Difference between revisions of "Outputs"
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* Z. Feng et al. [https://arxiv.org/abs/2506.20380 TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis], July 2025. | * Z. Feng et al. [https://arxiv.org/abs/2506.20380 TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis], 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'', | + | * 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. |
| − | 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. | * 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. | ||
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Revision as of 13:49, 1 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: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis, 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
- TESSERA overview presentation (PPTX) James Cook University, S. Keshav, September 29, 2025.
- Self-supervised learning for earth observation, (PPTX) S. Keshav, Exeter, April 2025
Demos
Linked here.
Prior work
- Original Barlow Twin paper by Zbontar et al. (2021) for classical still images which inspired the development of a spectral-temporal representation learning
- Master's report (2022) with the first implementation of the Barlow Twins at Cambridge University (link)
- 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
- First paper (2025) demonstrating the effectiveness of spectral-temporal representations for AGB monitoring in tropical countries
