CrossEarth
Segments satellite imagery in regions and conditions it was not trained on.
- Field
- Remote sensing
- Method
- Foundation model
- Data it takes
- imagery
- License
- MIT
Semantic segmentation models typically degrade when applied outside the region, resolution or climate they were trained on. CrossEarth targets that problem using a frozen DINOv2 backbone with augmentation designed to survive domain shift, and ships with a segmentation head.
It suits cases where labels exist for one area and the same classification is needed somewhere different.
Catalog entry last checked 2026-08-17. All models →
Run CrossEarth on your data
The lab runs this on its own compute, in a container, with the inputs and parameters recorded alongside the result. Initial scoping conversations are free.
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