AI Foundation Models for Earth Observation
What are geospatial foundation models, who has built them, and how do they change the economics of EO analytics?
Chapter summary
Foundation models are large neural networks pre-trained on huge archives of satellite imagery so that they can be adapted to new tasks (crop type, flood extent, building damage, land cover) with few labelled examples.
Key takeaways
- 1
Three matter in 2026: Google's AlphaEarth Foundations (2025), which turns multi-sensor data into compact "embeddings" for every 10 m patch of land and is available as data but not as open model weights; NASA and IBM's Prithvi-EO-2.0 (December 2024), open-source, trained on Harmonized Landsat–Sentinel data and the first geospatial foundation model run in orbit; and Clay v1.5, open-access, trained on about 70 million image chips from seven sensors.
- 2
Their effect is to make analytics cheaper to build, shifting value from model-building to labelled outcomes, workflow and data access.
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