction and an integrated visualisation module. • A system architecture diagram (simplified) is depicted in Figure 3 below. Figure 3: Systems Architecture • The system architecture is entirely open-source, comprising multiple python packages including Geopandas, TensorFlow, Keras, PyTorch and NumPy, together with well-established database package PostgreSQL/PostGIS and server GeoServer. GeoServer is able to serve both geospatial data and HTML (www) files. The front end is under development, with software packages for 2D and 3D data including Leaflet and OL3-Cesium under evaluation. 4. Preliminary results: a Leaflet-based map viewer • Data provided by the Cyvl system was processed using 16 GeoTIFF tiles into a GeoServer Image Mosaic. These geotiff files were transformed from .LAS point cloud files collected by Civiltech. • Configured Web Map Service (WMS) rendering using coordinate reference system EPSG:28355 • The map added grayscale SLD for better visibility • See screenshot produced by Ryan Watson Consulting is shown below as Figure 4. Figure 4: Screenshot of 16 geotiff files as a mosaic 5. Conclusions • The developed WebGIS application effectively extracts and visualizes Mobile LiDAR survey data. This will help road authorities make more effective use of the data being collected. • Future research will focus on optimizing querying algorithms and expanding the application scope of the proposed framework. • Future applications may include complex queries such as assigning Pavement Condition Index (PCI) and road safety ratings such as the International Road Assessment Program (iRAP) star rating. 6. References 1. National Cooperative Highway Research Program (US) (2025). Chapter 9: Typical components of mobile LIDAR systems. In: Transportation Research Board of the National Academies (ed.) Guidelines for the Use of Mobile LIDAR in Transportation Applications. Available from: https://learnmobilelidar.com/guidance-document/chapter-9-background/. 2. Luo, Z., Gao, L., Xiang, H. and Li, J. (2023). Road object detection for HD map: Full-element survey, analysis and perspectives. ISPRS Journal of Photogrammetry and Remote Sensing 197 2023/03/01/ 122-144 DOI: https://doi.org/10.1016/j.isprsjprs.2023.01.009 3. Abro, G.-E. M., Zahid, F., Rajput, S., Azhar Ali, S. S. and Aromoye, I. A. (2025). Challenges and Innovations in 3D Object Recognition: The Integration of LiDAR and Camera Sensors for Autonomous Applications. Transportation Research Procedia 84 2025/01/01/ 618-624 DOI: https://doi.org/10.1016/j.trpro.2025.03.116 PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/3NRE7Q/ WG802 Richard Watson PUBLISH SNCZ98@@talks.osgeo.org -SNCZ98 Digital Earth Pacific - Enabling Earth Intelligence to Achieve Development Goals in the Pacific en en 20251121T093000 20251121T095500 0.02500 Digital Earth Pacific - Enabling Earth Intelligence to Achieve Development Goals in the Pacific Translating a successful public-good Earth Observation (EO) infrastructure across regions is not a straightforward process of replicating technology stacks and methodologies. Digital Earth Pacific (DEP) builds upon the established open-source, standards-based foundations of Digital Earth Australia and Digital Earth Africa, yet it adapts these frameworks to suit the distinctive environmental, political, and technical circumstances of the small island states in the Pacific region. This presentation provides an overview of the journey undertaken by DEP to scale Earth observation capabilities to a regional, multi-agency context within the Pacific. We will delve into the design choices, partnership models, data architecture transformations, and scaling-up strategies that are pivotal to DEP’s success. From the challenges posed by disparity in scale to the capacity-building requirements, this presentation highlights both the limitations and innovations inherent in constructing a cloud-native platform designed for equitable access, scalability, and long-term sustainability, to inform sustainable development efforts within the Pacific region. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/SNCZ98/ WG802 Sachindra Singh PUBLISH FYLYBK@@talks.osgeo.org -FYLYBK ROCS: Extending Romania’s National Infrastructure within the European Collaborative Ground Segment with FOSS4G Solutions en en 20251121T100000 20251121T102500 0.02500 ROCS: Extending Romania’s National Infrastructure within the European Collaborative Ground Segment with FOSS4G Solutions 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. