GEM-15: Global surface and terrain elevation models (15 meters) from sub-meter optical stereo imagery
Year: 2026
Type: preprint
Abstract: Existing open global digital elevation models (DEMs) are limited to 30 m resolution, blur fine-scale urban and canopy structure, and rely on satellite missions flown between 2000 and 2015. We present Google's Global Elevation Models at 15 m (GEM-15), to our knowledge the first openly available near-global multi-layer elevation dataset at 15 m grid spacing. GEM-15 aggregates native 0.5 m optical stereo reconstructions acquired through 2024 (63.2% of mapped tiles primarily from 2022–2024 30 cm Pléiades Neo imagery) and provides four times the spatial sampling density of 30 m baselines, more current representation of the Earth's surface, and higher vertical accuracy over complex canopies, buildings, and steep relief. Alongside the primary 15 m digital surface model (DSM), GEM-15 includes within-cell elevation percentiles (P5, P50, P95), local elevation standard deviation (DSM σz), and a co-registered 15 m bare-earth digital terrain model (DTM) to capture within-cell 3D structure. We evaluate GEM-15 alongside the open 30 m DEMs Copernicus GLO-30, ALOS AW3D30, NASADEM and, for the terrain model, FABDEM against spaceborne GEDI lidar, 12 airborne and drone lidar benchmarks, and 4,071 GNSS CORS stations. Across the lidar benchmarks, the GEM-15 DSM has a 12.3% lower mean tile-debiased root-mean-square error (DRMSE) than Copernicus GLO-30 (3.37 m vs. 3.85 m), rising to 13.7% (3.69 m vs. 4.27 m) for the seven benchmarks with acquisition midpoints from 2021 onward; the margin increases with the reference survey date (Spearman ρ = 0.87 across the 10 non-rainforest campaigns) and reaches 27% for the 2024–2025 New Zealand survey. The co-registered DTM is comparable in accuracy to FABDEM in open terrain and less accurate beneath closed canopy. In repeat surveys at US NEON sites, the 30 m DEMs diverge increasingly from recent lidar, whereas GEM-15 has the lowest DRMSE in every year. The dataset is available under a CC BY 4.0 license at https://developers.google.com/earth-engine/datasets/catalog/projects_climate-and-sustainability_assets_gem-15_v1.
DOI: 10.31223/x55n75
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225771580
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