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

In progress

• Below-ground Fungal Biodiversity Can be Monitored Using Self-Supervised Learning Satellite Features, Robin Young et al

Submitted

• TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis, Science Advances
• Towards Understanding User Requirements for Human-Centered Geospatial Foundation Models, Robin Young, ACM SIGSPATIAL GeoHCC workshop.
• Maddy's paper on small fields, submitted to ISPRS Open Journal on Photogrammetry and Remote Sensing
• PROPL

Presentations

• Self-supervised learning for earth observation, short version, (PPTX) S. Keshav, May 2025
• Self-supervised learning for earth observation, (PPTX) S. Keshav, Exeter, April 2025