Heterogeneous SAR–optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset

Optical remote sensing imagery is frequently degraded by cloud and cloud-shadow contamination, which limits its reliability for near-real-time land use and land cover (LULC) mapping. Although synthetic aperture radar (SAR) can provide cloud-penetrating structural information, existing SAR–optical fusion methods often assume reliable optical observations and insufficiently address the semantic uncertainty introduced by cloud contamination. To address this issue, we propose CloudLULC-Net, an end-to-end heterogeneous SAR–optical fusion framework that directly predicts LULC maps from cloud-contaminated Sentinel-2 imagery and temporally adjacent Sentinel-1 SAR observations. The proposed network first employs optical reliability modulation to adaptively regulate unreliable optical responses, then uses heterogeneous information adaptive aggregation to progressively model cross-modal spatial and channel interactions between optical and SAR representations, and finally applies a unified semantic mapping transformer to organize the fused features in a LULC-oriented latent space. A semantic anchor-guided optimization strategy is further introduced to improve the consistency of intermediate semantic representations. To support this task, we construct CloudLULC-Set, a large-scale benchmark dataset containing 40,223 curated SAR–optical–label triplets with pixel-level LULC annotations across diverse geographic regions and cloud conditions. Experimental results show that CloudLULC-Net achieves an OA of 86.60%, an F1-score of 83.29%, and an mIoU of 73.51%, outperforming the second-best method by 0.94, 1.25, and 0.78 percentage points, respectively. Product-level reference comparisons with existing global LULC products and analyses under different cloud-cover levels further demonstrate the robustness and practical value of CloudLULC-Net for target-date LULC mapping in cloud-prone regions. The project is publicly available at: https://github.com/RSIIPAC/CloudLULC .