High Resolution Canopy Height Maps by WRI and Meta

Accurate forest mapping can lead to more accountable forest-based carbon offsets and facilitate the development of carbon projects. To support this work, Meta partnered with the World Resources Institute to create Canopy Height Maps. Canopy Height Maps were developed using AI models on high-resolution worldwide Maxar satellite imagery for over half of the globe.

Data and Resources

Additional Info

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Last Updated January 8, 2026, 07:35 (UTC)
Created January 8, 2026, 07:33 (UTC)
accessRights <h4><strong><u>How to Cite</u></strong></h4><p>High Resolution Canopy Height Maps by WRI and Meta was accessed on&nbsp;<code style="background-color: rgb(249, 242, 244); color: rgb(199, 37, 78);">DATE</code>&nbsp;from https://registry.opendata.aws/dataforgood-fb-forests. Meta and World Resources Institude (WRI) - 2024. High Resolution Canopy Height Maps (CHM). Source imagery for CHM © 2016&nbsp;<a href="https://www.maxar.com/products/imagery-basemaps" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 125, 188);">Maxar</a>. Accessed DAY MONTH YEAR.</p>
creationMethod <p><span style="color: rgb(51, 51, 51);">Created using machine learning models on high-resolution worldwide Maxar satellite imagery.</span></p>
dataAuthType public
dataType GeoTIFF
datasetPageUrl https://registry.opendata.aws/dataforgood-fb-forests/
docsURL https://github.com/facebookresearch/HighResCanopyHeight
issueDate 2016-01-01
lastUpdateDate 2025-04-09
license cc-by
ndp_creator_md5 e22ccfac8fe6a68759e60ced39f231b9
pocEmail dataforgood@meta.com
pocName Meta
purpose <p>Read more about purpose at: https://research.facebook.com/blog/2023/4/every-tree-counts-large-scale-mapping-of-canopy-height-at-the-resolution-of-individual-trees/</p>
spatialRes 1 meter
status submitted
theme ["climate-change","computer-vision","machine-learning","canopy-height-model"]
updateFreq As needed
uploadType dataset
usageInfo <h4>Usage Examples</h4><p><br></p><h5><strong>Tools &amp; Applications</strong></h5><ul><li><a href="https://meta-forest-monitoring-okw37.projects.earthengine.app/view/canopyheight" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 125, 188);">Global Canopy Height on Earth Engine</a>&nbsp;by Meta and WRI</li></ul><p><br></p><h5><strong>Publications</strong></h5><ul><li><a href="https://sustainability.fb.com/blog/2024/04/22/using-artificial-intelligence-to-map-the-earths-forests/" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 125, 188);">Using Artificial Intelligence to Map the Earth’s Forests</a>&nbsp;by Jamie Tolan, Camille Couprie, John Brandt, Justine Spore, Tobias Tiecke, Tracy Johns and Patrick Nease</li><li><a href="https://research.facebook.com/blog/2023/4/every-tree-counts-large-scale-mapping-of-canopy-height-at-the-resolution-of-individual-trees/" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 125, 188);">Every tree counts: Large-scale mapping of canopy height at the resolution of individual trees</a>&nbsp;by Jamie Tolan, Camille Couprie, and Tracy Johns</li><li><a href="https://www.sciencedirect.com/science/article/pii/S003442572300439X" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 125, 188);">Sub-meter resolution canopy height maps using self-supervised learning and a vision transformer trained on Aerial and GEDI Lidar</a>&nbsp;by Jamie Tolan, Hung-I Yang, Ben Nosarzewski, Guillaume Couairon, Huy Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie</li></ul><p><br></p>