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.
1 Storage, Zarr, and cloud-native distribution
A lot of the early 2026 work has been on the plumbing needed to actually use TESSERA at scale. We restructured the store around a Zarr v3 layout and a shared geo-embeddings convention, iterating on the chunking after community feedback and shipping it through geotessera 0.8 with multi-year support and a browser-based TZE explorer backed by HTTP range requests.
On the storage side, we expanded the Cambridge Ceph cluster to 1.4PB just in time to mirror the full half-petabyte to AWS Open Data, with the sync finishing a week or so later. The
geotessera client now discovers tiles from multiple
registries so consumers can pull from whichever copy is closest. In parallel,
Mark Elvers has been porting Brotli/Zstd/Snappy to OxCaml and
building ocaml-zarr as the basis
for native OCaml access to the cloud-native stores.