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.

Urban vitality is a critical driver of sustainable urban development, significantly contributing to the enhancement of human well-being. A thorough and multidimensional comprehension of urban vitality is essential for shaping future urban planning and policy-making. This study, focused on Chengdu, proposes a framework for assessing various dimensions of UV through the distribution of milk tea and coffee shops. Using random forest and multi-scale geographically weighted regression models, this study investigates the factors influencing urban vitality from both mathematical thresholds and spatial heterogeneity, and develops spatial maps of future vitality to inform targeted urban strategies. The results show that (1) the milk tea index is effective in capturing population vitality, while the coffee index is more closely associated with economic vitality and urban renewal; (2) office buildings (13.46%) and commercial complexes (13.70%) have the most significant impact on both economic and population vitality, while the importance of transportation factors has notably decreased; (3) the influence of these factors demonstrates spatial heterogeneity and nonlinear relationships, with subway station density of 0.5–0.8 stations per kilometer being optimal for stimulating both types of vitality. The minimum threshold for economic vitality in a given unit is a housing price exceeding 6000 RMB/m²; (4) the future vitality map suggests that urban planners should pay greater attention to non-central districts with high development potential. Moreover, spontaneous social interactions and consumer behaviors stimulated by various shops are critical components of urban vitality. In designing the physical environment and urban spatial forms, special attention should be given to enhancing the attractiveness of physical spaces and their capacity to accommodate social interaction.

Rapid urbanization is universally acknowledged to degrade ecosystem services, posing significant threats to human well-being. However, the effects of urban shrinkage, a global phenomenon and a counterpart to urbanization, on ecosystem services (ESs) remain unclear. This study focuses on China’s Rust Belt during the period from 2000 to 2020, constructing a comprehensive analytical framework based on long-term remote sensing data to reveal the temporal and spatial patterns of ESs and their associations with cities experiencing varying degrees of shrinkage. It employs a random forest (RF) model and a Shapley additive explanation (SHAP) model to measure and visualize the significance and thresholds of socioeconomic factors influencing changes in ESs. Our findings highlight the following: (1) Since 2010, the three provinces of Northeast China (TPNC) have begun to shrink comprehensively, with the degree of shrinkage intensifying over time. Resource-based cities have all experienced contraction. (2) Regional urban shrinkage has been found to enhance the overall provision capacity of ESs, with the most significant improvements in cities undergoing continuous shrinkage. (3) The impact of the same socioeconomic drivers varies across cities with different levels of shrinkage; increasing green-space ratios and investing more in public welfare have been identified as effective measures to enhance ESs. (4) Threshold analysis indicates that the stability of the tertiary sector’s proportion is critically important for enhancing ESs in cities undergoing intermittent shrinkage. An increase of 10% to 15% in this sector can allow continuously shrinking cities to balance urban development with ecological improvements. This research highlights the positive aspects of urban shrinkage, demonstrating its ability to enhance the provision capacity of ESs. It offers new insights into the protection and management of regional ecosystems and the urban transformation of the three eastern provinces.

With the acceleration of urbanisation and the increased utilisation of underground space, providing a comfortable and healthy environment in public underground areas has emerged as a significant research topic. This study constructs a comprehensive decision-making framework for underground space environments by integrating human perception evaluations with physical environmental parameters. Using Shanghai Wujiaochang as a case study, field data collection and questionnaire surveys were conducted to evaluate key factors such as temperature (22.63 °C–26.39 °C), wind speed (0.26 m/s–0.67 m/s), and sound levels (59.68 dB–61.21 dB) for commercial-oriented spaces, and 63.15 dB–75.45 dB for transport-oriented spaces) to users’ perceived experiences. The appropriate ranges for key parameters were identified through single-indicator fitted regression analysis and the XGBoost machine-learning model, revealing the relationship between environmental parameters and human perception. The results indicated significant differences in user needs across various functional spaces, with commercial-oriented areas emphasising environmental attractiveness and comfort, while transport-oriented spaces prioritised access efficiency and safety. This study provided quantitative design benchmarks for underground spaces’ dynamic regulation and sustainable management, proposing a precise and adaptive environmental decision-making framework that combines physical parameters with user-perception feedback.