Resumen
The color of urban streets plays a crucial role in shaping a city?s image, enhancing street appeal, and optimizing the experience of citizens. Nevertheless, the relationship between street color environment and residents? perceptions has rarely been deeply discussed, and most of the existing studies adopt qualitative methods. To accurately and effectively assess the connection between street color environment and residents? emotional perceptions, this paper introduces a quantitative research framework based on multi-source data called ?Color Emotion Perception with K-Means, Adversarial Strategy, SegNet, and SVM (CEP-KASS)?. By combining K-Means unsupervised machine learning and SegNet computer vision techniques, it captures and analyzes visual elements and color data from Baidu Street View Images (BSVI). It then employs a human?machine adversarial scoring model to quantify residents? perceptions of BSVI and uses the support vector machine regression model to predict the final perception scores. Based on these data, a Pearson correlation analysis and visual analysis were conducted on the elements and color in the urban environment. Subsequently, the streets were classified based on perception frequency and perception scores by integrating multi-source data, and areas within the third ring of Xuzhou City were selected for validating the research framework. The results demonstrate that utilizing street-view images and the CEP-KASS framework can quantitatively analyze urban color perception and establish a connection with residents? emotions. In terms of color perception, red, orange, and blue all have a strong positive correlation with the interesting score, whereas black is positively correlated with a sense of safety. Regarding color attributes, low-saturation bright colors result in higher fun perception scores in urban spaces; too low saturation and brightness can affect their attractiveness to residents; brightness has an inverse relationship with the perception of safety, and adjusting brightness inversely can improve the perceived safety experience in certain urban external spaces. The street classification criteria based on perception frequency and perception scores proposed herein can provide references for planners to prioritize color transformation decisions, with a priority on emulating HSHF streets and transforming LSHF streets. When formulating color planning, suggestions for color adjustment can be given based on the correlation study of color with visual elements and perception scores, optimizing urban residents? spatial perception and their emotional experiences. These findings provide robust theoretical support for further enhancing the visual quality of streets and refining urban color planning.