DeepMind and WRI's ForTy forest map scores 90.2%, but misses many planted forests
Google DeepMind and WRI's ForTy forest map scores 90.2% overall, but finds only about half of planted forests and tree crops, the types regulators care about.
Google DeepMind and Google Research, with the World Resources Institute (WRI) and IIASA, released Forest Typology (ForTy) 2020, a global forest map with a 10-meter output grid that sorts land into six types: primary forest, naturally regenerating forest, planted forest, plantation forest, tree crops and agroforestry, and other land. An early annual time series covers 2017 to 2025. The paper appeared in Scientific Data on October 21.
The headline number is 90.2% overall accuracy across 8,190 expert-labeled sites. The authors warn that the score gets a large boost from the easy "other land" class. For planted forest, the paper reports an F1 score of 58.2%, according to Model Current. The Decoder reports that the map found 89% of confirmed primary forests but only about half of tree crops and planted forests, and 60% of plantations. The map was wrong for a third of areas labeled planted forest.
For context, The Decoder reports that, on the same test data, ForTy beat European Commission maps made for the EU deforestation regulation, 90.9% to 87.8% on forest types. It also overestimates primary forest in Asia by about 200 million hectares and misses about 94 million hectares of forest in Africa.
My take: a 90% that leans on the easiest class is a flattering number. The categories a deforestation rule cares about, plantations and crops, are exactly where the map is weakest. The Earth Engine catalog says the class scores have not been calibrated after training, so "probability" is doing some generous work.
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Sources
- Beyond tree cover: mapping the world’s forest types, Google DeepMind
- Google Deepmind creates a global map of forests with satellite data and AI, The Decoder
- ForTy maps forest types beyond tree cover, with uneven accuracy, modelcurrent.com
- GitHub - google-deepmind/forest_typology: Datasets to protect Earth's forests and biodiversity, GitHub
- Global forest typology at 10-meter resolution for forest and land-use monitoring, Scientific Data
Meanwhile at the anchor desk
A map of 4.1 billion hectares of forest, presented as a baseline for the EU's deforestation rules, and it beats the European Commission's own maps on the same test data. The people in that room must be thrilled.
The repo is on GitHub: software under Apache 2.0, everything else CC-BY. Class scores come uncalibrated, so tune your thresholds before you trust them.
