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Gabriel Tseng, Favyen Bastani, Patrick Alan Johnson, Yawen Zhang, Piper Wolters, Ando Shah, Hadrien Sablon, Joseph Redmon, Heather DeHaven, Rachel Ratner, Sasha Levy, Jordan Steward, Katrina Midgley, Andrew Howe, Hunter Pitelka, Ted Schmitt
· 1 min read
ResearcharXiv cs.CV
OlmoEarth v1.3: Embeddings
arXiv:2610.32294v1 Announce Type: new
Abstract: We make some OlmoEarth embeddings. To do this, we train a new model: OlmoEarth v1.3. OlmoEarth v1.3 is designed to condense multimodal, multitemporal remote sensing data into a single digestible 64 or 128-dimensional vector. OlmoEarth v1.3 produces state-of-the-art embeddings. In addition, OlmoEarth v1.3 is a strong encoder backbone -- competitive with OlmoEarth v1 and v1.2 -- while requiring 18% and 53% of the GPU hours to train, respectively. We use OlmoEarth v1.3 to compute global, annualized 10m embeddings, which we release under the OlmoEarth artifact license.
Original source
This story was published by arXiv cs.CV and written by Gabriel Tseng, Favyen Bastani, Patrick Alan Johnson, Yawen Zhang, Piper Wolters, Ando Shah, Hadrien Sablon, Joseph Redmon, Heather DeHaven, Rachel Ratner, Sasha Levy, Jordan Steward, Katrina Midgley, Andrew Howe, Hunter Pitelka, Ted Schmitt. SyncAI.news shows a preview; the complete article is on the publisher's site.
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