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.
2 Scaling up to v2
The v2 checkpoint paper was uploaded in July 2026, led by Frank Feng with Sadiq Jaffer, David Coomes, Srinivasan Keshav and others, using the UKRI AIRR allocation on Isambard 2 for a hero run to scale the model. Hundreds of ablation sweeps gave us a simple rule for allocating compute: as the training budget grows, the encoder and the satellite data should grow together while the projector stays fixed. The result is a 1-billion parameter model, with a 2B one still training (as of July 2026).
Since inference over 1.8 million tiles has to stay cheap, we distil that model
into a family of smaller student models. Pleasingly, TESSERA-v2-1B-M has
fewer parameters than v1 and still outperforms it, along with every other
GeoFM we could find. v2 also adds Matryoshka embeddings, so the first 1/8th of the 128
dimensions carry ~90% of the downstream performance. This matters for smaller
devices, but leaves open how to slice dimensions efficiently in Zarr.
3 Distribution and downstream results
We are moving the embeddings to Source Cooperative after the enthusiastic reaction at CNG London, which mostly means routing terabytes via Cambridge to dodge five-figure AWS egress charges, with Zarr v3 becoming the default access mechanism. Isaac Corley has been very helpful in getting us started.
On the downstream side, tree species mapping in Trentino showed foundation-model embeddings reaching near-asymptotic accuracy from as few as 5% of training parcels, provided a nonlinear classifier is used.
Pedro Sousa is applying the embeddings to probabilistic weather downscaling, where they supply the sub-grid structure that topography alone cannot. Independent validations have also started appearing, with TESSERA the best decametric option for cocoa mapping in Côte d'Ivoire and on GeoLifeCLEF. On the outreach side, Sadiq Jaffer presented at the RAISE Summit in Paris with Vultr, and we ran a stall at a House of Lords reception on AI for science with NVIDIA, who have been helping optimise our kernels.