We combine Machine Learning, Remote Sensing and Geography to discover unprecedented knowledge at the level of individual trees.​
We use PlanetScope, Sentinel-2, Landsat, Skysat, Maxar and Gaofen satellite images to produce detailed maps on tree locations, cover, biomass, and canopy height, from local to continental scale.​
View our maps!​​
Our work has been published in Nature, Nature Climate Change, Nature Sustainability, PNAS Nexus, Science Advances, Nature Plants, Nature Food, Nature Geoscience, Nature Ecology, Nature Communications, etc.​​​
Latest
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New study in Nature Cities studying urban trees with RapidEye and PlanetScope
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Nature Reviews: High-resolution sensors and deep learning models for tree resource monitoring.
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The Tree Explorer is regularly updated (all still experimental).
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We mapped about 3 million baobabs all over the Sahel, published in Nature Ecology.
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New paper at ECCV 2024 including codes and dataset
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New paper in Nature Sustainability also covered in "The Hindu".
HIGHLY DETAILED
Trees are mapped as objects.
Homogeneous
We combine sensors to improve temporal and spatial consistency.
Large Scale
We work at continental and even global scale.
LONG TERM
We work on time series up to 25 years.