Data-Driven Urban Color Governance for Digital City Planning: A Machine Learning-Assisted Framework Using Street View Images in Jiading District, Shanghai

Jie Xu, Zhongnan Ye, Di Wang, Shasha Huang, Yang Liu, Yu Xiang

Urban color plays a fundamental role in shaping the visual character and cultural identity of cities. Yet in many contexts, current practices remain fragmented, with color analysis often disconnected from planning implementation and governance. To address this issue, this study proposes a decision-support framework and a method for urban color evaluation and planning that integrates street view imagery, machine learning algorithms, and a parameter-based decision-support system. Using 430,000 street view images of Jiading District, Shanghai, we developed a computational model to systematically map building color characteristics in terms of hue, saturation, and brightness at both building and neighborhood scales. A multi-dimensional criteria framework encompassing the macro-environment, building characteristics, and micro-context is developed to guide automatic color scheme generation and evaluation for both existing and new buildings. The findings extract dominant color features and reveal spatial clustering patterns across Jiading District. The platform evaluates color schemes for new developments and generates color schemes for existing buildings, thereby linking urban color analysis with planning recommendations. This study presents a digital decision-support tool for urban color governance that integrates SVI, semantic segmentation, and rule-based reasoning. It shows how large-scale visual data can be organized and translated into structured references for planning practice, offering a more systematic and measurable support tool for urban color assessment.