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Coffee Talk - FathomDEM, a new global 30 m DTM

FathomDEM, a new global 30 m DTM

Dr. Chris Lucas
Fathom company

January 20th, 2026
14:00 (UTC)

Recording available in our YouTube channel

Bio: Chris Lucas is the Principal Machine Learning Engineer at Fathom, a global water intelligence company. Holding a PhD in High Energy Physics, his 11 years in industry span from software engineering to ML research, contributing to projects such as scaling medical diagnosis models and developing new computer vision techniques for vehicle analysis. At Fathom, he leverages his combined ML, software, and scientific expertise to advance multiple levels of the company’s modeling stack, from terrain modeling (DEMs) to generative AI for climate downscaling and hydrological modeling with graph neural networks.

Abstract: Accurate digital elevation models (DEMs) are foundational inputs for a vast array of geomorphometry applications, including natural hazard modeling, glaciology, and infrastructure planning. However, existing global DEMs, such as Copernicus DEM (COPDEM), often contain surface features like trees and buildings, limiting their effectiveness as Digital Terrain Models (DTMs). This talk introduces FathomDEM, a new global 30 m DTM created using a novel machine-learning methodology. We utilized a hybrid vision transformer model within a U- Net architecture to perform pixel-wise regression, analyzing and correcting the height biases in COPDEM. This approach differs significantly from previous methods (like the pixel-by-pixel correction used for FABDEM) by leveraging 2D spatial information (context) as an inductive basis, essentially employing ‘computer vision’ to achieve more spatially coherent and robust corrections. FathomDEM was trained on extensive, diverse LiDAR reference data and has been rigorously validated, demonstrating: Improved Accuracy, surpassing the accuracy of existing best-ranked global DEMs; Excellent Performance in Specific Landscapes, showing reduced error even when compared to specialized coastal DEMs (e.g., DeltaDTM); High Utility in Downstream Tasks, when utilised in flood inundation modeling, FathomDEM achieves increased accuracy, approaching the performance levels of models derived from high-resolution LiDAR data. Join this session for an informal discussion on the methodology behind FathomDEM, its novel use of ML for artifact removal, and its potential to improve applied geomorphometry tasks globally.

Coffee Talk - Building Global Ensemble Terrain Model and Derivatives in 30m (GEDTM30): Towards a Open Science Community

Building Global Ensemble Terrain Model and Derivatives in 30m (GEDTM30): Towards an Open Science Community

Yu-Feng Ho
OpenGeoHub, NL

May 7th , 2025
7:00 MDT (UTC -6), 9:00 EDT (UTC -4), 10:00 BRT (UTC -3), 13:00 GMT (UTC +0), 15:00 CEST (UTC+2), 15:00 EET (UTC +2), 21:00 CST (UTC +8)

Recording available in our YouTube channel

Bio: Yu-Feng Ho is a Research Assistant / Geoinformatician in OpenGeoHub. He is the one of the main producers of GEDTM dataset and has experiences in global space-borne lidar (ICESat-2 and GEDI) and operating global topography datasets. He specializes in geocomputing with Remote Sensing data, optimize and automate modeling frameworks.

Abstract: Terrain models and derivatives are used in multidisciplinary subjects. What is the current software and methodologies that we can use to derive and maintain an open global terrain dataset? How can open data easily interact with the International Society for Geomorphometry (ISG)? OpenGeoHub creates a fully opened global digital terrain model in 30m (GEDTM30) and its multiscale derivatives in 30, 60, 120, 240, 480 and 960m. In this presentation, we will dive in the methodology of “global-to-local” modeling of deriving GEDTM30 by fusing ALOS AW3D30, CoperincusDEM and global satellite lidar (ICESat-2 and GEDI), and also the implementation of Whitebox Workflow to derive multiscale DTM derivatives. At the end, we provide several solutions to access and interact with this open global terrain dataset through Jupyter notebook, QGIS, and GitHub.