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/FYLYBK/ WG802 Vasile Crăciunescu Marian Neagul Iuhasz Gabriel PUBLISH TARXLF@@talks.osgeo.org -TARXLF Free and Open Source AI Assisted Mapping : fAIr en en 20251121T110000 20251121T112500 0.02500 Free and Open Source AI Assisted Mapping : fAIr The service uses AI models, specifically computer vision techniques, to detect objects in satellite and UAV imagery. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/TARXLF/ WG802 Kshitij Raj Sharma Omran NAJJAR Leen D'hondt PUBLISH SWMF7K@@talks.osgeo.org -SWMF7K Introducing ChatMap: Open mapping with chat apps en en 20251121T113000 20251121T115500 0.02500 Introducing ChatMap: Open mapping with chat apps Nearly 3.5 billion people in the world already use an instant messaging app for communications on a routine basis, many of them in high vulnerability locations. We've created a solution that enables mapping with instant messaging apps. From the chat to the map! - No need to install new apps - Learn how to map in 1 min - No installations or integrations needed - Everyone can access and contribute to the map We'll explore how it works, successful use cases and the future of this new way of mapping. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/SWMF7K/ WG802 Emilio Mariscal PUBLISH QQMXKK@@talks.osgeo.org -QQMXKK OpenSearch Geospatial en en 20251121T120000 20251121T122500 0.02500 OpenSearch Geospatial OpenSearch is the open source version of what you may have otherwise heard of ('Elastic Search') if at some point if you work in DevOps or cloud infrastructure in the past decade or so. In my role as a DevOps/software engineer I have worked on integrating OpenSearch as an observability platform (analysing/monitoring logs and metrics) for cloud hosted software application and services. I was very excited to see that this software has geospatial! This presentation will outline the geospatial capabilities of OpenSearch and use cases for the platform beyond an observability platform. For more info - see https://opensearch.org/ or https://github.com/opensearch-project PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/QQMXKK/ WG802 Rebecca Ryan PUBLISH GMWCAD@@talks.osgeo.org -GMWCAD Introduction to libCartoSym, libCQL2 and libDE9IM en en 20251121T133000 20251121T135500 0.02500 Introduction to libCartoSym, libCQL2 and libDE9IM libCartoSym is a Free and Open-Source Software library implementing [OGC Cartographic Symbology 2.0](https://github.com/opengeospatial/cartographic-symbology) _libCartoSym_ aims to be an implementation of the [CartoSym-CSS](https://docs.ogc.org/DRAFTS/18-067r4.html#rc-cscss) and [CartoSym-JSON](https://docs.ogc.org/DRAFTS/18-067r4.html#rc-json) encodings defined in the candidate [OGC Cartographic Symbology - Part 1: Core Model and Encodings Standard version 2.0](https://docs.ogc.org/DRAFTS/18-067r4.html) Standard. The library allows to read and write these CartoSym encodings, as well as import from and export to additional encodings of portrayal rules such as OGC [SLD](https://portal.ogc.org/files/?artifact_id=22364)/[SE](https://portal.ogc.org/files/?artifact_id=16700) and [Mapbox GL Styles](https://docs.mapbox.com/mapbox-gl-js/guides/styles/). Since the CartoSym encodings extend the [OGC Common Query Language (CQL2)](https://www.opengis.net/doc/IS/cql2/1.0), the library also relies on a related open-source libCQL2 library providing support for parsing and writing CQL2-Text and CQL2-JSON expressions, as well as run-time evaluation of CQL2 expressions. Support for performing spatial relation queries based on the [Dimensionally Extended-9 Intersection Model](https://en.wikipedia.org/wiki/DE-9IM) is also integrated within a jointly developed _libDE9IM_ open-source library, and support for OGC Simple Features as well as parsing and writing geometries defined in [Well-Known Text (WKT)](http://portal.opengeospatial.org/files/?artifact_id=25355) and [GeoJSON](https://tools.ietf.org/rfc/rfc7946.txt) is also provided by related open-source libraries. While these libraries are written in the [eC programming language](https://ec-lang.org), object-oriented bindings for _libCartoSym_ automatically generated using Ecere's [binding generating tool (bgen)](https://github.com/ecere/bgen) will also be made available for the C, C++ and Python programming languages, with additional support planned for Java and Rust. _Acknowledgement_ Financial support provided by GeoConnections, a national collaborative initiative led by Natural Resources Canada. GeoConnections supports the modernization of the Canadian Geospatial Data Infrastructure (CGDI). The CGDI is the collection of geospatial