Mapping the World's Forests with Greater Precision: Introducing Canopy Height Maps v2
Meta AI
Meta just dropped a much sharper AI model for mapping tree heights worldwide, built on its DINOv3 vision tech. Accuracy jumped hugely, so governments can now track forests, carbon and deforestation with way less guesswork.
Based on reporting by Meta AI — read the original for the full story.
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Meta and the World Resources Institute just pushed out the second version of their Canopy Height Maps, and the jump in quality is the kind of thing that actually changes what researchers can do with satellite data. The core upgrade is swapping out the old DINOv2 backbone for DINOv3, Meta's newer self-supervised vision model, trained on a satellite image set called SAT-493M. That's 493 million images, for context, all unlabeled — the model learns to recognize shadows, canopy texture, and crown shape on its own rather than needing armies of humans to hand-label trees.
The numbers back up the hype here. R², the metric researchers use to gauge how well predictions match ground truth, climbed from 0.53 in the first version to 0.86 now. That's not a marginal tweak — it's the difference between a map you treat as a rough estimate and one you can actually build policy on. Meta also says the model now handles tall trees with far less bias, which matters because older height-mapping tools tended to underestimate exactly the trees that store the most carbon.
None of this happened by just throwing more compute at the problem. The team expanded the lidar training data to cover more geographically diverse forests, built new tools to line up satellite imagery with real lidar readings, and wrote a custom loss function specifically for the quirks of measuring canopy height. It's the unglamorous data-plumbing work that usually decides whether a flashy new AI backbone actually translates into usable science.
And the maps aren't sitting in a lab somewhere. Forest Research, the UK's Forestry Commission arm, is already using version one to track climate commitments across Great Britain. The European Commission's Joint Research Centre used the original maps for its 2020 global forest cover study and wants CHMv2 for future editions, including monitoring the EU's pledge to plant 3 billion trees by 2030. In the US, ten cities — Atlanta, Baltimore, Boston, Dallas, New Orleans among them — are feeding this data into urban cooling plans through the Smart Surfaces Coalition, deciding where trees, green roofs, and reflective pavement will do the most good.
Meta says the model and the global maps are open source, downloadable, and viewable through Google Earth Engine. Gaps remain — sparse data in some regions, distortion from viewing angles, and limited ability to track change over time — but this is clearly meant as infrastructure other people build on, not a one-off research flex.
My take — AI-written commentary, not fact-checked reporting
Credit where due: this is Meta doing something with AI that's boring in the best way — no chatbot, no hype cycle, just better measurement tools handed to people who actually manage land. Open-sourcing the model is the right call, and it's the kind of soft-power move that does more for Meta's reputation in Brussels than any lobbying push. If only every 'AI for good' announcement came with an R² score instead of a press release adjective.
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