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Increasing efficiency by decreasing sequence lengths Designing the token For developers For researchers Get started 🧠 Models: https://huggingface.co/collections/allenai/olmoearth | 📄 Tech Report: https://allenai.org/papers/olmoearth_v1_1 | 💻 Code: https://github.com/allenai/olmoearth_pretrain
We released OlmoEarth (v1) in November 2025. Since then, partners have applied it across a wide range of tasks, from tracking mangrove change to classifying drivers of forest loss to producing country-scale crop-type maps in days, scaling deployments to national, continental, and global areas. Every release moves us closer to our mission: bringing state-of-the-art AI to organizations and communities working to protect people and our planet.
When OlmoEarth processes satellite imagery to make predictions across tens to hundreds of thousands of square kilometers, efficiency shapes what’s possible. Over the full lifecycle of running OlmoEarth – data export, preprocessing, inference, and post-processing – compute is by far the highest cost. A more efficient model means we can support more partners on the OlmoEarth Platform, and that anyone running OlmoEarth on their own can leverage this technology faster and at lower expense.
