OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation
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arXiv
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| Format: | Preprint |
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2025
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| author | Herzog, Henry Bastani, Favyen Zhang, Yawen Tseng, Gabriel Redmon, Joseph Sablon, Hadrien Park, Ryan Morrison, Jacob Buraczynski, Alexandra Farley, Karen Hansen, Joshua Howe, Andrew Johnson, Patrick Alan Otterlee, Mark Schmitt, Ted Pitelka, Hunter Daspit, Stephen Ratner, Rachel Wilhelm, Christopher Wood, Sebastian Jacobi, Mike Kerner, Hannah Shelhamer, Evan Farhadi, Ali Krishna, Ranjay Beukema, Patrick |
| author_facet | Herzog, Henry Bastani, Favyen Zhang, Yawen Tseng, Gabriel Redmon, Joseph Sablon, Hadrien Park, Ryan Morrison, Jacob Buraczynski, Alexandra Farley, Karen Hansen, Joshua Howe, Andrew Johnson, Patrick Alan Otterlee, Mark Schmitt, Ted Pitelka, Hunter Daspit, Stephen Ratner, Rachel Wilhelm, Christopher Wood, Sebastian Jacobi, Mike Kerner, Hannah Shelhamer, Evan Farhadi, Ali Krishna, Ranjay Beukema, Patrick |
| contents | Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temporal foundation model that employs a novel self-supervised learning formulation, masking strategy, and loss all designed for the Earth observation domain. OlmoEarth achieves state-of-the-art performance compared to 12 other foundation models across a variety of research benchmarks and real-world tasks from external partners. When evaluating embeddings OlmoEarth achieves the best performance on 15 out of 24 tasks, and with full fine-tuning it is the best on 19 of 29 tasks. We deploy OlmoEarth as the backbone of an end-to-end platform for data collection, labeling, training, and inference of Earth observation models. The OlmoEarth Platform puts frontier foundation models and powerful data management tools into the hands of non-profits and NGOs working to solve the world's biggest problems. OlmoEarth source code, training data, and pre-trained weights are available at $\href{https://github.com/allenai/olmoearth_pretrain}{\text{https://github.com/allenai/olmoearth_pretrain}}$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation Herzog, Henry Bastani, Favyen Zhang, Yawen Tseng, Gabriel Redmon, Joseph Sablon, Hadrien Park, Ryan Morrison, Jacob Buraczynski, Alexandra Farley, Karen Hansen, Joshua Howe, Andrew Johnson, Patrick Alan Otterlee, Mark Schmitt, Ted Pitelka, Hunter Daspit, Stephen Ratner, Rachel Wilhelm, Christopher Wood, Sebastian Jacobi, Mike Kerner, Hannah Shelhamer, Evan Farhadi, Ali Krishna, Ranjay Beukema, Patrick Computer Vision and Pattern Recognition Machine Learning Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temporal foundation model that employs a novel self-supervised learning formulation, masking strategy, and loss all designed for the Earth observation domain. OlmoEarth achieves state-of-the-art performance compared to 12 other foundation models across a variety of research benchmarks and real-world tasks from external partners. When evaluating embeddings OlmoEarth achieves the best performance on 15 out of 24 tasks, and with full fine-tuning it is the best on 19 of 29 tasks. We deploy OlmoEarth as the backbone of an end-to-end platform for data collection, labeling, training, and inference of Earth observation models. The OlmoEarth Platform puts frontier foundation models and powerful data management tools into the hands of non-profits and NGOs working to solve the world's biggest problems. OlmoEarth source code, training data, and pre-trained weights are available at $\href{https://github.com/allenai/olmoearth_pretrain}{\text{https://github.com/allenai/olmoearth_pretrain}}$. |
| title | OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2511.13655 |