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.

Wastewater treatment plants (WWTPs) play a vital role in controlling wastewater discharge and promoting recycling. Accurate WWTP identification and spatial analysis are crucial for environmental protection, urban planning, and sustainable development. However, the diverse shapes and scales of WWTPs and their key facilities pose challenges for traditional detection methods. This study employs a Multi-Attention Network (MANet) for WWTP extraction, integrating channel and spatial feature attention. Additionally, a Global-Local Feature Modeling Network (GLFMN) is introduced to segment key facilities, specifically sedimentation and secondary sedimentation tanks. The approach is applied to Beijing, utilizing geographic data such as WWTP locations, treatment capacities, and surrounding residential and water distributions. Results indicate that MANet achieves 80.1% accuracy with a 90.4% recall rate, while GLFMN significantly improves the extraction of key facilities compared to traditional methods. The spatial analysis reveals WWTP distribution characteristics, offering insights into treatment capacity and geographic influences. These findings contribute to emission regulation, water quality supervision, and enterprise management of WWTPs in Beijing. This research provides a valuable reference for optimizing wastewater treatment infrastructure and supports decision-making in environmental governance and sustainable urban development.