Google DeepMind and the World Resources Institute have released a new global map of 2020 land cover that distinguishes between primary forests, timber plantations, and tree crop farms.
The system, called ForTy, assigns the world’s land area to six categories at a 10-meter resolution. These classes include primary forest, naturally regenerating forest, planted forest, plantation forest, tree crops such as oil palm, rubber, or cocoa, and other land. The authors claim this is the first global map of its kind. Previous datasets, including one from the European Commission, cover only parts of the globe.
EU deforestation rules need a reliable 2020 reference point
The main driver behind the project is the EU Regulation on Deforestation-free Products (EUDR). This rule prohibits goods linked to forests cleared or degraded after December 31, 2020. This includes goods produced after a primary forest was converted to a plantation. Oil palm and cocoa farms count as agricultural land, so companies assessing supply chain risks need to know how a site was used in 2020.
ForTy gives them that 2020 reference point. It could also feed into other initiatives that depend on similar data, including the Tropical Forest Forever Facility (TFFF) and the UN goal of protecting 30 percent of the world’s land by 2030.
Building that reference point is hard because a forest’s type depends on its history, not just how it looks, DeepMind says. An old planted mixed forest can look like a natural one from space, and a recently cleared plantation still counts as forest by definition even though satellite images show bare ground.
ForTy uses three model stages to refine its classifications
To distinguish these categories, ForTy runs on Google DeepMind’s AlphaEarth Foundations model. This model compresses data from multiple satellites into a compact description of each pixel. Several image recognition models build on those descriptions, starting with one that learns from verified examples.
The first model’s predictions, combined with outside data on tree heights, buildings, and forest loss, become expanded training data for a second model. A final step analyzes several years of data together to smooth outliers. Google’s Gemini language model also helped adapt older training data to the EU’s definitions.
During those correction rounds, experts added about 43,000 examples to address errors. These included vineyards in Europe classified as tree crops, natural pine forests in Central America labeled as planted forests, and oil palms missed in Africa and Indonesia.
Even with those corrections, some distinctions remain out of reach for satellites alone. Satellite images rarely show how land is managed, so the researchers use a 40-year rotation period to separate plantations from other planted forests. That cutoff does not fit every region.
ForTy is less reliable for plantations and tree crops
To check the map, two experts reviewed each of 8,190 randomly selected sites. They found the map correct in 90.2 percent of cases. The authors warn that this overall score gets a large boost from the easy-to-identify “other land” class.
Results vary sharply by forest type. The map correctly identified 89 percent of primary forests that experts confirmed on the ground. It found only about half of tree crops and planted forests and 60 percent of plantations. Flipping the lens, roughly a quarter to a third of areas mapped as plantations or tree crops turned out to be misclassified. The map was wrong for a third of areas labeled as planted forest.
These gaps hit the boundaries that matter most for the EUDR. Uncertainty in the evaluation may also make the results look slightly better than they are. Experts were most often unsure about managed areas, and the team excluded those uncertain cases from its assessment.
ForTy still outperforms European Commission maps made specifically for the EUDR on the same test data. It scored 95.5 percent compared with 93.6 percent for forest versus nonforest classification. For forest types, it scored 90.9 percent compared with 87.8 percent.
The model also inherited a weakness from its training data. Its annotators rarely classified forests near roads or settlements as primary forest, even when the forests appeared untouched.
Sweden has plenty of trees but little natural forest
The researchers estimate that the world has about 4.1 billion hectares of forest. This includes nearly 3.9 billion hectares of natural forest, of which about 2 billion hectares are primary forest. Planted forests cover about 250 million hectares, and tree crops cover nearly 200 million hectares, mostly in Asia.
Regional errors complicate that picture. The map overestimates primary forest in Asia by about 200 million hectares, largely in Russia’s conifer forests. In Africa, it instead misses about 94 million hectares of forest, likely because of open dry forests and savannas.
About two-thirds of Sweden’s forest area is planted, and natural forest covers only around one-fifth of the country. The authors say overall tree cover there may be adequate, but the share of natural forest, which matters for biodiversity, could be critically low. The Democratic Republic of the Congo, Indonesia, and Brazil have the highest shares of natural forest.
ForTy still struggles with shaded crops and open woodlands
Beyond these regional errors, some land uses remain difficult to classify. In some regions, the map still labels shaded cocoa or coffee grown beneath a natural canopy as forest. Selectively logged or burned forests also pose problems.
ForTy does not yet have a separate class for open woodlands, which the EUDR treats separately. The researchers say future versions should add one. Users can make their own classification choices using the dataset’s confidence scores for each class, which let them set their own thresholds.
The map, classification layer, and test data are freely available under a CC BY 4.0 license, including in Google Earth Engine. Google DeepMind has released the code in the GeeFlow and JEO repositories.
DeepMind introduced the underlying AlphaEarth Foundations model in July 2025 as a kind of virtual satellite. It combines optical satellite images, radar, laser scans, and climate simulations into a compact description of each 10-by-10-meter area. Annual embeddings covering years since 2017 are freely available in Google Earth Engine.
ForTy is one of the first large mapping projects built on those embeddings. Google says it is part of Earth AI. That connection places the project within a broader effort, as Google connected Earth AI to Gemini in fall 2025 to help detect floods, droughts, and algal blooms faster.
What it means
For businesses and regulators, ForTy offers a specific tool to verify compliance with the EUDR. However, the data requires careful handling. Users must account for the lower accuracy regarding plantations and tree crops. The exclusion of uncertain managed areas means the dataset does not cover every edge case relevant to supply chain risk.
