Forests are vital ecosystems for fighting climate change, supporting livelihoods and protecting biodiversity.

Yet critical gaps remain in the scientific understanding of the structure and extent of forests around the globe. While satellite data has made it possible to visualize and analyze timely, globally consistent information about the world’s forests, the majority of existing data has resolutions of 10 or 30 meters, which is not granular enough to see the details of more dispersed forest systems such as agroforestry, drylands forests and alpine forests, which together constitute more than a third of the world's forests.

But now, WRI’s Global Restoration Initiative and researchers from Land & Carbon Lab have partnered with Meta to develop a groundbreaking AI foundation model that we’ve used to produce the world’s first global map of tree canopy height at a 1-meter resolution, allowing the detection of single trees at a global scale.

This new high-resolution data sets a baseline for remotely monitoring changes at the level of individual trees, making it a critical advancement for measuring land use emissions and tracking progress on conservation and restoration projects, which are essential for achieving the world’s goals for climate, nature and people. While this initial data set has limitations, it demonstrates the power of foundation models — a type of AI model that can serve as the “foundation” for a variety of tasks — to pave a new path toward AI-driven earth monitoring.

How was the 1-meter tree canopy height data developed?
The accelerating pace of breakthroughs in AI and foundation models are changing the ways in which we all interact with the world around us. In recent years, mapping forests through remote sensing has made rapid improvements in terms of scale, resolution and refresh rate (how often an area is imaged).

This new 1-meter tree canopy height data set creates a global baseline of where trees are located, including individual trees and forests with open canopies.

To create the maps at this resolution, both a globally robust model and the computational resources to generate 100 trillion pixels of data were needed. To do this, we used a state-of-the-art AI model called DiNOv2 based on methods developed by AI at Meta Research. The model was trained on 18 million satellite images encompassing more than a trillion pixels from across the globe.

To learn more visit: https://www.landcarbonlab.org/news-updates/mapping-trees-unprecedented-detail-ai