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 incorporates optical reliability modulation to suppress unreliable optical responses, heterogeneous information adaptive aggregation to model high-order spatial-channel interactions between optical and SAR representations, and a unified semantic mapping transformer to organize 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 representative heterogeneous reconstruction-first and end-to-end SAR-optical mapping methods. 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

翻译：光学遥感影像常受云及云阴影污染影响,导致其在近实时土地利用与土地覆盖(LULC)制图中的可靠性受限。尽管合成孔径雷达(SAR)可提供穿透云层的结构信息,但现有SAR-光学融合方法通常预设可靠的光学观测数据,未能充分解决云污染引发的语义不确定性。针对该问题,本文提出CloudLULC-Net——一种端到端异构SAR-光学融合框架,可直接从受云污染的哨兵二号影像与时间相邻的哨兵一号SAR观测数据预测LULC图。该网络通过光学可靠性调制抑制不可靠的光学响应,采用异构信息自适应聚合建模光学与SAR表征间的高阶空间-通道相互作用,并借助统一语义映射Transformer将融合特征组织至面向LULC的隐空间。此外,引入语义锚点引导优化策略以提升中间语义表征的一致性。为支撑该任务,我们构建了CloudLULC-Set——一个包含40,223组经精心筛选的SAR-光学-标签三元组的大规模基准数据集,其像素级LULC标注覆盖多种地理区域与云况条件。实验结果表明,CloudLULC-Net总体精度(OA)达86.60%,F1分数为83.29%,平均交并比(mIoU)为73.51%,性能优于具有代表性的异构重建优先型与端到端SAR-光学制图方法。与现有全球LULC产品的对比及不同云覆盖率下的分析进一步验证了CloudLULC-Net在多云区域目标时相LULC制图中的鲁棒性与实用价值。项目开源地址:https://github.com/RSIIPAC/CloudLULC