Coffee Talk - Quantitative interrogation of DEMs using TopoToolbox

Quantitative interrogation of DEMs using TopoToolbox

Wolfgang Schwanghart
University of Potsdam Germany

April 2nd , 2025
7:00 MDT (UTC -7), 9:00 EDT (UTC -5), 11:00 BRT (UTC -3), 14:00 GMT (UTC +0), 15:00 CEST (UTC+1), 16:00 EET (UTC +2), 22:00 CST (UTC +8)

Recording available in our YouTube channel

Bio: Dr. Wolfgang Schwanghart is a physical geographer and geomorphologist at the University of Potsdam, Germany. He has more than 15 years experience in working with DEMs and has written the software TopoToolbox, a MATLAB software for terrain analysis. His research focuses on landscape evolution on geological timescales and natural hazards, particularly in high mountain areas. In this context, he applies digital terrain analysis to extract as much quantitative and qualitative information as possible from DEMs, enhancing the understanding of tectonic and climatic influences on landscape evolution and improving predictions of natural hazards and risks.

Abstract: In this talk, I will present current developments within the project TopoToolbox 3 – improving the quality and reuse of a research software for terrain analysis. Adopting FAIR principles of research software development, the project will make the MATLAB-based TopoToolbox available in other high-level programming languages. I will also talk about the software design which aims at providing a platform that fosters creativity, development, and proto-typing. Lastly, I will show some applications.

Coffee Talk - Insights into the production process of global radar DEMs

Insights into the production process of global radar DEMs

Ernest Fahrland
Airbus, Germany

December 4th, 2024
7:00 MDT (UTC -7), 9:00 EST (UTC -5), 11:00 BRT (UTC -3), 14:00 GMT (UTC +0), 15:00 CET (UTC +1), 16:00 EET (UTC +2), 22:00 CST (UTC +8)

Recording available in our YouTube channel

Bio: Ernest Fahrland is a 3D Data Development Manager working within the Radar Programs unit of Airbus. He is a studied Cartographer and has more than 15 years of experience in the field of global Digital Elevation Models. He was involved in the process design and development of production tools for the WorldDEMTM, a consistent, highly accurate and pole-to-pole DEM, that was released in 2015. On-going acquisitions of global raw DEM data in combination with the user demand for quick delivery of error-free DEMs invoked the development of a fully automated, high-performant and consistent DEM production process, hence leading to the WorldDEM Neo product. Ernest was responsible for its development and contributed his concept ideas, processing strategies and algorithmic tools. His passion for global Digital Elevation Models and his continuous strive for improvements have paved the ground for many current DEMs such as the Copernicus DEM and its derivatives.

Abstract: In his talk, Ernest Fahrland will provide an insight into a fully-automated editing & production process of global DEM data primarily from interferometric radar DEM acquisitions. The presentation comprises a short look into the history with its manual & semi-automated DEM editing. He will also address on-going challenges with interferometry-based elevation data and provide an outlook on error compensation strategies (e.g. height reconstruction from radar amplitude data based on machine-learning techniques).

GRASS GIS 8.4.0RC1 release

The GRASS GIS 8.4.0RC1 release provides more than 515 improvements and fixes with respect to the release 8.3.2.

Check the full announcement at https://github.com/OSGeo/grass/releases/tag/8.4.0RC1.

Please support in testing this release candidate.

Latest Posts

Whitebox Workflows Next Gen released

Whitebox Workflows Next Gen, a complete rewrite of the Whitebox geospatial analysis platform, is now publicly available for Python, R, and QGIS.

Built from the ground up in pure Rust, Whitebox Next Gen provides more than 700 geospatial analysis tools for geomorphometry, terrain analysis, spatial hydrology, LiDAR processing, remote sensing, vector GIS, and spatial statistics. All three interfaces run on the same high-performance backend, allowing users to work in scripts, notebooks, statistical workflows, or a familiar desktop GIS environment.

