Copernicus FM

• Repository: https://github.com/zhu-xlab/Copernicus-FM
• Paper: https://arxiv.org/abs/2503.11849

Citation

@misc{wang2025unifiedcopernicusfoundationmodel,
      title={Towards a Unified Copernicus Foundation Model for Earth Vision}, 
      author={Yi Wang and Zhitong Xiong and Chenying Liu and Adam J. Stewart and Thomas Dujardin and Nikolaos Ioannis Bountos and Angelos Zavras and Franziska Gerken and Ioannis Papoutsis and Laura Leal-Taixé and Xiao Xiang Zhu},
      year={2025},
      eprint={2503.11849},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.11849}, 
}

deprecated/CopernicusFM_ViT_base_varlang_e100_fullmodel.pth

pth · 1,72 GB · SHA-256 e273c5cb365c…00b3 · Hugging Face

--- datasets: - wangyi111/Copernicus-Pretrain - wangyi111/Copernicus-Bench license: cc-by-4.0 pipeline_tag: image-feature-extraction tags: - earth-observation - remote-sensing - sentinel - foundation-model --- # Model Card for Copernicus-FM <!-- Provide a quick summary of what the model is/does. --> [Copernicus-FM](https://github.com/zhu-xlab/Copernicus-FM) is an extension of the [DOFA](https://github.com/zhu-xlab/DOFA) foundation model, able to process any spectral or non-spectral sensor modality using extended dynamic hypernetworks and flexible metadata encoding. The model is pretrained on the Copernicus-Pretrain dataset with masked image modeling and continual distillation. ## Model Description ## Uses ## Related Sources <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/zhu-xlab/Copernicus-FM - **Paper:** https://arxiv.org/abs/2503.11849 ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> ```bibtex @misc{wang2025unifiedcopernicusfoundationmodel, title={Towards a Unified Copernicus Foundation Model for Earth Vision}, author={Yi Wang and Zhitong Xiong and Chenying Liu and Adam J. Stewart and Thomas Dujardin and Nikolaos Ioannis Bountos and Angelos Zavras and Franziska Gerken and Ioannis Papoutsis and Laura Leal-Taixé and Xiao Xiang Zhu}, year={2025}, eprint={2503.11849}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2503.11849}, } ```