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Chesapeakeiw/tessera-v1.1-mpc-encoder
tessera-v1.1-mpc-encoder is a machine learning model from Chesapeakeiw. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc0-1.0.
This is an unofficial mirror of the Tessera v1.1 QAT encoder-only checkpoint, originally distributed via Google Drive by the Tessera team at the University of Cambridge. Mirrored here to make it auto-downloadable via…
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From the Hugging Face model README
This is an unofficial mirror of the Tessera v1.1 QAT encoder-only
checkpoint, originally distributed via Google Drive by the Tessera team
at the University of Cambridge. Mirrored here to make it auto-downloadable
via huggingface_hub (Google Drive has no equivalent to hf_hub_download),
for use with torchgeo-bench.
The original authors have stated they plan to publish an official HuggingFace mirror once the Drive links stabilize — once that exists, prefer it over this one.
v1.1 branch, checkpoint table under "v1.1 QAT Model Weight (latest, recommended)"tessera_v1_1_mpc_encoder.ptTessera is a per-pixel self-supervised foundation model producing 128-dimensional embeddings from Sentinel-1/Sentinel-2 time series (10m resolution). This checkpoint is the encoder-only inference graph (S2 + S1 transformer backbones
If you use this checkpoint, please cite the original paper:
@misc{feng2025tesseratemporalembeddingssurface,
title={TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis},
author={Zhengpeng Feng and Clement Atzberger and Sadiq Jaffer and Jovana Knezevic and Silja Sormunen and Robin Young and Madeline C Lisaius and Markus Immitzer and David A. Coomes and Anil Madhavapeddy and Andrew Blake and Srinivasan Keshav},
year={2025},
eprint={2506.20380},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2506.20380},
}