Difference between revisions of "Outputs"
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=Demos= | =Demos= | ||
Linked [https://docs.google.com/document/d/1p5ec7wKRWlEb4ddDSFvUTfDbFT5LxaIMeKv3sUFO9ps/edit?usp=sharing here]. | Linked [https://docs.google.com/document/d/1p5ec7wKRWlEb4ddDSFvUTfDbFT5LxaIMeKv3sUFO9ps/edit?usp=sharing here]. | ||
| + | |||
| + | = Preceeding work = | ||
| + | * Original Barlow Twin paper by Zbontar et al. (2021) for classical still images which inspired the development of a spectral-temporal representation learning (https://proceedings.mlr.press/v139/zbontar21a) | ||
| + | * Master thesis (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 (https://ieeexplore.ieee.org/abstract/document/10592304) | ||
| + | * First paper (2025) demonstrating the effectiveness of spectral-temporal representations for AGB monitoring in tropical countries (https://www.sciencedirect.com/science/article/pii/S0034425725001786) | ||
Revision as of 20:47, 29 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
Z. Feng et al. TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis, July 2025.
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.
Preceeding work
- Original Barlow Twin paper by Zbontar et al. (2021) for classical still images which inspired the development of a spectral-temporal representation learning (https://proceedings.mlr.press/v139/zbontar21a)
- Master thesis (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 (https://ieeexplore.ieee.org/abstract/document/10592304)
- First paper (2025) demonstrating the effectiveness of spectral-temporal representations for AGB monitoring in tropical countries (https://www.sciencedirect.com/science/article/pii/S0034425725001786)
