# TESSERA v2: Scaling Pixel-wise Earth Foundation Models

*2026-07-01 — paper*

Authors: Zhengpeng Feng, Sadiq Jaffer, Ira Shokar, Jovana Knezevic, Mark Elvers, Clement Atzberger, Robin Young, Aneesh Naik, Niall Robinson, Andrew Blake, David Coomes, Anil Madhavapeddy, Srinivasan Keshav


Pixel-wise Earth-observation (EO) foundation models are now achieving
state-of-the-art performance via generated spatial embeddings. However, how
these models scale and how best to spend a pretraining budget remain poorly
understood.

We present the largest controlled scaling study for EO to date: 395 training
runs on 1,024 GH200 superchips within a fixed pixel-wise Barlow Twins family,
each evaluated on 15 downstream tasks. We find that pretraining loss barely
predicts downstream performance (\|Pearson r\| \< 0.2), so selecting models by
loss wastes a large share of the compute. We also find that, as the training
budget grows, the encoder and the data should grow together while the projector
stays fixed, which gives a simple rule for allocating compute. Using this rule,
we train a family of pixel-wise models (0.5B and 1B, with a 2B model in
training) and distill them into compact students for embeddings-as-data
deployment.

The 21-million-parameter distilled TESSERA v2-1B-M in aggregate outperforms all
open and proprietary models tested, some of which are orders of magnitude
larger. These students produce Matryoshka representations that are inexpensive
to serve: a 16-dimensional prefix keeps 92% of the full 128-dimensional
performance at 1/8 of the storage. Upon completion of training we plan to
release v2 global embeddings covering 2017-2025.

Together, these results give a concrete, empirically grounded recipe for
scaling pixel-wise EO foundation models: train large encoders, select by
downstream performance, and distil into flexible student models. All code will
be released at https://github.com/ucam-eo/tessera.


DOI: 10.48550/arXiv.2607.03949
Classification: preprint
Venue: arXiv
URL: http://arxiv.org/abs/2607.03949

## Related

- [.plan-26-29: Perfect weather, imperfectly measured, precisely predicted](https://anil.recoil.org/notes/2026w29) (note, 2026-07-19)
- [.plan-26-28: What fun papers piled up while I was out at sea](https://anil.recoil.org/notes/2026w28) (note, 2026-07-12)
- [TESSERA, a pixelwise geospatial foundation model](https://anil.recoil.org/projects/tessera) (project, 2025-01-01)

---
Canonical: https://anil.recoil.org/papers/2026-tessera-v2
Type: paper
Tags: preprint