Revision as of 09:51, 12 September 2025

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.

Please follow this link.

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 =Papers=
-==In progress==
+Please follow this [https://docs.google.com/document/d/1bAoatAiM1EpNwTj-RucmzNBcXbZQHf58cvgPuhoowY8/edit?usp=sharing link].
-* 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
-==Published==
 = Presentations =
 * [https://svr-sk818-web.cl.cam.ac.uk/tessera/images/0/0d/PROTEA-short_version.pptx Self-supervised learning for earth observation, short version], (PPTX)  S. Keshav, May 2025
 * [https://svr-sk818-web.cl.cam.ac.uk/tessera/images/b/b3/BTFM_talk_Exeter_v2.pptx Self-supervised learning for earth observation], (PPTX)  S. Keshav, Exeter, April 2025