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[ARTICLE · art-156269] src=runtimewire.com ↗ pub= topic=machine-learning verified=true sentiment=↑ positive

Ai2 releases satellite embeddings that let mapping teams skip repeat model runs

Ai2 released OlmoEarth v1.3 on October 22nd, pairing a satellite model with precomputed annual 10-meter embeddings so mapping teams can build new maps without rerunning the model over imagery for each task. Coverage is global for 2024 and 2025, near-global for 2023, and limited to selected countries for 2017-2022, and Ai2 says its embeddings led two named competitors on most of its evaluations. The Seattle nonprofit, founded in 2014 by late Microsoft co-founder Paul Allen, makes the features publicly accessible, though uneven year-by-year coverage and artifact license restrictions shape where teams can use them.

read1 min views1 publishedOct 23, 2026
Ai2 releases satellite embeddings that let mapping teams skip repeat model runs
Image: Runtimewire (auto-discovered)

OlmoEarth v1.3 supplies precomputed 10-meter features and a browser-based mapping tool. Coverage is global for 2024 and 2025, near-global for 2023, and limited to selected countries for 2017-2022; Ai2 says its embeddings led two named competitors on most of its evaluations.

        By [RuntimeWire Staff](https://runtimewire.com/author/runtimewire-staff)
        · Published 

Primary source: [Hugging Face Newsroom](https://huggingface.co/blog/allenai/olmoearth-v1-3)

Why it matters #

Precomputed features let mapping teams test many downstream questions without rerunning a large satellite model for each one. OlmoEarth makes that data publicly accessible, but the uneven year-by-year coverage and artifact license restrictions shape where and how teams can use it.

Ai2, the Seattle nonprofit founded in 2014 by late Microsoft co-founder Paul Allen, released OlmoEarth v1.3 on October 22nd. The release pairs a satellite model with precomputed annual embeddings: reusable features intended to let researchers make new maps without running the model over satellite imagery again for each task.…

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