Light-weight geospatial model for global deforestation attribution

Forests are in decline worldwide and it is critical to attribute forest cover loss to its causes. We gathered a curated global dataset of all forest loss drivers and developed a neural network model to recognize the main drivers of deforestation or forest degradation at 1-km scale. Using remote sensing satellite data together with ancillary biophysical and socioeconomic data the model estimates the dominant drivers of forest loss from 2001 to 2022. Using a relatively light-weight geospatial model allowed us to to train a single world-wide model at 79.4% overall accuracy across 7 drivers categories (first of its kind product at given scale and categorization detail). We generated a global map of drivers of forest loss that is being validated, and present the first insights such data can provide.