Predicting Near-future Deforestation Across the Tropics Using Deep Learning: Insights from the Forest Foresight Project

International audience Tropical deforestation continues to threaten biodiversity, carbon storage, and climate regulation. While satellite-based near-real-time monitoring systems track deforestation across local to global scales, they only detect forest loss after it occurs. Proactively identifying areas at risk can help support timely mitigation efforts. As part of the Forest Foresight initiative led by the World Wide Fund for Nature (Netherlands), we developed a deep learning model to predict near-future deforestation risk. The model produces monthly risk maps at a 400-meter spatial resolution with a six-month prediction horizon, based on near real-time deforestation alerts and various open-access geospatial datasets used as predictors. It was tested in 17 countries across humid tropical forests in South America, Africa, and Southeast Asia, and achieved an average F0.5 score of 64.8%. It demonstrates a modest performance gain over both a rule-based baseline model (4% global improvement) and an XGBoost decision forest model (1.4% improvement globally), as well as greater temporal and cross-country prediction stability. Prediction performance was highest in areas near recent deforestation and declined with increasing distance from these areas, highlighting the model's dependence on past deforestation patterns as the main limitation. The model is less effective in predicting new deforestation events in previously undisturbed forests, such as regions where new logging roads are being developed after the prediction date. To substantially improve prediction performance, it is essential to integrate frequently updated, region-specific, and novel data sources, particularly real-time indicators of human activity, such as mobile phone movements or economic signals.