构建城市遮阳模拟基准数据集，助力可持续城市热环境研究
ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society
- 基于多模态数据构建时空动态遮阳地图
- 涵盖全球多样城市场景，支持遮阳生成与分割等任务
- 适合城市气候、智能规划领域研究人员使用
城市热岛效应加剧导致热暴露问题日益严峻。建筑引起的精细遮阳模式显著影响行人热感受和户外活动规划。然而，由于缺乏大规模数据集和系统评估框架，大范围精确建模城市遮阳仍具挑战。为此，我们提出ShadeBench，一个全面的城市遮阳理解数据集与基准。ShadeBench包含地理多样化的城市场景，配有随时间变化的模拟遮阳图、文本描述，以及对齐的卫星影像、建筑骨架和3D建筑网格。基于此多模态数据集，ShadeBench支持遮阳生成、遮阳分割、3D建筑重建等下游任务，并建立了标准化评估协议与基线方法。通过实现可扩展的细粒度遮阳分析，ShadeBench为数据驱动的城市气候研究提供基础，支持未来热韧性城市规划与决策研究。代码与数据集公开于 https://darl-genai.github.io/shadebench/。
原文摘要 · Abstract (English)
Urban heat exposure is becoming an increasingly critical challenge due to the intensifying urban heat island effect. Fine-grained shade patterns, especially those induced by urban buildings, strongly influence pedestrians' thermal exposure and outdoor activity planning. However, accurately modeling and analyzing urban shade at scale remains difficult because of the lack of large-scale datasets and systematic evaluation frameworks. To address this challenge, we present ShadeBench, a comprehensive dataset and benchmark for urban shade understanding. ShadeBench contains geographically diverse urban scenes with temporally varying simulated shade maps and textual descriptions, together with aligned satellite imagery, building skeleton representations, and 3D building meshes. Built upon this multimodal dataset, ShadeBench supports a range of downstream tasks, including shade generation, shade segmentation, and 3D building reconstruction. We further establish standardized evaluation protocols and baseline methods for these tasks. By enabling scalable and fine-grained shade analysis, ShadeBench provides a foundation for data-driven urban climate research and supports future studies in heat-resilient urban planning and decision-making. The code and dataset are publicly available at https://darl-genai.github.io/shadebench/.
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