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  1. Google DeepMind

    Google DeepMind, together with WRI and IIASA, releases ForTy 2020, a 10-meter-resolution global forest type map

    Google DeepMind and Google Research, together with the World Resources Institute (WRI) and the International Institute for Applied Systems Analysis (IIASA), released Forest Typology (ForTy) 2020, which classifies every 10×10-meter parcel of land worldwide into six forest and land-use types, serving as a key baseline for the European Union's deforestation regulation. The team trained the model on about 1.7 million labeled locations and used AlphaEarth Foundations embedding vectors as a key input; in validation at more than 9,000 randomly sampled points, the forest type classification accuracy was 90%, and the accuracy in distinguishing forest from non-forest was 95%. A companion year-by-year time series from 2017 to 2025 is also openly available and can be used free of charge under an open license on Google Earth Engine.

    A close-up of a 3D voxel-style world map focusing on the blocky terrain of South America.

    Google DeepMind and Google Research, together with the World Resources Institute (WRI) and the International Institute for Applied Systems Analysis (IIASA), released Forest Typology (ForTy) 2020, which classifies every 10×10-meter parcel of land worldwide into six forest and land-use types, serving as a key baseline for the European Union's deforestation regulation. The team trained the model on about 1.7 million labeled locations and used AlphaEarth Foundations embedding vectors as a key input;

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  2. FactRelated story

    Google DeepMind and others release the global forest typology map ForTy 2020

    Google DeepMind, together with Google Research, the World Resources Institute (WRI), and the International Institute for Applied Systems Analysis (IIASA), released the Forest Typology (ForTy) 2020 global forest type map. The map classifies every 10×10 meter parcel of land worldwide into six forest and land-use types, and serves as a key baseline for the European Union's deforestation regulation. The team trained the model on about 1.7 million labeled locations, using AlphaEarth Foundations embedding vectors as a key input; in validation at more than 9,000 randomly sampled points, the forest type classification accuracy was 90%, and the accuracy in distinguishing forest from non-forest was 95%. A companion year-by-year time series from 2017 to 2025 is also openly available and can be used free of charge under an open license on Google Earth Engine. There are currently no further developments.

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    View story:Google DeepMind and others release the global forest type map ForTy 2020

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  1. Story1 reports · 1 facts

    Google DeepMind and others release the global forest type map ForTy 2020

    Google DeepMind, together with Google Research, the World Resources Institute (WRI), and the International Institute for Applied Systems Analysis (IIASA), released the Forest Typology (ForTy) 2020 global forest type map. The map classifies every 10×10 meter parcel of land worldwide into six forest and land-use types, and serves as a key baseline for the European Union's deforestation regulation. The team trained the model on about 1.7 million labeled locations, using AlphaEarth Foundations embedding vectors as a key input; in validation at more than 9,000 randomly sampled points, the forest type classification accuracy was 90%, and the accuracy in distinguishing forest from non-forest was 95%. A companion year-by-year time series from 2017 to 2025 is also openly available and can be used free of charge under an open license on Google Earth Engine. There are currently no further developments.

    Google DeepMind, together with Google Research, the World Resources Institute (WRI), and the International Institute for Applied Systems Analysis (IIASA), released the Forest Typology (ForTy) 2020 global forest type map. The map classifies every 10×10 meter parcel of land worldwide into six forest and land-use types, and serves as a key baseline for the European Union's deforestation regulation. The team trained the model on about 1.7 million labeled locations, using AlphaEarth Foundations embed

    Matched fieldsTitle