Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation. Because physics-based climate simulations are computationally expensive, planners typically evaluate fewer than ten manual designs. \gls{qd} algorithms offer a way to systematically illuminate the design space, but they require surrogate models to be practical. In this paper, we replace a slow, regulatory physics simulator with a spatial deep-learning surrogate (U-Net) inside an offline MAP-Elites loop. We systematically compare this spatial approach with a traditional \gls{gp} surrogate across different training-data strategies (quasi-random Sobol sampling vs.\ active \gls{qd} bootstrapping). Our results reveal that scalar \gls{gp} surrogates fail catastrophically when trained on random samples, requiring expensive, actively generated \gls{qd} archives to generalize. In contrast, the spatial inductive bias of the U-Net allows it to learn the underlying physics mapping robustly ($R^2 = 0.996$), completely independent of the training data source. This allows offline \gls{qd} optimization to achieve highly accurate fitness rankings ($ρ= 0.994$) using only a one-time batch of random training samples. The resulting pipeline, deployed in the open-source OpenSKIZZE tool, generates thousands of diverse, climate-evaluated building layouts in under ten minutes.

翻译：优化城市布局以实现气候适应性需在建筑密度与冷空气通风之间取得平衡。由于基于物理的气候模拟计算成本高昂，规划者通常仅评估少于十个手工设计方案。质量-多样化算法提供了一种系统性地探索设计空间的方法，但其实际应用需借助替代模型。本文采用空间深度学习替代模型（U-Net）替代缓慢的监管物理模拟器，并将其嵌入离线MAP-Elites循环框架中。我们系统地比较了该空间方法与传统高斯过程替代模型在不同训练数据策略（拟随机Sobol采样与主动质量-多样化引导采样）下的表现。结果表明，标量高斯过程替代模型在随机样本上训练时完全失效，需依赖昂贵且主动生成的质量-多样化档案库才能实现泛化。相比之下，U-Net的空间归纳偏置使其能够稳健地学习底层物理映射（R²=0.996），且与训练数据来源完全无关。这使得离线质量-多样化优化仅需一次性随机训练样本批次即可获得高度准确的适应度排名（ρ=0.994）。该流程已部署于开源工具OpenSKIZZE中，可在十分钟内生成数千种多样且经气候评估的建筑布局。