OlmoEarth v1.1: A more efficient family of OlmoEarth models
About
We present a set of improvements to the OlmoEarth family. These improvements allow us to cut compute costs during training ($1.7 \times$ reduction in GPU hours required to train our Base models) and inference ($2.9\times$ reductions in MACs on Sentinel-2 tasks), while maintaining the models' overall performance. All training code is available at github.com/allenai/olmoearth_pretrain.
Gabriel Tseng, Yawen Zhang, Favyen Bastani, Henry Herzog, Joseph Redmon, Hadrien Sablon, Piper Wolters, Patrick Alan Johnson, Christopher Wilhelm, Patrick Beukema• 2026
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Semantic segmentation | Sen1Floods11 (test) | mIoU80.1 | 24 | |
| Semantic segmentation | PASTIS (test) | mIoU30.8 | 22 | |
| Image Classification | m-bigearthnet (test) | µF1 Score72.3 | 19 | |
| Classification | m-so2sat (test) | Mean Accuracy69.8 | 17 | |
| Multi-Label Classification | m-bigearthnet (test) | µF1 Score63.7 | 4 | |
| Time-series classification | CropHarvest PRC (test) | Accuracy82 | 4 | |
| Time-series classification | Nandi (test) | Accuracy74.5 | 4 | |
| Object Detection | Vessel Detection (test) | F1 Score78.9 | 2 | |
| Semantic segmentation | Solar Farm Detection (test) | mIoU84.6 | 2 |
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