Difference between revisions of "Outputs"

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=Papers=
 
=Papers=
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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.
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Please follow this [https://docs.google.com/document/d/1bAoatAiM1EpNwTj-RucmzNBcXbZQHf58cvgPuhoowY8/edit?usp=sharing link] for papers in progress and under submission.
 
Please follow this [https://docs.google.com/document/d/1bAoatAiM1EpNwTj-RucmzNBcXbZQHf58cvgPuhoowY8/edit?usp=sharing link] for papers in progress and under submission.
  

Revision as of 09:53, 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.

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