Mapping the Forests with Precision:Introducing Canopy Height Maps

Mapping the World's Forests with Greater Precision: Introducing Canopy Height Maps v2 Meta AI AI Research The Latest About Get Llama Try Meta AI Computer Vision Mapping the World's Forests with Greater Precision: Introducing Canopy Height Maps v2 March 10, 2026 • 5 minute read 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. Collaborations with the Public Sector in Europe, the United States, and Beyond Our previous AI model and associated maps, CHMv1, are already supporting climate migration, restoration, and biodiversity efforts. In the United Kingdom, Forest Research — the research agency of the Forestry Commission — is using these to transform how they monitor and manage Great Britain’s forests. Their work demonstrates how these tools can support national-scale forest inventory and help track progress toward climate commitments. Read more about this collaboration . Beyond the United Kingdom, Canopy Height Maps are helping national and local governments across Europe advance their environmental goals. The European Commission’s Joint Research Centre used the first version of Canopy Height Maps in its Global Forest Cover map for 2020 research ( ESSD paper , EU Forest Observatory) and hopes to use CHMv2 for future map versions and other tree monitoring efforts, including the 3 Billion Tree Initiative — a commitment to plant at least 3 billion biodiverse trees across the European Union by 2030. In the United States, these maps have also been leveraged in city planning tools being used for the implementation of Cities for Smart Surfaces, an initiative led by the Smart Surfaces Coalition and signed on by the mayors of 10 cities, including Atlanta, Baltimore, Boston, Columbia (South Carolina), Dallas, and New Orleans. Cities for Smart Surfaces is a multiyear project funded by Waverley Street Foundation and the MacArthur Foundation to cool cities and metropolitan areas with reflective (cool) roofs and pavements, green roofs, solar energy, porous pavements, rain gardens, and trees. Additionally, WRI Ross Center for Sustainable Cities is making use of these maps in Cool Cities Lab, a forthcoming globally relevant scenario planning tool — initially available for cities in 11 countries — that helps cities assess the temperature effects of urban cooling interventions. Looking Ahead CHMv2 represents a significant step forward, but challenges remain. We’re continuing to improve predictions in regions where data is sparse, address viewing-geometry effects, and extend temporal coverage to better support change detection over time. By making these advances available to the research community, we hope to accelerate progress in forest monitoring worldwide. Better maps enable better decisions — for conservation, climate action, and the countless communities that depend on healthy forests. 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