Whitebox Next Gen is not an incremental update to the previous Whitebox architecture. It replaces the earlier monolithic system with a modular, full-stack geospatial platform. Core capabilities for raster and vector I/O, coordinate systems and reprojection, vector topology, spatial indexing, LiDAR processing, and other foundational operations are implemented directly within the Whitebox codebase rather than delegated to external C or C++ GIS libraries. This approach provides consistent cross-platform behaviour, fewer system-level dependencies, tighter control over performance and correctness, and greater flexibility for continued research and development

Highlights

  • More than 700 tools for geomorphometry, hydrology, LiDAR, remote sensing, vector analysis, spatial statistics, and general geospatial processing
  • Publicly available interfaces for Python, R, and QGIS
  • A modular, high-performance backend written entirely in Rust, including wbprojection, wbraster, wbvector, wblidar, wbspatialstats, and wbtopology open-source (MIT/Apache licensed) backend libraries
  • Most of the 80+ tools that existed within the previous extension product have been migrated to the new Whitebox open core, including the tools for advanced surface curvature analysis and DEM processing
  • Native coordinate-reference-system handling and reprojection workflows via wbprojection
  • Expanded raster support (19 formats), including GeoTIFF, Cloud-Optimized GeoTIFF (COGs), GeoPackage Raster, and JPEG2000
  • Expanded vector support (12 formats), including Shapefile, GeoPackage, FlatGeobuf, GeoParquet, GeoJSON, TopoJSON, and GML
  • Modern point-cloud support, including LAS, LAZ, COPC, E57, and PLY
  • A dedicated topology engine supporting robust vector analysis, network analysis, route-event workflows, and linear referencing
  • Local-first processing on Windows, macOS, and Linux
  • A consistent analysis platform across Python scripts and R workflows
  • A newly updated and more advanced QGIS Processing toolbox

Whitebox has particular strengths in terrain analysis, geomorphometry, spatial hydrology, and LiDAR processing. Whitebox Next Gen carries these areas forward while substantially expanding the project’s capabilities for vector analysis, remote sensing, modern spatial data formats, and reproducible geospatial workflows.

Get started

The geomorphometry community is invited to explore Whitebox Next Gen, test its tools and workflows, report issues, and contribute to the continued development of the project.

DEMIX reveals which DEMs perform best

First round of the DEMIX exercise published final report

Global digital elevation models (DEMs) have become routine operational inputs across mapping, environmental monitoring, modelling and Earth-observation workflows. Their broad coverage and global availability have made them data that many practitioners simply take for granted. But knowledge of the terrain and topography of Earth’s surface is fundamental for monitoring and understanding terrestrial ecosystems and the planet’s habitability. For that reason, the CEOS Working Group on Calibration and Validation (WGCV) maintains a subgroup dedicated to digital topography and the quality of DEMs, the ‘Terrain Mapping Sub-Group (TMSG).

DEMs are representations of elevation in the form of a georectified grid, at global scale commonly derived from space-based interferometric synthetic aperture radar (InSAR) or stereoscopic optical observations, while regionally and locally airborne Laser induced detection and ranging (LIDAR) has become the primary source. DEMs can be classified as digital surface models (DSMs) when depicting the lower surface of the atmosphere or digital terrain models (DTMs) when depicting the top of the lithosphere (the Earth’s crust).

A wide variety of DEM products exist – each having different characteristics which suit different applications. It can be challenging to understand which of the available DEM products are fit for purpose for certain applications or regions. That is the practical gap addressed by the Digital Elevation Model Intercomparison Exercise (DEMIX) undertaken by TMSG. By exposing where widely used global DEMs agree, where they differ and how those differences affect rankings, the exercise delivered evidence to users for selecting a DEM rather than defaulting to one by habit.

DEM products were compared through a ‘wine contest,’ which identified requirements of openness, reproducibility, adaptability, and statistical rigour. The characteristics, evaluations, and ranking for each DEM were collected in a GIS database with over 50,000 entries. A quantitative assessment was carried out for each global DEM based on pixel-by-pixel differences of geomorphometric parameters against finer spatial resolution (1-5 m resolution) reference DEMs.

The study incorporates an unprecedented amount of high quality reference data covering a broad range of landforms and surface types, comparing their characteristics without resampling or interpolation. Users are invited to consult the results of DEMIX in order to make informed choices when needing to use a DEM, or in some cases a combination of the most relevant DEMs for their applications.

