Forests are essential to life on Earth — storing carbon, sheltering wildlife, and shaping our climate. To protect and restore them, we must see them as never before. Today, in partnership with the World Resources Institute, we’re announcing Canopy Height Maps v2 (CHMv2): an open source model and world-scale maps generated with it. Together, they will help researchers and governments measure and understand every tree, gap, and canopy edge — enabling smarter biodiversity support and land-management decisions.

At the heart of CHMv2 is DINOv3, Meta’s self-supervised vision model, which brings unprecedented clarity and detail to forest mapping worldwide. But visibility isn’t enough — having accurate, high-resolution data on forest structure is essential for turning insights into action. Tree canopy height measurements are important for monitoring forest health, tracking restoration efforts, detecting degradation, and estimating carbon storage.

Building on our original high-resolution canopy height maps released in 2024, CHMv2 delivers substantial improvements in accuracy, detail, and global consistency. This comes from replacing the DINOv2 backbone with our more capable DINOv3 backbone, pre-trained on SAT-493M, a large and diverse dataset of satellite imagery.

“DINOv3 strengthens our ability to measure forest structure across diverse landscapes, making high-resolution restoration monitoring more consistent and more scalable,” says John Brandt, Data Science Lead at the World Resources Institute.

DINOv3 learns robust visual features from large amounts of unlabeled imagery. By training on diverse satellite data, DINOv3 captures the subtle visual cues that indicate tree height, such as shadows, textures, and crown shapes — without requiring millions of manually labeled examples. This enables CHMv2 to deliver major gains in accuracy and detail over the previous version.

Additionally, the model's R² — a way of measuring how closely predictions match real-world measurements — has soared from 0.53 to 0.86. The model now delivers sharper canopy maps and minimizes bias for tall trees, making its predictions more trustworthy for scientific and operational use.

The training dataset for CHMv2 was also expanded and improved by adding more geographically diverse, high-quality lidar examples. To better align satellite imagery with real-world lidar measurements, we built automated matching tools and developed a specialized loss function to address the unique challenges of canopy height estimation. Together, these advances enable CHMv2 to set a new bar for global forest mapping.