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In remote sensing, multi-modal data from various sensors capturing the same scene offers rich opportunities, but learning a unified representation across these modalities remains a significant challenge. Traditional methods have often been limited to single or dual-modality approaches. In this paper, we introduce COP-GEN-Beta, a generative diffusion model trained on optical, radar, and elevation data from the Major TOM dataset. What sets COP-GEN-Beta apart is its ability to map any subset of modalities to any other, enabling zero-shot modality translation after training. This is achieved through a sequence-based diffusion transformer, where each modality is controlled by its own timestep embedding. We extensively evaluate COP-GEN-Beta on thumbnail images from the Major TOM dataset, demonstrating its effectiveness in generating high-quality samples. Qualitative and quantitative evaluations validate the model's performance, highlighting its potential as a powerful pre-trained model for future remote sensing tasks.
1@inproceedings{espinosa2025copgenbeta,
2 title={COP-GEN-Beta: Unified Generative Modelling of COPernicus Imagery Thumbnails},
3 author={Espinosa, Miguel and Marsocci, Valerio and Jia, Yuru and Crowley, Elliot J. and Czerkawski, Mikolaj},
4 booktitle={CVPRW},
5 year={2025}
6}