EaSe-CR: Evidence-Aware Multimodal Representation Learning for Cloud Reconstruction

Clouds obscure critical information in optical satellite imagery, making reliable reconstruction fundamentally dependent on how complementary observations are interpreted. Existing CR (cloud-removal/reconstruction) methods primarily fuse optical and synthetic aperture radar (SAR) observations without explicitly reasoning about whether the available evidence is reliable, degraded or mutually in- consistent. We introduce EaSe-CR (Evidence-aware Sensing experts for Cloud Reconstruction), a reconstruction model that jointly learns modality reliability, cross-modal evidence interaction, disagreement and latent uncertainty, allowing the reconstruction process to adapt to the quality and consistency of available evidence. Our analysis show that the learnt representation responds systematically to changes in evidence quality and cross-modal consistency, while external evaluation demonstrates that EaSe-CR retains an advantage over fixed-fusion baselines under zero-shot, geographically diverse conditions spanning Kerala, Rajasthan and Ladakh