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| + | __NOTOC__ | ||
| + | =Software= | ||
| + | * [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. | ||
| + | * [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= | ||
| − | + | * Z. Feng et al. [https://arxiv.org/abs/2506.20380 TESSERA: Precomputed FAIR Global Pixel Embeddings for Earth Representation and Analysis], ''Preprint on ArXiv'', July 2025. | |
| + | * C. Atzberger et al. [https://doi.org/10.1016/j.rse.2025.114774 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, [https://ieeexplore.ieee.org/abstract/document/10592304 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 [https://docs.google.com/document/d/1bAoatAiM1EpNwTj-RucmzNBcXbZQHf58cvgPuhoowY8/edit?usp=sharing link] for papers in progress and under submission. |
= Presentations = | = Presentations = | ||
| − | * [https:// | + | |
| − | * [https://svr-sk818-web.cl.cam.ac.uk/tessera/ | + | * [https://svr-sk818-web.cl.cam.ac.uk/tessera/images/a/a3/CRI-2slide.pptx 2-slide summary (PPTX)] for CRI Flash Talks, S. Keshav, October 7, 2025 |
| − | * [https://svr-sk818-web.cl.cam.ac.uk/tessera/images/b/b3/BTFM_talk_Exeter_v2.pptx Self-supervised learning for earth observation], S. Keshav, Exeter, April 2025 | + | * [https://svr-sk818-web.cl.cam.ac.uk/tessera/index.php/File:251006_LabMeeting_BallJGC.pptx Foundation model overview] (PPTX) for Ecology Groups meeting, University of Cambridge, DAB, James Ball, October 6, 2025 |
| + | * [https://svr-sk818-web.cl.cam.ac.uk/tessera/index.php/File:Tessera_talk_maryland_1st_Oct.pdf TESSERA overview presentation with a focus on ecological applications] (PDF) University of Maryland, Frank Feng, October 1, 2025 | ||
| + | * [https://svr-sk818-web.cl.cam.ac.uk/tessera/index.php/File:JCU-tesserav2.pptx TESSERA overview presentation] (PPTX) James Cook University, S. Keshav, September 29, 2025 | ||
| + | * [https://svr-sk818-web.cl.cam.ac.uk/tessera/index.php/File:TESSERA_Talk_DAB_5_20.pdf TESSERA overview presentation] (PDF) University of Cambridge, DAB, Frank Feng, May 20, 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 | ||
| + | |||
| + | =Demos= | ||
| + | Linked [https://docs.google.com/document/d/1p5ec7wKRWlEb4ddDSFvUTfDbFT5LxaIMeKv3sUFO9ps/edit?usp=sharing here]. | ||
| + | |||
| + | = Prior work = | ||
| + | * First [https://www.sciencedirect.com/science/article/pii/S0034425725001786 paper] (2025) demonstrating the effectiveness of spectral-temporal representations for AGB monitoring in tropical countries | ||
| + | * First [https://ieeexplore.ieee.org/abstract/document/10592304 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 [https://proceedings.mlr.press/v139/zbontar21a Barlow Twin paper] by Zbontar et al. (2021) for classical still images which inspired the development of a spectral-temporal representation learning | ||
Latest revision as of 17:53, 6 October 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
- 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
