Urban space architectural color is the first feature to be perceived in a complex vision beyond shape, texture and material, and plays an important role in the expression of urban territory, humanity and style. However, because of the difficulty of color measurement, the study of architectural color in street space has been difficult to achieve large-scale and fine development. The measurement of architectural color in urban space has received attention from many disciplines. With the development and promotion of information technology, the maturity of street view big data and deep learning technology has provided ideas for the research of street architectural color measurement. Based on this background, this study explores a highly efficient and large-scale method for determining architectural colors in urban space based on deep learning technology and street view big data, with street space architectural colors as the research object. We conducted empirical research in Jiefang North Road, Tianjin. We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. Based on K-Means clustering model, we identified the colors of the architectural elements in the street view. The accuracy of the building color measurement results was cross-sectionally verified by means of a questionnaire survey. The validation results show that the method is feasible for the study of architectural colors in street space. Finally, the overall coordination, sequence continuity, and primary and secondary hierarchy of architectural colors of Jiefang North Road in Tianjin were analyzed. The results show that the measurement model can realize the intuitive expression of architectural color information, and also can assist designers in the analysis of architectural color in street space with the guidance of color characteristics. The method helps managers, planners and even the general public to summarize the characteristics of color and dig out problems, and is of great significance in the assessment and transformation of the color quality of the street space environment.

Spatial perception is essential for understanding residents’ subjective experiences and well-being. However, effective methods for tracking changes in spatial perception over time and space remain limited. This study proposes a novel approach that leverages historical street view imagery to monitor the evolution of urban spatial perception. Using the central urban area of Shanghai as a case study, we applied machine learning techniques to analyze 67,252 street view images from 2013 and 2019, aiming to quantify the spatiotemporal dynamics of urban perception. The results reveal the following: temporally, the average perception scores in 2019 increased by 4.85% compared to 2013; spatially, for every 1.5 km increase in distance from the city center, perception scores increased by an average of 0.0241; among all sampling points, 65.79% experienced an increase in perception, while 34.21% showed a decrease; and in terms of visual elements, natural features such as trees, vegetation, and roads were positively correlated with perception scores, whereas artificial elements like buildings, the sky, sidewalks, walls, and fences were negatively correlated. The analytical framework developed in this study offers a scalable method for measuring and interpreting changes in urban perception and can be extended to other cities. The findings provide valuable time-sensitive insights for urban planners and policymakers, supporting the development of more livable, efficient, and equitable urban environments.

Rapid socio-economic development and imbalanced ecosystem conservation have heightened the risk of species extinction, reduced urban climate adaptability, and threatened human health and well-being. Constructing ecological green space networks is an effective strategy for maintaining urban ecological security. However, most studies have primarily addressed biodiversity needs, with limited focus on coordinating street spaces in human settlement planning. This study examines the area within Chengdu’s Third Ring Road, employing the following methodologies: (1) constructing the regional ecological network using Morphological Spatial Pattern Analysis (MSPA), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and circuit theory; (2) analyzing the street green view index (GVI) through machine learning semantic segmentation techniques; and (3) identifying key areas for the coordinated development of urban ecological networks and street green spaces using bivariate spatial correlation analysis. The results showed that (1) Chengdu’s Third Ring Road exhibits high ecological landscape fragmentation, with 41 key ecological sources and 94 corridors identified. Ecological pinch points were located near urban rivers and surrounding woodlands, while ecological barrier points were concentrated in areas with dense buildings and complex transportation networks. (2) Higher street GVI values were observed around university campuses, urban parks, and river-adjacent streets, while lower GVI values were found near commercial areas and transportation hubs. (3) To coordinate the construction of ecological networks and street green spaces, the central area of the First Ring Road and the northwestern region of the Second and Third Ring Roads were identified as priority restoration areas, while the northern, western, and southeastern areas of the Second and Third Ring Roads were designated as priority protection areas. This study adopts a multi-scale spatial perspective to identify priority areas for protection and restoration, aiming to coordinate the construction of urban ecological networks and street green spaces and provide new insights for advancing ecological civilization in high-density urban areas.

Traditional villages are vital carriers of cultural heritage and key foundations for rural revitalization and sustainable development, yet rapid urbanization increasingly threatens their survival, making it necessary to clarify their spatial distribution and driving mechanisms to support effective conservation and rational utilization. Yunnan Province, home to 777 nationally recognized traditional villages and the highest number in China, offers a representative context for such analysis. Methodologically, this study uses a 12 km × 12 km geographic grid (3005 cells) rather than administrative units. The count of catalogued traditional villages in each cell is taken as the dependent variable, and nine indicators selected from five dimensions (traffic accessibility, natural topography, climatic conditions, socioeconomic factors, and historical and cultural factors) serve as explanatory variables. Assuming that relationships between villages and their environment are spatially nonstationary and operate at multiple spatial scales, we combine spatial autocorrelation analysis with a multiscale geographically weighted regression (MGWR) model to detect clustering patterns and estimate location-specific coefficients and bandwidths. The results indicate that: (1) traditional villages in Yunnan exhibit significant clustering, with over 60% concentrated in Dali, Baoshan, Honghe, and Lijiang; (2) the spatial pattern follows a “more in the northwest, fewer in the southeast, dense in mountainous areas” distribution, shaped by both natural and socioeconomic factors; (3) natural geographic factors show the strongest associations, with sunshine duration and water availability strongly promoting village presence, while slope exhibits regionally differentiated effects; (4) socioeconomic development and transportation accessibility are generally negatively associated with village distribution, but in tourism-driven areas such as Dali and Lijiang, road improvements have facilitated protection and revitalization; and (5) historical and cultural factors, particularly proximity to nationally protected cultural heritage sites, contribute to spatial clustering and long-term preservation. The MGWR model achieves strong explanatory power (R² = 0.555, adjusted R² = 0.495) and outperforms OLS and standard GWR, confirming its suitability for analyzing the spatial mechanisms of traditional villages. Finally, the study offers targeted recommendations for the conservation and sustainable development of traditional villages in Yunnan.

With accelerating urbanization, Mount Tai is increasingly at risk of homogenization with other scenic spots, losing its landscape aesthetic characteristics as ‘the Most Revered of the Five Sacred Mountains’, which poses a growing threat to the long-term sustainable development of Mount Tai. Accordingly, this study takes texts written by different social groups as the research material and analyses the aesthetic themes and spatial distribution of the elements in the texts to determine the aesthetic characteristics of the Mount Tai landscape from multiple perspectives and long time series. This study complements the research on the Mount Tai landscape, which focuses on specific research material and time. The results of the study can help to raise awareness of the complexity and multiple values of Mount Tai landscapes and have implications for research and practice of other heritage landscapes.