Urban areas are increasingly vulnerable to thermal extremes driven by rapid urbanization and climate change. Traditionally, thermal extremes have been monitored using Earth-observing satellites and numerical modeling frameworks. For example, land surface temperature derived from Landsat or Sentinel imagery is commonly used to characterize surface heating patterns. These approaches operate as forward models, translating radiative observations or modeled boundary conditions into estimates of surface thermal states. While forward models can predict land surface temperature from vegetation and urban form, the inverse problem of determining spatial vegetation configurations that achieve a desired regional temperature shift remains largely unexplored. This task is inherently underdetermined, as multiple spatial vegetation patterns can yield similar aggregated temperature responses. Conventional regression and deterministic neural networks fail to capture this ambiguity and often produce averaged solutions, particularly under data-scarce conditions. We propose a conflated inverse modeling framework that combines a predictive forward model with a diffusion-based generative inverse model to produce diverse, physically plausible image-based vegetation patterns conditioned on specific temperature goals. Our framework maintains control over thermal outcomes while enabling diverse spatial vegetation configurations, even when such combinations are absent from training data. Altogether, this work introduces a controllable inverse modeling approach for urban climate adaptation that accounts for the inherent diversity of the problem. Code is available at the GitHub repository.

翻译：城市地区日益面临由快速城市化和气候变化驱动的热极端事件的脆弱性。传统上，热极端事件通过地球观测卫星和数值建模框架进行监测。例如，源自Landsat或Sentinel影像的地表温度常用于表征地表加热模式。这些方法作为正向模型运行，将辐射观测或建模边界条件转化为地表热状态的估计。虽然正向模型能够根据植被和城市形态预测地表温度，但确定实现期望区域温度变化的植被空间配置的逆向问题在很大程度上仍未被探索。这一任务本质上是欠定的，因为多种空间植被格局可能产生相似的聚合温度响应。传统的回归和确定性神经网络未能捕捉这种模糊性，并常产生平均化解决方案，尤其在数据稀缺条件下。我们提出一种融合逆向建模框架，将预测性正向模型与基于扩散的生成式逆向模型相结合，以生成基于特定温度目标的多样化、物理上合理的影像植被格局。我们的框架保持对热结果的控制，同时实现多样化的空间植被配置，即使在训练数据中缺乏此类组合时也有效。总之，这项工作引入了一种适用于城市气候适应的可控逆向建模方法，该方法考虑了问题的固有多样性。代码可在GitHub仓库获取。