Decoupling Urban Street Attractiveness: An Ensemble Learning Analysis of Color and Visual Element Contributions

Tao Wu, Zeyin Chen, Siying Li, Peixue Xing, Ruhang Wei, Xi Meng, Jingkai Zhao (), Zhiqiang Wu () and Renlu Qiao
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Tao Wu: College of Architecture and Urban Planning, Tongji University, 1239 Siping Road, Shanghai 200092, China
Zeyin Chen: College of Architecture and Urban Planning, Tongji University, 1239 Siping Road, Shanghai 200092, China
Siying Li: School of Architecture, Tsinghua University, 30 Shuangqing Road, Beijing 100084, China
Peixue Xing: School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Ruhang Wei: College of Architecture and Urban Planning, Tongji University, 1239 Siping Road, Shanghai 200092, China
Xi Meng: Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
Jingkai Zhao: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, 1239 Siping Road, Shanghai 200092, China
Zhiqiang Wu: College of Architecture and Urban Planning, Tongji University, 1239 Siping Road, Shanghai 200092, China
Renlu Qiao: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, 1239 Siping Road, Shanghai 200092, China

Land, 2025, vol. 14, issue 5, 1-26

Abstract: Constructing visually appealing public spaces has become an important issue in contemporary urban renewal and design. Existing studies mostly focus on single dimensions (e.g., vegetation ratio), lacking a large-scale integrated analysis of urban color and visual elements. To address this gap, this study employs semantic segmentation and color computation on a massive street-view image dataset encompassing 56 cities worldwide, comparing eight machine learning models in predicting Visual Aesthetic Perception Scores (VAPSs). The results indicate that LightGBM achieves the best overall performance. To unpack this “black-box” prediction, we adopt an interpretable ensemble approach by combining LightGBM with Shapley Additive Explanations (SHAPs). SHAP assigns each feature a quantitative contribution to the model’s output, enabling transparent, post hoc explanations of how individual color metrics and visual elements drive VAPS. Our findings suggest that the vegetation ratio contributes the most to VAPS, but once greening surpasses a certain threshold, a “saturation effect” emerges and can no longer continuously enhance visual appeal. Excessive Sky Visibility Ratio can reduce VAPS. Moderate road visibility may increase spatial layering and vibrancy, whereas overly dense building significantly degrades overall aesthetic quality. While keeping the dominant color focused, moderate color saturation and complexity can increase the attractiveness of street views more effectively than overly uniform color schemes. Our research not only offers a comprehensve quantitative basis for urban visual aesthetics, but also underscores the importance of balancing color composition and visual elements, offering practical recommendations for public space planning, design, and color configuration.

Keywords: urban aesthetics; urban color; street-view visual elements; machine learning; Place Pulse 2.0 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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