From counting losses to predicting risk

Nature underpins our climate, our economies, and our very lives. And within nature, forests stand as one of the most powerful pillars — storing carbon, regulating rainfall, mitigating floods, and harboring the majority of the planet’s terrestrial biodiversity.

Yet, despite their critical importance, the world continues to lose forests at an alarming rate. Last year alone, we lost the equivalent of 18 soccer fields of tropical forest every minute, totaling 6.7 million hectares — a record high and double the amount lost the year before. Today, habitat conversion is the greatest threat to biodiversity on land.

For years, satellite data has been our essential tool for measuring this loss. More recently, in collaboration with the World Resources Institute, we helped map the underlying drivers of that loss — from agriculture and logging to mining and fire — for the years 2000–2024. These maps, which are at an unprecedented 1km² resolution, provide a basis for a wide range of forest protection measures. However those insights, critical as they are, only look backward. Now, it’s time to look ahead.

We’re excited to announce the release of “ForestCast: Forecasting Deforestation Risk at Scale with Deep Learning”, along with the first publicly available benchmark dataset dedicated to training deep learning models to predict deforestation risk. This shift from merely monitoring what’s already gone to forecasting what’s at risk in the future changes the game. Previous approaches to risk have depended on assembling patchily-available input maps, such as roads and population density, which can quickly go out of date. By contrast, we have developed an efficient approach based on pure satellite data that can be applied consistently, in any region, and can be readily updated in the future when more data becomes available. We found that this approach could match or exceed the accuracy of previous approaches. To ensure the community can reproduce and build on our work, we are releasing all of the input, training, and evaluation data as a public benchmark dataset.