data, standards, policies, applications, and governance that facilitate its access, use, integration, and preservation. PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/GMWCAD/ WG802 Jerome St-Louis PUBLISH NRGXSA@@talks.osgeo.org -NRGXSA Forecasting Flash Floods in Seoul Using Tiny Radars without FOSS4G in the Operational Phase en en 20251121T140000 20251121T142500 0.02500 Forecasting Flash Floods in Seoul Using Tiny Radars without FOSS4G in the Operational Phase . PUBLIC CONFIRMED Talk https://talks.osgeo.org/foss4g-2025/talk/NRGXSA/ WG802 YJ Won PUBLISH DXRKGK@@talks.osgeo.org -DXRKGK Virtually flooded: representing flood model predictions in virtual reality for improved public engagement and understanding of risk en en 20251121T143000 20251121T145500 0.02500 Virtually flooded: representing flood model predictions in virtual reality for improved public engagement and understanding of risk Flood inundation is a frequent, widespread, and impactful hazard, with flood risk expected to increase in future because of climate change through increased storminess coupled with rapid growth of urban areas. To manage flood risk efficiently and effectively, communication of risk assessments for multiple scenarios needs to be targeted with the right method for the right audience. However, there is limited information on what works best, why, and for whom. Flood risks may be poorly understood by the public, with flood events taking communities by surprise even if risk assessments have been completed. Further, traditional two-dimensional risk maps are limited by interpretability challenges, especially given the inherent complexity of the data shown (e.g., depth of flooding at a given annual exceedance probability), and differences cartographic decisions taken (e.g., spatial scale and what additional features to show). Three-dimensional visualisations and immersive Virtual Reality (VR) technology have been found to be more intuitive due to their increased realism, with several studies finding that is a useful tool for improved community preparedness. Our research is developing advanced automated visualizations of flood risk scenarios using VR, with the aim to improve awareness of flood risk. Our work is building on our open-source flood risk assessment system, Flood Resilience Digital Twin (FReDT), which brings together computational models of flood inundation with other data for hazard assessment, management, and mitigation. A key objective of FReDT is to enable the automation of flood risk assessments, so that multiple scenarios can be assessed rapidly. Here, our focus is on the development of open-source software components which enable dynamic flood model predictions of water levels during a flood event to be ingested into virtual representations of modelled areas in Unreal Engine software from Epic Games. Thus, predictions for different scenarios (from any hydraulic model) are visualised in an immersive way in VR or with 360-degree video representations. We have created a processing pipeline that takes a modelled flood scenario from FReDT (currently, produced using the BG-Flood hydraulic engine) and adds a representation of that flooding into an Unreal Engine virtual environment (level) of the same area. The level landscape must be produced from the same LiDAR terrain dataset as used to generate the flood model, so that the Unreal Engine representation aligns vertically with the flood model. Further development will include workflows to produce an Unreal Engine level purely from environmental data but currently we require this to be created independently. Our pipeline takes the flood scenario depth data, consisting of a time-series of geospatial raster layers in NetCDF format, and creates water “sources” within Unreal Engine that match the depth over time of the raster. These water sources use Fluid Flux 3.0 for realistic fluid simulations at runtime and is required to represent the dynamics of the flood event, including the interaction of flow with objects in the VR environment. Fluid Flux is a proprietary Unreal Engine plugin from Imaginary Blend that solves the full shallow water equations in real-time at a high spatial resolution. Water source placement currently decided by developers choosing points within the flood scenario extent and saving these to a geospatial vector file, although in a future version this process will also be automated. Our processing pipeline begins by using Python with open-source libraries such as XArray and GeoPandas to extract the depths over time for each of the given points and collate these