Outputs

Software

• GeoTessera Python library for accessing and working with Tessera geospatial foundation model embeddings. GeoTessera provides access to geospatial embeddings from the Tessera foundation model, which processes Sentinel-1 and Sentinel-2 satellite imagery to generate 128-channel representation maps at 10m resolution. These embeddings compress a full year of temporal-spectral features into dense representations optimized for downstream geospatial analysis tasks.
• Interactive Tessera Embedding Classifier This repository contains a Jupyter notebook based tool for interactive, human-in-the-loop classification of geospatial data using the Tessera foundation model embeddings. The tool allows a user to define an area of interest, visualize the high-dimensional embedding data with PCA, and iteratively train a machine learning model by simply clicking on the map to label.

Papers

• Z. Feng et al. TESSERA: Precomputed FAIR Global Pixel Embeddings for Earth Representation and Analysis, Preprint on ArXiv, July 2025.
• C. Atzberger et al. A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects, Remote Sensing of Environment, Volume 327, 2025.
• M. C. Lisaius, A. Blake, S. Keshav and C. Atzberger, Using Barlow Twins to Create Representations From Cloud-Corrupted Remote Sensing Time Series, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 13162-13168, 2024.

Please follow this link for papers in progress and under submission.

Presentations

• 2-slide summary (PPTX) for CRI Flash Talks, S. Keshav, October 7, 2025
• Foundation model overview (PPTX) for Ecology Groups meeting, University of Cambridge, DAB, James Ball, October 6, 2025
• TESSERA overview presentation with a focus on ecological applications (PDF) University of Maryland, Frank Feng, October 1, 2025
• TESSERA overview presentation (PPTX) James Cook University, S. Keshav, September 29, 2025
• TESSERA overview presentation (PDF) University of Cambridge, DAB, Frank Feng, May 20, 2025
• Self-supervised learning for earth observation, (PPTX) S. Keshav, Exeter, April 2025

Demos

Linked here.

Prior work

• First paper (2025) demonstrating the effectiveness of spectral-temporal representations for AGB monitoring in tropical countries
• First paper (2024) demonstrating that spectral-temporal representations from optical EO time series can be learned fully self-supervised using the Barlow Twins loss function - demonstration on crop type classification
• Master's report (2022) with the first implementation of the Barlow Twins at Cambridge University (link)
• Original Barlow Twin paper by Zbontar et al. (2021) for classical still images which inspired the development of a spectral-temporal representation learning