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AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data

About

Unprecedented volumes of Earth observation data are continually collected around the world, but high-quality labels remain scarce given the effort required to make physical measurements and observations. This has led to considerable investment in bespoke modeling efforts translating sparse labels into maps. Here we introduce AlphaEarth Foundations, an embedding field model yielding a highly general, geospatial representation that assimilates spatial, temporal, and measurement contexts across multiple sources, enabling accurate and efficient production of maps and monitoring systems from local to global scales. The embeddings generated by AlphaEarth Foundations are the only to consistently outperform a suite of other well-known/widely accepted featurization approaches tested on a diverse set of mapping evaluations without re-training. We have released a dataset of global, annual, analysis-ready embedding field layers from 2017 through 2024.

Christopher F. Brown, Michal R. Kazmierski, Valerie J. Pasquarella, William J. Rucklidge, Masha Samsikova, Chenhui Zhang, Evan Shelhamer, Estefania Lahera, Olivia Wiles, Simon Ilyushchenko, Noel Gorelick, Lihui Lydia Zhang, Sophia Alj, Emily Schechter, Sean Askay, Oliver Guinan, Rebecca Moore, Alexis Boukouvalas, Pushmeet Kohli• 2025

Related benchmarks

TaskDatasetResultRank
ClassificationTreeSatAI-TS
F1 Score76.9
75
RegressionBorneo CHM (1% labels)
RMSE36.03
25
RegressionBorneo CHM (30% labels)
RMSE20.62
25
Semantic segmentationAustrian Crop (1% labels)
mIoU14.64
25
Semantic segmentationAustrian Crop (30% labels)
mIoU21.75
25
RegressionBorneo CHM (All labels)
RMSE16.11
25
Semantic segmentationAustrian Crop All labels
mIoU25.7
25
Semantic segmentationPASTIS-R (30% labels)
mIoU46.42
24
Semantic segmentationPASTIS-R (All labels)
mIoU51.08
24
RegressionBiomassters (1% labels)
RMSE35.45
24
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