Urban Architectural Color Evaluation: A Cognitive Framework Combining Machine Learning and Human Perception

Xu Li, Jianan Qin, Yixiang Long

Architectural color significantly impacts the quality of built environments and is closely related to the physical and mental health of residents. Previous studies have conducted numerous valuable explorations in this field; however, the challenge of quantitatively measuring the characteristics of architectural colors in depth and examining the complex relationship between these colors and human perception remains an unresolved issue. To this end, this study builds upon recent advancements in data technology and emotion analysis to develop a comprehensive cognitive framework for urban architectural color evaluation. It combines machine learning techniques and perception scales, utilizing both objective and subjective data. By acquiring and recognizing numerous street-view images of the Changsha Central District, we quantitatively examined the hue, saturation, value, color complexity index and color harmony index of urban architectural colors and investigated the complex relationships between human perception and architectural colors through large-scale participant ratings and correlation analyses. The results show that the architectural colors of the study area are warm, with low saturation and moderate value. Most areas exhibit a high color complexity index, whereas the overall color harmony score is low. Human-perception evaluations indicate that people are generally satisfied with the urban architectural colors of the Changsha Central District. For further optimization, the saturation and color harmony scores need to be enhanced. This study provides a comprehensive evaluation of urban architectural color quality, visualizing the complex relationship between urban architectural color and human perception. It offers new perspectives for improving the built environments and supporting sustainable development, with practical application value.