OpenGeoHub Global Elevation DTM 30

Dataset details:

With global coverage and a harmonized data format, the dataset supports environmental monitoring and planning across local, regional, and continental scales. The GEDTM30 dataset is created using a machine learning approach that combines several global elevation sources, including ALOS AW3D, GLO-30, MERIT DEM, and high-quality national DTMs. Vegetation and building effects are removed using indicators of canopy height (greater than 2 meters) and surface roughness (greater than 6 meters). Ground elevation is estimated from nearly 30 billion LiDAR measurements collected by the GEDI and ICESat-2 satellite missions, which form the training data for a global random forest model with local refinements. The dataset uses the Equi7 tiling system to preserve hydrological continuity and reduce border effects. It also includes additional terrain variables such as slope, curvature, and hydrological indices derived from the final elevation model.