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Meta, WRI, and Land & Carbon Lab Introduce Global AI-Driven Tree Canopy Height Map

Meta, WRI, and Land & Carbon Lab Introduce Global AI-Driven Tree Canopy Height Map

Meta, WRI, and Land & Carbon Lab launch a 1-meter resolution AI-powered tree canopy height map, advancing carbon monitoring, conservation, and transparency in forest ecosystems

Meta, in collaboration with the World Resources Institute (WRI) and Land & Carbon Lab, has launched the world’s first AI-powered global tree canopy height map with an unprecedented 1-meter resolution. This cutting-edge tool marks a transformative advancement in environmental monitoring, enabling precise detection of individual trees worldwide while addressing critical knowledge gaps about forest ecosystems.


Revolutionizing Forest Monitoring

The map harnesses AI to process over a trillion pixels from 18 million satellite images, establishing a high-accuracy baseline for tree canopy height data. With a mean absolute error of just 2.8 meters, the model is a reliable tool for forest analysis, ensuring credible data for both conservationists and commercial entities.


"Democratizing access to artificial intelligence can be an important tool in unlocking finance for and increasing transparency in mitigating and adapting to climate change," said Meta.


Covering one-third of Earth’s landmass, or 50 million square kilometers, the dataset is openly accessible through platforms such as AWS, Google Earth Engine, and GitHub. This open-access model aims to accelerate innovation in carbon credit verification and restoration projects.


Enhancing Carbon Market Accountability

The high-resolution map significantly improves the monitoring and verification processes for forest-based carbon credits. Its ability to track growth in sparse or fragmented forests strengthens the transparency and credibility of carbon markets, addressing a key challenge in global climate initiatives.


Meta emphasized, “Forest-based carbon removal and the use of technology to better monitor, report, and verify carbon sequestration are essential components of Meta’s carbon removal strategy.”


Pioneering AI for Climate Solutions

Powered by the AI model DiNOv2, which utilizes Self-Supervised Learning (SSL) to analyze unlabeled satellite imagery, the tool ensures scalability and robust global applicability. This versatile technology extends beyond forest monitoring, offering capabilities in tree detection and segmentation for broader applications.


By enabling precise forest management and fostering innovation in conservation, Meta and its partners aim to drive impactful global action on climate resilience and sustainability.

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