TESSERA v2: Scaling Pixel-wise Earth Foundation Models
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 within a fixed pixel-wise Barlow Twins family, each evaluated on 15 diverse 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, whic...
| Authors | Zhengpeng Feng, Sadiq Jaffer, Ira Shokar et al. |
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| Primary category | cs.CV |
| Published | 4 July 2026 |
| Last updated at source | 6 August 2026 |
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