用扩散模型填补云层遮挡的城市地温数据，效果优于传统方法。
UrbanDIFF: A Denoising Diffusion Model for Spatial Gap Filling of Urban Land Surface Temperature Under Dense Cloud Cover
- 基于扩散模型，结合城市结构信息进行纯空间重建。
- 在85%云覆盖率下仍保持SSIM 0.89、RMSE 1.2K的高精度。
- 适合需要连续城市热岛监测的研究者使用。
卫星反演的地表温度（LST）产品对城市热岛监测至关重要，但云层遮挡常导致观测缺失，影响连续分析。现有重建方法多依赖多时相或多源数据融合，但在持续云覆盖下难以获取辅助数据。纯空间填补方法虽可替代，但传统统计方法在大范围或连续缺失区域表现差，深度学习模型随缺失比例增加而性能骤降。近期去噪扩散图像修复模型在高缺失率下表现出更强鲁棒性，为该问题提供新思路。本文提出UrbanDIFF，一种仅依赖空间信息的去噪扩散模型，用于重建云遮挡下的城市地温图像。模型以建筑用地数据和数字高程模型为静态结构先验，并在推理阶段通过监督像素引导精修确保与无云像素的一致性。模型基于NASA MODIS Terra LST数据，在美国七个主要都市区（2002–2025年）上训练与评估。合成云掩码实验显示，当云覆盖率达20%至85%时，UrbanDIFF始终优于插值基线，尤其在密集云覆盖下表现突出：85%云覆盖时，达到SSIM 0.89、RMSE 1.2 K、R² 0.84，且随云密度增加性能下降更缓慢。
原文摘要 · Abstract (English)
Satellite-derived Land Surface Temperature (LST) products are central to surface urban heat island (SUHI) monitoring due to their consistent grid-based coverage over large metropolitan regions. However, cloud contamination frequently obscures LST observations, limiting their usability for continuous SUHI analysis. Most existing LST reconstruction methods rely on multitemporal information or multisensor data fusion, requiring auxiliary observations that may be unavailable or unreliable under persistent cloud cover. Purely spatial gap-filling approaches offer an alternative, but traditional statistical methods degrade under large or spatially contiguous gaps, while many deep learning based spatial models deteriorate rapidly with increasing missingness. Recent advances in denoising diffusion based image inpainting models have demonstrated improved robustness under high missingness, motivating their adoption for spatial LST reconstruction. In this work, we introduce UrbanDIFF, a purely spatial denoising diffusion model for reconstructing cloud contaminated urban LST imagery. The model is conditioned on static urban structure information, including built-up surface data and a digital elevation model, and enforces strict consistency with revealed cloud free pixels through a supervised pixel guided refinement step during inference. UrbanDIFF is trained and evaluated using NASA MODIS Terra LST data from seven major United States metropolitan areas spanning 2002 to 2025. Experiments using synthetic cloud masks with 20 to 85 percent coverage show that UrbanDIFF consistently outperforms an interpolation baseline, particularly under dense cloud occlusion, achieving SSIM of 0.89, RMSE of 1.2 K, and R2 of 0.84 at 85 percent cloud coverage, while exhibiting slower performance degradation as cloud density increases.
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