TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis

TL;DR

TESSERA is a pixel-wise foundation model for Earth surface spectral data that learns robust, label-efficient embeddings from multi-modal satellite time series, enabling accurate downstream tasks with minimal computation.

Contribution

The paper introduces TESSERA, a novel model that learns invariant, efficient embeddings from irregular multi-modal satellite data, improving accuracy and accessibility for Earth observation analysis.

Findings

TESSERA achieves state-of-the-art accuracy on diverse Earth observation tasks.

Embeddings require minimal task-specific training, demonstrating high label efficiency.

Open-sourced code and data facilitate large-scale retrieval and inference.

Abstract

Satellite Earth-observation (EO) time series in the optical and microwave ranges of the electromagnetic spectrum are often irregular due to orbital patterns and cloud obstruction. Compositing addresses these issues but loses information with respect to vegetation phenology, which is critical for many downstream tasks. Instead, we present TESSERA, a pixel-wise foundation model for multi-modal (Sentinel-1/2) EO time series that learns robust, label-efficient embeddings. During model training, TESSERA uses Barlow Twins and sparse random temporal sampling to enforce invariance to the selection of valid observations. We employ two key regularizers: global shuffling to decorrelate spatial neighborhoods and mix-based regulation to improve invariance under extreme sparsity. We find that for diverse classification, segmentation, and regression tasks, TESSERA embeddings deliver state-of-the-art accuracy with high label efficiency, often requiring only a small task head and minimal computation. To democratize access, adhere to FAIR - principles, and simplify use, we release global, annual, 10m, pixel-wise int8 embeddings together with open weights/code and lightweight adaptation heads, thus providing practical tooling for large-scale retrieval and inference at planetary scale. All code and data are available at: https://github.com/ucam-eo/tessera.