Public service facilities are the cornerstone of urban development and further expansion, and their spatial distribution fairness is closely related to the quality of life of urban residents. Existing research tends to focus on coverage analysis of a single city or a single type of public service facility, lacking a macro perspective at a medium-to-large scale and consideration of residents’ public service needs. To improve the monitoring of urban public service facility coverage and supply–demand patterns, this paper adopts an urban diagnostic perspective, using 14 megacities from nine urban agglomerations in China as the study area. By integrating spatial and temporal social sensing big data, including road networks, population, and points of interest (POI) data, and employing spatial analysis methods including coverage rate calculation, supply–demand matching efficiency, spatial heterogeneity, and sp{atial stability analysis, this study reveals the spatial distribution patterns of various facilities within cities, as well as the heterogeneity, balance, and equity of supply–demand matching efficiency between different cities. The results show that the spatial distribution of public service facilities in different cities generally tends to concentrate in the central areas, although there are some variations due to local topographical influences. The coverage rate of transportation and education facilities is relatively high, while that of healthcare facilities is generally lower. This study provides information support for urban planning and the optimization of public service facility allocation, contributing to the achievement of sustainable urban development through the comprehensive analysis and comparison of 14 megacities.

In the automated modeling generated by oblique photography, various terrains cannot be physically distinguished individually within the triangulated irregular network (TIN). To utilize the data representing individual features, such as a single building, a process of building monomer construction is required to identify and extract these distinct parts. This approach aids subsequent analyses by focusing on specific entities, mitigating interference from complex scenes. A deep convolutional neural network is constructed, combining U-Net and ResNeXt architectures. The network takes as input both digital orthophoto map (DOM) and oblique photography data, effectively extracting the polygonal footprints of buildings. Extraction accuracy among different algorithms is compared, with results indicating that the ResNeXt-based network achieves the highest intersection over union (IOU) for building segmentation, reaching 0.8255. The proposed “dynamic virtual monomer” technique binds the extracted vector footprints dynamically to the original oblique photography surface through rendering. This enables the selective representation and querying of individual buildings. Empirical evidence demonstrates the effectiveness of this technique in interactive queries and spatial analysis. The high level of automation and excellent accuracy of this method can further advance the application of oblique photography data in 3D urban modeling and geographic information system (GIS) analysis.

Rapid urban population aging and increasing land constraints pose significant challenges for improving both service coverage and spatial equity in elderly care facility planning. This study develops an integrated optimization framework that simultaneously addresses accessibility, efficiency, and equity in the spatial allocation of elderly care facilities. First, an improved Gaussian Two-Step Floating Catchment Area (G2SFCA) method is employed to evaluate accessibility patterns across multiple facility types under both walking and driving scenarios. Second, resource allocation equity is quantified using Lorenz curves and spatial Gini coefficients to identify mismatches between elderly care supply and population demand. Building upon these analyses, a fairness-oriented maximum covering location model—termed the Equity Maximum Covering Location Problem (EMCLP)—is formulated and further transformed into a Markov Decision Process. A deep reinforcement learning-based algorithm is subsequently designed to solve the EMCLP under complex spatial constraints. Experiment results demonstrate that the proposed approach achieves improved computational efficiency while maintaining robust solution quality, and effectively enhances service provision in underserved areas through differentiated functional allocation strategies.

The urban fringe, a critical transitional zone between urban and rural areas, exhibits the most intense land-use conflicts and the most dynamic spatial restructuring. Its fragmentation not only undermines the efficiency of spatial land use but also threatens ecosystem stability, thereby posing a challenge to sustainable urban development. This study aims to examine the characteristics and future trends of spatial fragmentation within the urban fringe of Xi’an. Using multi-source datasets—including nighttime light imagery, land use data, and building vector data—we delineated the urban fringe boundary based on a light gradient threshold method. By combining landscape metrics with spatial overlay analysis, we assessed land use changes and spatial fragmentation patterns from 2000 to 2020 and projected land use conditions for 2030, 2040, and 2050. The results reveal that between 2000 and 2020, cultivated land became increasingly fragmented due to the expansion of construction land, while forest areas continued to expand under ecological restoration policies. Impervious surfaces exhibited infill aggregation. Rapid increases in nighttime light intensity were strongly correlated with heightened fragmentation (R² = 0.65, p < 0.01), confirming that urban expansion is the dominant driver of fragmentation. Projections indicate that the proportion of cultivated land will decline to 0.51, approaching a “critical warning threshold”.