← Directory
data api · Global
WRI/Google DeepMind Global Drivers of Forest Loss (1km)
This dataset maps the dominant driver of tree cover loss globally from 2001 to 2025 globally. Produced by the World Resources Institute (WRI) and Google Deepmind. Using satellite imagery and a neural network to classify seven categories of drivers. Source & citation: Zenodo record 14163025; also on the WRI Data Explorer.
Claim this listing
Represent WRI/Google DeepMind Global Drivers of Forest Loss (1km)? Send a claim for review so we can discuss listing updates. Claims do not automatically grant ownership or verification.
What's in it
- Data offered docs-verified
- dominant driver of tree cover loss (classification, 7 classes) · per-class pseudo-probabilities (probability_1 to probability_7)
- Coverage docs-verified
- Global, longitude -180 to 180, latitude -56 to 84 · 2001 to 2025
- Resolution docs-verified
- 0.01° (~1 km) grid; one dominant driver per cell for the whole 2001–2025 period
- Formats docs-verified
- GeoTIFF (COG) · Icechunk virtual store (xarray) · STAC 1.1 catalog
- Access docs-verified
- Public, anonymous access via HTTPS/S3 (data.source.coop); CC-BY 4.0 licence, attribution required
Useful for
- Use cases llm-inferred
- Attributing historical tree-cover loss drivers (e.g. logging, agriculture, wildfire) for a site-scale ecological or land-use baseline · Screening upstream catchments for past forest-loss drivers as context for sediment or water-quality risk assessments · Environmental due diligence for overseas project sites where UK-style land-use records are unavailable
- Audience llm-inferred
- ecologists · GIS analysts · hydrologists · planners · environmental consultants
Limitations
- Constraints docs-verified
- Does not separate natural forest loss from the clearing of planted trees · Only one dominant driver per 1 km cell for the whole period; co-occurring drivers and their sequence are not recorded · Loss detection inherits the accuracy of Global Forest Change v1.13 · Training and validation data from the original study are not included in the mirror · Icechunk access may require downgrading zarr to 3.2.1 due to virtual_tiff incompatibility
- Licence
- see product README
- Authentication
- Public; anonymous HTTPS/S3 access via data.source.coop
- GitHub stars
- Unknown
- Last commit
- Unknown
- Last successful page check
- 14/10/2026
- Page health
- page reachable
- Package
- Not recorded
- Verification
- Not independently verified
Provenance & evidence
Verified via Source Cooperative API /api/v1/products/epoch (public product, updated 2026-09-11).
Metadata observed 14/10/2026 · research sweep: manual-2026-10-15 (source.coop)