DeltaDTM: A global coastal digital terrain model

doi: 10.4121/21997565.v2
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21997565
Datacite citation style:
Pronk, Maarten (2024): DeltaDTM: A global coastal digital terrain model. Version 2. 4TU.ResearchData. dataset.
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version 2 - 2024-03-06 (latest)
version 1 - 2023-10-30
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Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels due to Sea Level Rise (SLR), subsidence and changing extreme weather patterns. However, current freely available elevation data sets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 30 m and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects the CopernicusDEM with space borne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods (including machine learning) recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications.

  • 2023-10-30 first online
  • 2024-03-06 published, posted
Cloud Optimized GeoTiff (COG) as in .tif files, zipped per continent, with the mask .tif files in An overview of the tiles is given in the deltadtm_tiles.gpkg.
associated peer-reviewed publication
DeltaDTM: A global coastal digital terrain model
TU Delft, Faculty of Architecture and the Built Environment, Department of Urban Data Science


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