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

arXiv: https://arxiv.org/abs/2605.20510 · HTML: https://mlsanity.com/paper/2605.20510/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 13 of 13)

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- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?](https://mlsanity.com/paper/1904.03189/) (2019)
- [Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice](https://mlsanity.com/paper/1911.01685/) (2019)
- [Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing](https://mlsanity.com/paper/2510.19808/) (2025)
- [ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models](https://mlsanity.com/paper/2309.01111/) (2023)
- [R-LPIPS: An Adversarially Robust Perceptual Similarity Metric](https://mlsanity.com/paper/2307.15157/) (2023)
- [Generative AI in Transportation Planning: A Survey](https://mlsanity.com/paper/2503.07158/) (2025)
- [GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications](https://mlsanity.com/paper/2205.12224/) (2022)
- [DeepShade: Enable Shade Simulation by Text-conditioned Image Generation](https://mlsanity.com/paper/2507.12103/) (2025)
- [Shaded Route Planning Using Active Segmentation and Identification of Satellite Images](https://mlsanity.com/paper/2407.13689/) (2024)

## Abstract (arXiv)

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/.