The open map that changed earth observation
Before 2021, if you wanted a global land-cover map at better than 30-meter resolution, you needed a commercial license and a non-trivial budget. ESA WorldCover changed that.
Released in 2022 (with a 2021 annual snapshot) and updated annually, WorldCover is a free, global, 10-meter-resolution land-cover classification derived from Sentinel-1 (radar) and Sentinel-2 (optical) satellite imagery. It's the foundation that most open geospatial tools — including ours — use to answer "what's really at this pin?"
The 11 classes
- Tree cover (code 10) — forests and woodlands >10% canopy cover.
- Shrubland (20) — woody vegetation <5 m tall.
- Grassland (30) — natural or semi-natural grasses.
- Cropland (40) — cultivated or plowed land.
- Built-up (50) — urban, roads, buildings.
- Bare / sparse vegetation (60) — deserts, rock, <15% vegetation.
- Snow and ice (70) — permanent ice, glaciers.
- Permanent water bodies (80) — lakes, reservoirs, large rivers.
- Herbaceous wetland (90) — marsh, seasonally flooded grasslands.
- Mangroves (95) — coastal salt-tolerant forest.
- Moss and lichen (100) — tundra, high-altitude ground cover.
Why 10 meters matters
At 10-meter resolution, one pixel is about the size of a backyard — ~100 square meters. That lets you see:
- Individual buildings in urban areas.
- Narrow creeks and waterways.
- Field boundaries in agricultural areas.
- Thin strips of riparian forest along rivers.
At 30 m (the old standard from Landsat), these get blurred together. At 10 m, a backyard garden shows up; a roadside forest buffer shows up; a reservoir's outline is crisp.
How it's made
The WorldCover team combined Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery — both free Copernicus satellites. They ran a gradient-boosted tree classifier trained on ~2 million human-labeled pixels worldwide, then applied temporal consistency filters to stabilize the output.
The radar component is critical: Sentinel-1 sees through clouds, so tropical regions (normally cloud-covered year-round in optical imagery) get classified from radar signals sensitive to vegetation height and roughness.
Who uses it
- Environmental monitoring: WWF, Global Forest Watch, Carbon Plan.
- Insurance underwriters: flood exposure and wildfire risk assessment.
- Agtech companies: first-pass cropland extent before running per-field models.
- Urban planners: measuring city growth over annual time steps.
- Journalists: cross-referencing claims in environmental reporting.
Try it on any spot
GeoLab pulls WorldCover data for any latitude/longitude and gives you the breakdown in plain English — no GIS software needed. Free.