Optical remote sensing imagery is indispensable for Earth observation, yet persistent cloud occlusion limits its downstream utility. Most cloud removal (CR) methods are optimized for low-level fidelity and can over-smooth textures and boundaries that are critical for analysis-ready data (ARD), leading to a mismatch between visually plausible restoration and semantic utility. To bridge this gap, we propose TDP-CR, a task-driven multimodal framework that jointly performs cloud removal and land-cover segmentation. Central to our approach is a Prompt-Guided Fusion (PGF) mechanism, which utilizes a learnable degradation prompt to encode cloud thickness and spatial uncertainty. By combining global channel context with local prompt-conditioned spatial bias, PGF adaptively integrates Synthetic Aperture Radar (SAR) information only where optical data is corrupted. We further introduce a parameter-efficient two-phase training strategy that decouples reconstruction and semantic representation learning. Experiments on the LuojiaSET-OSFCR dataset demonstrate the superiority of our framework: TDP-CR surpasses heavy state-of-the-art baselines by 0.18 dB in PSNR while using only 15\% of the parameters, and achieves a 1.4\% improvement in mIoU consistently against multi-task competitors, effectively delivering analysis-ready data.

翻译：光学遥感影像对地球观测至关重要，但持续的云层遮挡限制了其下游应用。大多数云去除（CR）方法优化目标是低级保真度，可能过度平滑对分析就绪数据（ARD）至关重要的纹理和边界，导致视觉上看似合理的恢复与语义实用性之间存在不匹配。为弥合这一差距，我们提出TDP-CR，一种任务驱动的多模态框架，可同时执行云去除和土地覆盖分割。我们的核心是提示引导融合（PGF）机制，它利用可学习的退化提示编码云厚度和空间不确定性。通过结合全局通道上下文与局部提示条件空间偏置，PGF仅在光学数据受损处自适应整合合成孔径雷达（SAR）信息。我们进一步引入参数高效的两阶段训练策略，将重建和语义表示学习解耦。在LuojiaSET-OSFCR数据集上的实验证明了我们框架的优越性：TDP-CR在仅使用15%参数的情况下，PSNR超越最先进的重基线模型0.18 dB，并在多任务对比中持续提升mIoU 1.4%，有效生成分析就绪数据。