OlmoEarth v1.3: State-of-the-art embeddings for the whole planet | Ai2

OlmoEarth v1.3: State-of-the-art embeddings for the whole planet October 22, 2026 Ai2 --- ModelsTech ReportDataCodeDemoInteractive story Today, we’re releasing OlmoEarth v1.3, a state-of-the-art satellite embedding model Alongside OlmoEarth v1.3, we’re sharing precomputed, annual embeddings for the whole planet 10m, 64- and 128-dimensions, plus a new browser-based tool to explore and customize maps based on those embeddings — the OlmoEarth Embeddings Explorer This means anyone can put OlmoEarth v1.3 to work right away, from tracking change over time to finding similar places—no sign-in, and no advanced technical knowledge required

Details

Until now, the primary way to use OlmoEarth was fine-tuning in OlmoEarth Studio: training a model for each task and running it over satellite imagery to make a map That requires GPU access and the ability to download and store the imagery Embedding models compress satellite data into ready-to-use features that can be reused cheaply for many tasks With OlmoEarth v1.3, we’re excited to give users more ways to explore and engage with planetary artifacts in whatever way suits them best

Context

Compute once, reuse everywhere An embedding represents what the model learned from millions of images OlmoEarth v1.3 reads a year of radar Sentinel-1 and multispectral Sentinel-2 and Landsat satellite time series and produces one 128-dimensional vector for every 10-meter pixel on Earth; the first 64 dimensions also work on their own as a smaller embedding

Impact

Foundation models and global embeddings are expensive to produce, and to make sure everyone is able to access the benefits, we’ve computed these embeddings for the entire planet and shared them with the community on Source Cooperative From there, a crop map, a land-cover map, or a change analysis becomes a lightweight modeling problem: train a small classifier on top and run it locally

What's Next

State-of-the-art embeddings We evaluated OlmoEarth v1.3's embeddings against AlphaEarth Foundations AEF and Tessera v2 across 27 evaluations, covering six continents and many different tasks OlmoEarth’s 128-dimensional embeddings score highest on 20 of 27 evals and rank highest overall The 64-dimensional version, at half the size, still leads on 18 of 27 evals

Background

As part of this evaluation, we introduce PLANTEUR, a new crop-type benchmark for tropical farming, and the embeddings generalize best to places a classifier has never seen PLANTEUR technical report is available here Everything behind them is fully open: the model weights, the pretraining data, and the pretraining code In practice, this means a small classifier trained on the OlmoEarth embeddings is enough to map what’s on the ground: think classifying smallholder crops in Kenya, detecting brick kilns west of Dhaka, assessing fire damage in Indonesia, and tracking gold mining in the Peruvian Amazon

Analysis

Compared to previous generations of OlmoEarth models, OlmoEarth v1.3 was also our cheapest model to train It took 53% of the computing time of our last version v1.2 and 18% of our first v1, with 50% and 22% of the carbon emissions And it also remains a strong fine-tuning model, matching v1.2's performance across research and partner labels Our latest model builds on the same foundation as earlier OlmoEarth versions, tuned so the embeddings stay lightweight without giving up accuracy across tasks, and that efficiency work carries forward into the versions ahead

More

Full results and methodology are in our technical report One place, nine years In Peru's Madre de Dios region, illegal gold mining around La Pampa peaked in 2017 and 2018, clearing more than 1,200 hectares of forest in the buffer zone of the Tambopata National Reserve, according to Amazon Conservation's MAAP monitoring program In February 2019, Peru's Operation Mercurio shut most of it down