Breaking Data Silos with Scene-Degradation-Aware Learning for Generalizable Remote Sensing Cloud Removal

Cloud removal models often struggle to generalize across diverse land-surface scenes and cloud degradations. While combining multiple datasets can broaden the range of training conditions, their uneven and redundant distributions limit the benefit of direct data aggregation; meanwhile, the increased diversity introduces substantially different restoration patterns that are difficult to accommodate with a single shared-parameter model. To address this challenge, we approach it from two complementary perspectives. First, Scene-Degradation-Aware Data Distillation (SDA-DD) derives a scene-degradation-aware sampling distribution over the pooled cloudy-clear pairs, encouraging the selected training data to be more evenly distributed across diverse restoration conditions. Second, we develop OmniCR with a Local-Routing Mixture-of-Experts (LR-MoE) architecture that routes different windows to experts with complementary restoration preferences, allowing spatially varying regions within the same image to adopt different restoration behaviors. Soft-Scene Optimization further emphasizes scene-degradation relations with restoration losses, preventing easy-restored conditions from dominating training. Experiments on multiple CR benchmarks demonstrate consistent performance gains across diverse scenes and cloud-degradation conditions.