data into a CSV file containing each point. This CSV file allows the second processing stage to occur within Unreal Editor, without requiring libraries to be installed into the Unreal Editor Python environment. In Unreal Editor, a second Python script is called, which reads the CSV and creates water source actors at each given location. Unreal Float Curves are used to represent the depth of the water source over time. These water sources use Fluid Flux 3.0 simulation modifiers with a custom Blueprint plugin implementation to provide time-series depth modification. These water sources are set to add water to the domain while the immediate surrounding area does not match their water level and remove water where the surrounding area exceeds it. The more sources of water added the more closely the Unreal Engine simulation aligns with the outputs of the flood model scenario. Using Fluid Flux 3.0, simulation states can be pre-run to allow switching between stages of flooding that could be hours apart in real time. This allows us to demonstrate multiple different stages of flooding, from a normal day through to peak inundation. Depending on how the simulations are setup, the topography of the VR domain can be substantially more detailed than the source flood model, and domain is likely to be a smaller spatial subset. The higher resolution allows the representation of flows around and between features such as buildings, although it should be noted that currently only the mass of water is included from source flood model simulations, not momentum, meaning that any highly dynamic flow (e.g., hydraulic jumps and super critical flow) is generated internally within Unreal Engine by Fluid Flux 3.0. Usual practice is to run flood model scenarios at around 5-10 m spatial resolution for river reaches of several kilometres, while the VR domain can be around 1 m spatial resolution for domains of approx. 2 x 2 km, depending on available video memory. Our current hardware comprises a NVidia RTX 4090 GPU with 24 Gb memory, of which around 8 Gb is used during simulations. A key outcome of our research is the methods and software which build on FReDT to create advanced flood visualizations, using virtual reality (VR) technology. We are currently engaging with communities regarding these visualisations to assess their effectiveness in communicating flood risk, including multiple scenarios of different levels of flood likelihood. The software developed enables users to switch between scenarios from within the VR system, with appropriate flood levels pulled into the system in real time. Ultimately, our aim is to enable users to interact with the environment, for example to make changes which are aimed at flood mitigation (e.g., natural flood solutions such as wetland restoration). These mitigation scenarios will be assessed using FReDT, enabling dynamic updating of the visualisations through automated ingestion of model outputs. More broadly, our work demonstrates how the outputs of advanced numerical models can be used directly within VR to create intuitive visualisations, with widespread potential applications such as in the communication of the potential impacts of climate change. GitHub repository: https://github.com/GeospatialResearch/UnrealFloodingScripts PUBLIC CONFIRMED Academic Paper https://talks.osgeo.org/foss4g-2025/talk/DXRKGK/ WG802 Matthew Wilson Luke Parkinson PUBLISH A8VM7H@@talks.osgeo.org -A8VM7H QGIS Community Day Event en en 20251122T090000 20251122T130000 4.00000 QGIS Community Day Event QGIS Events organized by QGIS Australia PUBLIC CONFIRMED Community Day Event https://talks.osgeo.org/foss4g-2025/talk/A8VM7H/ WG308 TE IRINGA Em Hain Simon Nitz PUBLISH A8VM7H@@talks.osgeo.org -A8VM7H QGIS Community Day Event en en 20251122T130000 20251122T170000 4.00000 QGIS Community Day Event QGIS Events organized by QGIS Australia PUBLIC CONFIRMED Community Day Event https://talks.osgeo.org/foss4g-2025/talk/A8VM7H/ WG308 TE IRINGA Em Hain Simon Nitz PUBLISH A8VM7H@@talks.osgeo.org -A8VM7H QGIS Community Day Event en en 20251123T090000 20251123T130000 4.00000 QGIS Community Day Event QGIS Events organized by QGIS Australia PUBLIC CONFIRMED Community Day Event https://talks.osgeo.org/foss4g-2025/talk/A8VM7H/ WG308 TE IRINGA Em Hain Simon Nitz PUBLISH A8VM7H@@talks.osgeo.org -A8VM7H QGIS Community Day Event en en 20251123T130000 20251123T170000 4.00000 QGIS Community Day Event QGIS Events organized by QGIS Australia PUBLIC CONFIRMED Community Day Event https://talks.osgeo.org/foss4g-2025/talk/A8VM7H/ WG308 TE IRINGA Em Hain Simon Nitz