TESSERA, a pixelwise geospatial foundation model

TESSERA is an open and pixel-wise foundation model for multi-modal (Sentinel-1/2) earth observation time series that learns robust, label-efficient embeddings.

Our goal with TESSERA is to make manipulating global satellite intelligence as easy as conventional programming tasks are. Towards this we release global, annual, 10m, pixel-wise embeddings together with open weights and code and lightweight adaptation heads. We also develop practical tooling for large-scale retrieval and inference at planetary scale.

As with any good foundation model, there are a staggering array of downstream tasks which can benefit. TESSERA embeddings deliver state-of-the-art accuracy with high label efficiency across diverse classification, segmentation, and regression tasks.