TESSERA

A pixel-wise Earth observation foundation model
from the University of Cambridge · ucam-eo

TESSERA turns raw satellite time series into ready-to-use embeddings. For every 10 m pixel on Earth it reads a full year of Sentinel-1 (SAR) and Sentinel-2 (optical) observations and compresses them into a compact, task-agnostic vector — a learned summary of the land's spectral and temporal behaviour. Trained self-supervised on billions of pixels, with no labels required, these embeddings work as drop-in features for land cover and crop mapping, biomass and carbon estimation, change detection, and more, and are far cheaper to store and serve than raw imagery.

Models

👉 Everything in one place: the TESSERA collection.

Get started

Just want embeddings? Skip the model entirely — download ready-made global embeddings with the geotessera Python library:

pip install geotessera

...or request coverage for your region. New v2 embeddings can be pre-requested here.

Want to run the model yourself? Generate embeddings from your own Sentinel-1/2 tiles with the code and instructions in ucam-eo/tessera.

Learn more

• 🌐 Website — https://geotessera.org
• 📦 Model & inference code — https://github.com/ucam-eo/tessera
• 🐍 Embeddings library — https://github.com/ucam-eo/geotessera
• 📄 Papers — TESSERA v2: Scaling Pixel-wise Earth Foundation Models · TESSERA (v1)

_{Model weights are released under CC0-1.0; the geotessera library under MIT. Use is additionally governed by the TESSERA Acceptable Use Policy.}