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Abstract Geographic Information Systems (GIS)-based technologies are increasingly crucial in the domain of cultural heritage conservation, facilitating the construction of dynamic information management systems and serving as robust platforms for research and display. This review utilizes CiteSpace and Bibliometrix R language to perform a bibliometric analysis of academic literature sourced from the Web of Science (WoS) Core Collection, focusing on the application of GIS in cultural heritage conservation. The analysis covers a broad spectrum of academic articles, identifying research hotspots, patterns of national cooperation, interdisciplinary mobility, knowledge structure, and developmental trends. The findings reveal that this research area is experiencing a phase of steady growth. While three emerging trends have been identified, demonstrating significant theoretical and technical advancements, there remains considerable potential for enhancing in their practical implication within conservation efforts. The study advocates for the integration of digital technologies into the humanities, emphasizing the need for a heritage database equipped with standardized data exchange protocols to support display and analytical functions. This systematic research approach not only illuminates new strategies for the inheritance and innovation in the conservation of cultural heritage, but also paves the way for future explorations in this increasingly vital field.

As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments.

Globally, dockless bike-sharing (DBS) systems are acclaimed for their convenience and seamless integration with public transportation, such as buses and metros. While much research has focused on the connection between the built environment and the metro–DBS integration, the influence of urban road characteristics on DBS and bus integration remains underexplored. This study defined the parking area of DBS around bus stops by a rectangular buffer so as to extract the DBS–bus integration, followed by measuring the access and egress integration using real-time data on dockless bike locations. This indicated that the average trip distance for DBS–bus access and egress integration corresponded to 1028.47 m and 1052.33 m, respectively. A zero-inflated negative binomial (ZINB) regression model assessed how urban roads and other transportation facilities correlate with DBS–bus integration across various scenarios. The findings revealed that certain street patterns strongly correlate with frequent connection hotspots. Furthermore, high-grade roads and ‘dense loops on a stick’ street types may negatively influence DBS–bus integration. The increase in the proportion of three-legged intersections and culs-de-sac in the catchment makes it difficult for bus passengers to transfer by DBS. These insights offer valuable guidance for enhancing feeder services in public transit systems.

Understanding the evolution of rural landscapes in metropolises during rapid urbanization is crucial for formulating policies to protect the rural ecological environment. In this study, remote sensing and geographical information system data, as well as applied landscape index analysis, are used to examine the spatiotemporal evolution of rural landscape patterns in the Beijing-Tianjin region of China, which has experienced rapid urbanization. The relationships between land use/land cover changes and changes in rural landscape patterns are explored. The results revealed significant spatial differences in the rural landscapes in the Beijing-Tianjin region; farmland and forestland were the main types of landscapes, creating a "mountain-field-sea" natural landscape pattern. The conversion of rural landscapes in the Beijing-Tianjin region involved mainly the conversion of farmland to urban areas, with few exchanges between other landscape types. The urban areas in the Beijing-Tianjin region increased by 3% per decade; farmland decreased at the same rate. Additionally, the rural landscape patterns in the Beijing-Tianjin region were dominated by fragmentation, dispersion, and heterogeneity and moved from complex to regular. Water bodies displayed the most fragmented natural landscape; their number of patches increased by 36%, though their network characteristics were maintained. Forestland was the most concentrated natural landscape. In this study, theoretical support and a scientific reference for the optimization of rural landscape patterns and the improvement in rural living environments in rapidly urbanizing areas are provided.

The representational image of the city has attracted people’s long-term attention. Nevertheless, the mechanism of interaction between the image and the built environment (BE) and image studies at the town scale have not been fully explored. In this study, we collected multi-source data from 26 characteristic towns in Tianjin, China. We explored a deep learning approach to recognize social media data, which led to the development of quantifiable town uniqueness image (UI) variables. We studied the influence of the BE on the town UI and the moderating effects of positive emotions on the relationship between the two. The results showed that positive emotions had significantly positive moderating effects on the water system ratio’s effect on UI, but weakened sidewalk density and tourist attraction density. They also inhibited the negative effects of road connectivity but could strengthen the negative effects of the sky view factor and points of interest (POI) mix. The moderating effects on other variables are relatively mediocre. This study helps to reveal the inner mechanism of BE and town image. It is conducive to accurately coordinating the relationship between planning policies and design strategies, optimizing resource allocation, and promoting sustainable town development.

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.