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.

China's metro system is developing rapidly. Walking is the most frequently adopted mode to connect to the metro, the attributes of the pedestrian-built environment around the stations directly influence people's willingness to use the metro. However, few studies have paid attention to the comprehensive assessments of the built environment in the metro catchment area. Thus, this paper attempts to construct a walkability evaluation model that combines subjective and objective perspectives. We collected field data of the built environment factors affecting on walkability in the 800 m buffer zone of eight case metro stations in Dalian city, China. We also collected on-site interviews from 867 passengers to evaluate the walkability. A machine learning-based approach was developed to calculate the weights of walkability variables, followed by constructing a Score-Effectiveness framework to identify the built environment factors in the metro catchment area that need to be improved. The study found that the shading facilities, obstacle barriers, and resting seats around pedestrian walkways are the most efficient and imbalanced variables recognized by the crowd. The convenience of overpasses and underpasses are additional efficient imbalance-type variables for leisure and commuting populations, respectively. This indicates that the current level of construction of the above five built environment factors is relatively low, but the construction has a significant impact on the degree of friendliness in supporting pedestrian walkability. In this paper, improvement measures are proposed in a targeted manner in order to achieve the effect of effectively improving the current level of metro catchment area's walkability. The results of the study can provide references to provide strategies for precise pedestrian planning in the metro catchment area, leading to a pedestrian environment with high walking quality.

As cities worldwide strive to promote healthy and sustainable non-motorized transport, the equity of dockless bike-sharing has become a central issue in urban transport planning. This study investigates the relationship between human-scale urban environmental perceptions and the equity of bike-sharing usage within 15-minute cycling catchments of metro stations. Using Shenzhen, China, as a case study, we integrated bike-share trip records from August 2021 (around 43 million trips), population grid data, and Baidu Street View images analyzed with deep learning models. The study first quantified the spatial inequality of bike-sharing usage within each metro catchment area using a per capita trip Gini coefficient. Subsequently, we assessed the correlation between these equity metrics and human-scale urban qualities quantified from street-level imagery. The findings reveal significant intra-catchment usage disparities, with some central urban station areas showing relatively equitable bike-sharing distribution (Gini as low as 0.37), while others, particularly on the urban fringe, exhibit highly inequitable patterns (Gini as high as 0.93). Spearman correlation analysis showed that catchments perceived as “livelier” and more “interesting” had significantly lower Gini coefficients, whereas other perceptual factors such as safety, beauty and wealth showed no significant linear relationship with equity. A Random Forest model further indicated that “liveliness” and “lack of boredom” are the strongest predictors of usage equity, highlighting the critical role of vibrant street environments in promoting equitable access. These findings bridge the fields of transportation equity and urban governance, suggesting that improving the human-scale environment around transit hubs, thereby making streets more engaging, safe, and pleasant, could foster more inclusive and equitable use of bike-sharing.

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.