AI for Conservation: Training Artificial Intelligence to Identify Trees from Satellite Imagery

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2Q 2021 | IN-6159


Learning to Spot Trees


Scientists from NASA’s Goddard Space Flight Center in Greenbelt, Maryland, along with international collaborators, have demonstrated a new method for mapping the location and size of trees growing outside of forests. Billions of trees were discovered in arid and semi-arid regions, laying the groundwork for more accurate global measurement of carbon storage on land. Using supercomputers and machine learning algorithms, the team mapped the crown diameter — the width of a tree when viewed from above — of more than 1.8 billion trees across an area of more than 500,000 square miles, or 1,300,000 square kilometers. The team mapped how tree crown diameter, coverage, and density varied depending on rainfall and land use.

The scientists used high-resolution commercial satellite imagery from DigitalGlobe to identify and measure the individual trees. These images came from the commercial GeoEye-1, QuickBird-2, WorldView-2, and WorldView-3 satellites with the team focusing the study area on the dryland regions of West Africa, consisting of varying landscapes — arid, semi-arid, and…

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