ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917514478354432 |
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| author | Da, Longchao Shivakoti, Mithun Liu, Xiangrui Kutralingam, T Pranav Yang, Yezhou Wei, Hua |
| author_facet | Da, Longchao Shivakoti, Mithun Liu, Xiangrui Kutralingam, T Pranav Yang, Yezhou Wei, Hua |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20510 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society Da, Longchao Shivakoti, Mithun Liu, Xiangrui Kutralingam, T Pranav Yang, Yezhou Wei, Hua Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society 68T45, 68U35 I.2.10; I.4.8 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/. |
| title | ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society 68T45, 68U35 I.2.10; I.4.8 |
| url | https://arxiv.org/abs/2605.20510 |