OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation
Authors: name: Henry Herzog; name: Favyen Bastani; name: Yawen Zhang; name: Gabriel Tseng; name: Joseph Redmon; name: Hadrien Sablon; name: Ryan Park; name: Jacob Morrison; name: Alexandra Buraczynski; name: Karen Farley; name: Joshua Hansen; name: Andrew Howe; name: Patrick Alan Johnson; name: Mark Otterlee; name: Ted Schmitt; name: Hunter Pitelka; name: Stephen Daspit; name: Rachel Ratner; name: Christopher Wilhelm; name: Sebastian Wood; name: Mike Jacobi; name: Hannah Kerner; name: Evan Shelhamer; name: Ali Farhadi; name: Ranjay Krishna; name: Patrick Beukema
Published: 2025-11-17 18:06:26

Abstract: 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 https://github.com/allenai/olmoearth_pretrain{https://github.com/allenai/olmoearth_pretrain}.