Difference between revisions of "Outputs"
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| − | [https://github.com/ucam-eo/geotessera GeoTessera] Python library to | + | * [https://github.com/ucam-eo/geotessera 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. |
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| + | * [https://github.com/ucam-eo/tessera-interactive-map 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= | =Papers= | ||
Revision as of 10:54, 11 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.
Papers
In progress
Submitted
Published
Presentations
- TESSERA: Remote Sensing Foundation Model for Earth Observation, Z. Feng, J. Knezevic, R. Young, Cambridge, May 2025
- Self-supervised learning for earth observation, short version, S. Keshav, May 2025
- Self-supervised learning for earth observation, S. Keshav, Exeter, April 2025
