Forests are crucial for the planet, storing carbon, regulating rainfall, and supporting biodiversity. Despite their importance, tropical forests are being lost at an alarming rate, with a record high in the past year. Habitat conversion is the primary driver of this deforestation. Previously, satellite data helped measure forest loss, and new maps identified its causes. However, this approach only looked backward at past events.A new deep learning model called ForestCast uses pure satellite data to forecast deforestation risk. This approach overcomes the limitations of older methods that relied on outdated and inconsistent geospatial data. ForestCast analyzes satellite time series and historical forest loss to predict future risks. The model's most significant input is the "change history," indicating when deforestation occurred.By using only satellite data, ForestCast offers consistency and scalability worldwide. Its deep learning vision model, based on vision transformers, captures spatial context and deforestation trends. The model's accuracy matches or exceeds previous methods that used specialized input maps. This breakthrough shifts the focus from monitoring past losses to proactively predicting future deforestation.The team is releasing ForestCast, its benchmark dataset, and all associated data to the public. This allows the machine learning community to verify, build upon, and improve deforestation risk models. The goal is to provide a tool that helps governments, companies, and communities intervene before forests are lost. By targeting resources to vulnerable areas, this forecasting tool aims to prevent deforestation, curb emissions, and protect biodiversity. Ultimately, it's about changing an unavoidable future into a protected one by empowering informed action.