With rapid urbanization, the development of Urban Underground Space (UUS) has become essential to addressing various urban challenges. However, the accelerated expansion of UUS has also introduced problems such as duplicated infrastructure, functional deficiencies, and underutilized spaces. Fundamentally, these issues result from imbalances between the supply and demand for UUS, a phenomenon particularly pronounced in the central areas of major cities. Therefore, employing scientific methods to accurately identify and quantify these gaps is crucial. Leveraging recent advances in remote sensing and point-of-interest (POI) data, this study constructs a multi-source data-driven framework for assessing UUS supply–demand relationships, applied using a grid-based analysis to the central urban area of Nanjing. The results indicate that both the highest supply capacity and demand intensity occur in Xinjiekou Street in Nanjing’s Old City. Most high and medium–high supply and demand zones are concentrated in the Old City. Areas with prominent supply–demand conflicts are identified and classified into five types using the Jenks natural breaks method, further categorized into three groups based on their spatial characteristics, with tailored development strategies proposed accordingly. The proposed evaluation framework provides a robust scientific approach for analyzing UUS supply–demand relationships, offering significant theoretical and practical value for refined urban governance in large cities with extensive data availability.

Housing vacancy rate is a key indicator for evaluating urban sustainable development. Due to rapid urbanization, population outflow and insufficient industrial support, the housing vacancy problem is particularly prominent in China’s underdeveloped regions. However, the lack of official data and the limitations of traditional survey methods restrict in-depth research. This study proposes a vacancy rate estimation method based on Baidu Street View residential exterior images and deep learning technology. Taking Nanning, Guangxi as a case study, an automatic discrimination model for residential vacancy status is constructed by identifying visual clues such as window occlusion, balcony debris accumulation, and facade maintenance status. The study first uses Baidu Street View API to collect images of residential communities in Nanning. After manual annotation and field verification, a labeled dataset is constructed. A pre-trained deep learning model (ResNet50) is applied to estimate the vacancy rate of the community after fine-tuning with labeled street view images of Nanning’s residential communities. GIS spatial analysis is combined to reveal the spatial distribution pattern and influencing factors of the vacancy rate. The results show that street view images can effectively capture vacancy characteristics that are difficult to identify with traditional remote sensing and indirect indicators, providing a refined data source and method innovation for housing vacancy research in underdeveloped regions. The study further found that the residential vacancy rate in Nanning showed significant spatial differentiation, and the vacancy driving mechanism in the old urban area and the emerging area was significantly different. This study expands the application boundaries of computer vision in urban research and fills the research gap on vacancy issues in underdeveloped areas. Its results can provide a scientific basis for the government to optimize housing planning, developers to make rational investments, and residents to make housing purchase decisions, thus helping to improve urban sustainable development and governance capabilities.

Against the background of the rapid transformation of traditional economies and societies and continuous global climate change, how to ensure the long-term stability of the coastal ecological environment has become a key issue to be studied. In this paper, we take the 20 km buffer zone extending inland from the South African coastal zone as the study area. By constructing a vegetation vulnerability evaluation system, the current and future scenarios are compared in depth based on the base period (2010–2020), the near term (2030–2059), and the long term (2070–2099) with the help of GIS spatial analysis, the Moran index, and other methods. The results show that there are obvious spatial differences in vegetation vulnerability in the South African coastal zone. The extremely vulnerable areas of vegetation are mostly distributed on the west coast of South Africa, and some areas have obvious high–high aggregation patterns. The transfer of SSP1-2.6 scenarios in the near term is relatively stable, and the vegetation vulnerability level rebounds significantly in the long term; the vulnerability level of SSP2-4.5 scenarios has increased in both the near term and the long term, indicating that the risk of vegetation vulnerability has increased; while the SSP5-8.5 scenario has a significant deterioration trend in the long term, and the risk of vegetation vulnerability shifting to a high vulnerability level has increased significantly. Land use type has a significant impact on the response of vegetation vulnerability to SSP prediction. In the process of transformation from the base period to the long term, the proportion of vegetation vulnerability shifting to extremely vulnerable and severely vulnerable levels is notably high for both cultivated land and forest land—particularly under high-emission scenarios, driven by agricultural intensification for cultivated land and climate stress for forest land. This paper deeply explores the spatiotemporal evolution law and driving mechanism of vegetation vulnerability in the South African coastal zone under different shared socioeconomic pathway (SSP) scenarios, providing decision support for better development and protection of the South African coastal zone in the future.

Based on panel data from 102 resource-based cities in China spanning 2013 to 2023, this study employs fixed effects and mediating effect models to empirically examine the impact of urban renewal on eco-environmental resilience. The main findings are as follows. First, urban renewal development significantly enhances eco-environmental resilience, and this conclusion remains robust after various robustness checks. Second, heterogeneity analysis reveals that the impact exhibits a distinctive regional pattern: “the central region shows the strongest promotion effect, the eastern region shows a relatively weaker promotion effect, and the western region exhibits a pronounced inhibitory effect.” Furthermore, the promoting effect is more pronounced in regions with lower energy consumption pressure, higher environmental governance investment, and larger city sizes. Third, urban renewal can enhance eco-environmental resilience through two pathways: promoting industrial structure optimization and upgrading, and strengthening environmental regulation. Fourth, the urbanization level plays a significant moderating role in the process by which urban renewal enhances the eco-environmental resilience of resource-based cities.