pith. machine review for the scientific record. sign in

arxiv: 2506.20380 · v7 · submitted 2025-06-25 · 💻 cs.LG

Recognition: unknown

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

Andrew Blake, Anil Madhavapeddy, Clement Atzberger, David A. Coomes, James Ball, Jovana Knezevic, Madeline C. Lisaius, Markus Immitzer, Robin Young, Sadiq Jaffer, Silja Sormunen, Srinivasan Keshav, Toby Jackson, Zhengpeng Feng

Authors on Pith no claims yet
classification 💻 cs.LG
keywords tesseraembeddingscodeglobalinvariancemodeloftenpixel-wise
0
0 comments X
read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Agentic AI for Remote Sensing: Technical Challenges and Research Directions

    cs.CV 2026-04 unverdicted novelty 6.0

    Agentic AI faces structural challenges in remote sensing due to geospatial data properties and workflow constraints, requiring EO-native agents built around structured state, tool-aware reasoning, and validity-aware e...

  2. Agentic AI for Remote Sensing: Technical Challenges and Research Directions

    cs.CV 2026-04 unverdicted novelty 5.0

    Agentic AI for remote sensing requires new designs centered on structured geospatial state, tool-aware reasoning, verifier-guided execution, and physical validity rather than generic extensions.

  3. Structure-Semantic Decoupled Modulation of Global Geospatial Embeddings for High-Resolution Remote Sensing Mapping

    cs.CV 2026-04 unverdicted novelty 5.0

    SSDM decouples global geospatial embeddings into structural modulation and semantic injection pathways to improve accuracy and consistency in high-resolution remote sensing land cover mapping.

  4. Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data

    cs.CV 2026-04 unverdicted novelty 5.0

    LIANet encodes multi-temporal Earth observation data into a coordinate-based neural field that supports label-only fine-tuning for downstream tasks without access to raw imagery.