ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society

2026-05-19

AI Summary

This study addresses the limited understanding of how building-induced shade affects pedestrian thermal exposure at the urban scale, hindered by the absence of large-scale datasets and systematic evaluation frameworks. To bridge this gap, we present ShadeBench, the first multimodal urban shade benchmark dataset, integrating time-varying shade maps across multiple regions with satellite imagery, building footprints, and 3D meshes to support tasks such as shade generation, segmentation, and 3D reconstruction. We propose a standardized evaluation protocol that unifies remote sensing, 3D modeling, computer vision, and semantic description into a spatiotemporally consistent analytical framework. The release of ShadeBench alongside baseline methods provides a scalable foundation for assessing thermal comfort and informing heat-resilient urban planning.

Abstract

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/.

Problem

• urban shade
• urban heat island
• benchmark dataset
• thermal exposure
• sustainable urban planning

Innovation

• urban shade simulation
• multimodal benchmark dataset
• 3D building reconstruction
• shade segmentation
• heat-resilient urban planning