It was developed using Sentinel-2 imagery with a 10 m resolution and deep learning methods.
The forest class is coded as 2 and defined as dense vegetation clustering of trees taller than 15 m, typically with a closed or dense canopy. This class includes various types of vegetation, such as wooded vegetation and clusters of dense tall vegetation within savannas, plantations, swamps, and mangroves.
The model utilized over 5 billion Sentinel-2 pixels, manually annotated from more than 20,000 sampling points distributed worldwide. By processing images captured on multiple dates throughout the year, the model generated a representative map. The overall accuracy is reported to be 85%.