COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data
Earth observation applications increasingly rely on data from multiple sensors, including optical, radar, elevation, and land-cover. Relationships between modalities are fundamental for data integration but are inherently non-injective: identical conditioning information can correspond to multiple physically plausible observations, and should be parametrised as conditional distributions. Deterministic models, by contrast, collapse toward conditional means and fail to represent the uncertainty and variability required for tasks such as data completion and cross-sensor translation. We introduce COP-GEN, a multimodal latent diffusion transformer that models the joint distribution of heteroge...
| Authors | Miguel Espinosa, Eva Gmelich Meijling, Valerio Marsocci et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 March 2026 |
| Last updated at source | 11 May 2026 |
Models and datasets referencing this paper
- Major-TOM/COP-GEN-BenchmarkDataset
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