# Satellite embedding model

OlmoEarth v1.3 turns satellite embeddings from per-task fine-tuning into compute-once, reuse-everywhere: it reads a year of Sentinel-1 radar and Sentinel-2 and Landsat multispectral time series and outputs a 128-dimensional vector for every 10-meter pixel on Earth, with the first 64 dimensions usable on their own. Until now the primary way to use OlmoEarth was fine-tuning in OlmoEarth Studio, training a model per task, which requires GPU access and the ability to download and store imagery. Against AlphaEarth Foundations and Tessera v2 across 27 evaluations on six continents, the 128-dimensional embeddings score highest on 20 of 27 and rank highest overall, while the 64-dimensional version leads on 18 of 27.

OlmoEarth v1.3 turns satellite embeddings from per-task fine-tuning into compute-once, reuse-everywhere: it reads a year of Sentinel-1 radar and Sentinel-2 and Landsat multispectral time series and outputs a 128-dimensional vector for every 10-meter pixel on Earth, with the first 64 dimensions usable on their own. Until now the primary way to use OlmoEarth was fine-tuning in OlmoEarth Studio, training a model per task, which requires GPU access and the ability to download and store imagery. Against AlphaEarth Foundations and Tessera v2 across 27 evaluations on six continents, the 128-dimensional embeddings score highest on 20 of 27 and rank highest overall, while the 64-dimensional version leads on 18 of 27.