DOI: 10.5281/zenodo.15689805
title: Global Ensemble Digital Terrain Model 30m (GEDTM30)

description: Disclaimer

This is the first release of the Global Ensemble Digital Terrain Model (GEDTM30). Use for testing purposes only. A publication describing the methods used has been submitted to PeerJ and is currently under review. This work was funded by the European Union. However, the views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is." The Open-Earth-Monitor project consortium, along with its suppliers and licensors, hereby disclaims all warranties of any kind, express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and non-infringement. Neither the Open-Earth-Monitor project consortium nor its suppliers and licensors make any warranty that the website will be error-free or that access to it will be continuous or uninterrupted. You understand that you download or otherwise obtain content or services from the website at your own discretion and risk.

Currently the dataset was produced by resampling to 0.00025 degree, which is closer to 0.9 arc sec. We are curretly working on produce a 0.000277778 degree GEDTM30 to resolve the confusion. See the issue detail here.*

Description

GEDTM30 is presented as a 1-arc-second (~30m) global Digital Terrain Model (DTM) generated using machine-learning-based data fusion. It was trained using a global-to-local Random Forest model with ICESat-2 and GEDI data, incorporating almost 30 billion high-quality points. To see the documentation, please visit our GEDTM30 GitHub(https://github.com/openlandmap/GEDTM30).

This dataset covers the entire world and can be used for applications such as topography, hydrology, and geomorphometry analysis.

Dataset Contents

This dataset includes:



GEDTM30Represents the predicted terrain height.

Uncertainty of GEDTM30 predictionProvides an uncertainty map of the terrain prediction, derived from the standard deviation of individual tree predictions in the Random Forest model.


Due to Zenodo's storage limitations, the original GEDTM30 dataset and its standard deviation map are provided via external links:



GEDTM30 30m

Uncertainty of GEDTM30 prediction 30m

Global-to-local mask


Related Identifiers



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


Data Details



Time period: static.

Type of data: Digital Terrain Model

How the data was collected or derived: Machine learning models.

Statistical Methods used: Random Forest.

Limitations or exclusions in the data: The dataset does not include data Antarctica.

Coordinate reference system: EPSG:4326

Bounding box (Xmin, Ymin, Xmax, Ymax): (-180, -65, 180, 85)

Spatial resolution: 120m

Image size: 360,000P x 178,219L

File format: Cloud Optimized Geotiff (COG) format.

Layer information:


 




Layer
Scale
Data Type
No Data


Ensemble Digital Terrain Model
10
Int32
-2,147,483,647


Standard Deviation EDTM
100
UInt16
65,535




Global-to-local mask - global : 0; global-to-local: 1; surface water : 2

1
Byte
255




Code Availability

The primary development of GEDTM30 is documented in GEDTM30 GitHub(https://github.com/openlandmap/GEDTM30). The current version (v1) code is compressed and uploaded as GEDTM30-main.zip. To access the up-to-date development please visit our GitHub page. 

Support

If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue here

Naming convention

To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files.

For example, for gedtm_rf_m_120m_s_20060101_20151231_go_epsg.4326.3855_v20250611.tif, the fields are:



generic variable name: edtm = ensemble digital terrain model

variable procedure combination: rf = random forest

Position in the probability distribution/variable type: m = mean | sd = standard deviation

Spatial support: 120m

Depth reference: s = surface

Time reference begin time: 20060101 = 2006-01-01

Time reference end time: 20151231 = 2015-12-31

Bounding box: go = global

EPSG code: EPSG:4326

Version code: v20250130 = version from 2025-01-30