GEDTM30 is a global 1-arc-second (~30m) Digital Terrain Model (DTM) built using a machine-learning-based data fusion approach. This dataset was generated using a global-to-local random forest model trained on ICEsat-2 and GEDI data, leveraging almost 30 billion of the highest-quality elevation points.
GEDTM30 is also used to generate 15 land surface parameters at six scales (30, 60, 120, 240, 480 and 960m), covering aspects of topographic position, light and shadow, landform characteristics, and hydrology. A publication describing methods used has been submitted to PeerJ and is in review. The repository demonstrates the modeling and parametrization.
- GEDTM30: Terrain Height Prediction
Represents the predicted terrain height.
- Uncertainty Map of Terrain Prediction
Provides an uncertainty map of the terrain prediction, derived from the standard deviation of individual tree predictions in the Random Forest model.
- 15 land surface parameters
Produced by DTM parametrization, representing different terrain features. Metadata of each parameter is currently stored at scale.csv. The optimized Equi7 tiling system for parameterization is currently stored at equi7_tiles.
- Landform: Slope in Degree, Geomorphons
- Light and Shadow: Positive Openness, Negative Openness, Hillshade
- Curvature: Minimal Curvature, Maximal Curvature, Profile Curvature, Tangential Curvature, Ring Curvature, Shape Index
- Local Topographic Position: Difference from Mean Elevation, Spherical Standard Deviation of the Normals
- Hydrology: Specific Catchment Area, LS Factor, Topographic Wetness Index
| Layer | Scale | Data Type | No Data |
|---|---|---|---|
| Ensemble Digital Terrain Model | 10 | Int32 | -2,147,483,647 |
| Standard Deviation EDTM | 100 | UInt16 | 65,535 |
| Difference from Mean Elevation | 100 | Int16 | 32,767 |
| Geomorphons | 1 | Byte | 255 |
| Hillshade | 1 | UInt16 | 65,535 |
| LS Factor | 1,000 | UInt16 | 65,535 |
| Maximal Curvature | 1,000 | Int16 | 32,767 |
| Minimal Curvature | 1,000 | Int16 | 32,767 |
| Negative Openness | 100 | UInt16 | 65,535 |
| Positive Openness | 100 | UInt16 | 65,535 |
| Profile Curvature | 1,000 | Int16 | 32,767 |
| Ring Curvature | 10,000 | Int16 | 32,767 |
| Shape Index | 1,000 | Int16 | 32,767 |
| Slope in Degree | 100 | UInt16 | 65,535 |
| Specific Catchment Area | 1,000 | UInt16 | 65,535 |
| Spherical Standard Deviation of the Normals | 100 | Int16 | 32,767 |
| Tangential Curvature | 1,000 | Int16 | 32,767 |
| Topographic Wetness Index | 100 | Int16 | 32,767 |
This dataset is designed for researchers, developers, and professionals working in earth sciences, GIS, and remote sensing. It can be integrated into various geospatial analysis workflows to enhance terrain representation and modeling accuracy. This dataset covers the entire world and is well-suited for applications in:
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Topography
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Hydrology
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Geomorphometry
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Others
- Access and test the model and parametrization, please clone this repository:
git clone https://github.com/openlandmap/GEDTM30.git
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COG urls of GEDTM30 and Land surface parameters can be found in this csv here: metadata/cog_list.csv
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Download the data set from Zenodo (10.5281/zenodo.15689805)
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Access and visualize COGs in QGIS.
Please follow the step in the GIF below.
Instruction:
right click --> copy link --> paste to QGIS Layer >> Add Layer >> Add Raster Layer >> select Protocol:HTTP(S), cloud, etc. >> paste the url and you can visualize in QGIS.
If you use this dataset in your research or application, please cite as:
@dataset{ho_2025_14900181,
author = {Ho, Yufeng and Hengl, Tomislav},
title = {Global Ensemble Digital Terrain Model 30m (GEDTM30)},
month = feb,
year = 2025,
publisher = {Zenodo},
version = {v20250130},
doi = {10.5281/zenodo.14900181},
url = {https://doi.org/10.5281/zenodo.14900181},
}
Technical documentation can be cited as:
@Article{yufengho2025GEDTM30,
AUTHOR = {Ho, Yu-Feng and Grohmann, Carlos H and Lindsay, John and Reuter, Hannes I and Parente, Leandro and Witjes, Martijn and Hengl, Tomislav},
TITLE = {{Global Ensemble Digital Terrain modeling and parametrization at 30 m resolution (GEDTM30): a data fusion approach based on ICESat-2, GEDI and multisource data}},
JOURNAL = {PeerJ},
VOLUME = {in review},
YEAR = {2025?},
PAGES = {1--49},
DOI = {10.21203/rs.3.rs-6280607/v1}
}
This dataset is released under fully open license CC-BY 4.0.
For any questions or contributions, feel free to open an issue or reach out via [[email protected]].
