Google DeepMind 用卫星数据和 AI 制作全球森林类型地图

Google Deepmind and the World Resources Institute created a global satellite map of 2020 land cover, classifying areas at 10-meter resolution into six categories, including primary forest, plantations, and tree crops. The system uses AlphaEarth Foundations and multiple image-recognition stages to distinguish forest types by growth and harvest cycles, not appearance alone. The map achieved 90.2% overall accuracy and outperformed European Commission maps on the same test data, but identified only about half of tree crops and planted forests and 60% of plantations. Google Deepmind and the World Resources Institute have developed ForTy, an AI map that distinguishes primary forests, timber plantations, and tree crop farms worldwide. Satellite maps usually show whether trees grow in a given area without identifying the forest or crop type. ForTy, short for Forest Typology, goes further by assigning the world's land area in 2020 to 6 classes at 10-meter resolution. These cover primary forest, naturally regenerating forest, planted forest, plantation forest, tree crops such as oil palm, rubber, or cocoa, and other land. The authors say ForTy is the first global map of its kind. Earlier datasets, including one from the European Commission, cover only parts of the world. The main driver behind the project is the EU Regulation on Deforestation-free Products (EUDR) , which prohibits goods linked to forests cleared or degraded after December 31, 2020, including goods produced after 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. Ad ForTy gives them that 2020 reference point and could also feed into other initiatives that depend on the same kind of data, including the Tropical Forest Forever Facility (TFFF) and the UN goal of protecting 30 percent of the world's land by 2030. Ad 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. To distinguish these categories, ForTy runs on Google DeepMind's AlphaEarth Foundations model, which 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. Ad That 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 such as 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. Ad 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, and that cutoff does not fit every region. Ad