The final rankings of the wine contest, presented below, compared the six DEMs based on land cover (forest, urban, or barren), slope (cliff, steep, gentle, or flat), differences in elevation, slope and roughness, and statistical metrics.

Led by the European Commission’s Joint Research Centre (JRC), the CEOS Working Group on Calibration and Validation (WGCV) Terrain Mapping Subgroup (TMSG) established DEMIX in partnership with the International Society for Geomorphometry. Several members of the International Society of the Geomorphometry contributed significantly and we hosted various secssions at our latest conference. The exercise was performed over a three-year period starting in 2020, with community-wide calls for participation and an assembly of experts producing a number of peer-reviewed publications, culminating with a JRC reference report published in July 2026.

The objective of DEMIX was to propose a procedure to rank the available free and open global DEMs, taking into consideration user needs while promoting the implementation of FAIR (Findable, Accessible, Interoperable and Reusable) principles. The exercise compared DEMs based on criteria for land cover and terrain slope categories, representative testing, and a statistically sound ranking approach. The report outputs tailored recommendations regarding available DEM products that are not limited to one domain, geographic area, or landscape type, with flexibility for different user needs and applications.

A major challenge in the comparison of the global DEMs were the varying formats, data, and metadata contents. The adoption of a common set of standards would enhance the quality and interoperability of global DEMs and streamline the exchange and utilisation of DEM data for both providers and users. DEMIX makes the following recommendations for data providers:

● Adopt a standardised grid layout, with complete grid definitions and encodings following ISO/OGC rules.

● Include vertical datum information as part of raster DEM files.

● Clearly indicate the product version in the file names.

● Participate in future DEMIX rounds, which serve as a neutral platform for independently evaluating products before their release.

● Provide high quality, finer resolution, accurate, and multi-temporal elevation data to be used as a reference alongside DEM products.

Global DEMs are indispensable operational geospatial datasets, but they should not be treated as error-free or interchangeable. DEMIX delivered to the global community a systematic and transparent method to help choose elevation products more intelligently and help define what better elevation measurements should look like in the future.

Further Reading:

Read more in the DEMIX Final Report, or see other DEMIX publications below - mainly by our ISG members:

  • Benchmarking Elevation Plus Land Surface Parameters Finds FathomDEM and Copernicus DEM Win as Best Global DEMs (2025) and subsequent discussion (2026a, 2026b)
  • Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation 2025
  • Novel Approach for Ranking DEMs: Copernicus DEM Improves One Arc Second Open Global Topography (2024)
  • Digital Elevation Models: Terminology and Definitions (2021)
  • The Digital Elevation Model Intercomparison eXperiment DEMIX, a community-based approach at global DEM benchmarking (2021)

Coffee Talk - GeoNadir

Building the world’s largest repository of FAIR drone mapping data

Paul Mead GeoNadir

September 2nd, 2026
11:30 (UTC)

Bio: Paul Mead is Co-founder and Head of Business & Strategy at GeoNadir, a platform building the world’s largest repository of FAIR drone mapping data to support environmental decision-making. A non-technical co-founder, Paul focuses on strategy, commercial development, and building high-performing teams — a discipline shaped by 11 years in the New Zealand Army, including leading a bomb disposal team in Lebanon and service in Afghanistan and the Asia-Pacific region. Since 2013 he has built and led several small businesses and start-ups, with drones as a central technology since 2016. Alongside GeoNadir, Paul co-founded She Maps, an internationally recognised drone and geospatial STEM education organisation, and has helped raise over $7M in equity and non-equity funding across his ventures. He holds a Master of Philosophy from the University of Queensland and is a Chartered Manager.

Abstract: GeoNadir set out to build the world’s largest repository of FAIR drone mapping data, and ended up building something more powerful. Drones have democratised data capture, but the same democratised ability to generate decision-grade insights from the data is lacking. I’ll walk through how we’re closing that gap: the data infrastructure behind hosting large volumes of UAV imagery and derived products, and how we’re moving from manual analysis toward automated, repeatable geospatial analytics.