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
Organization: Ai2

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}.