Multi-Source Geospatial Data for Parking Space Discovery for Hospitals in Densely Urban Areas

Bibliographic Data

ID22031344
AuthorsYimeng Zhang (0009-0001-9602-9531, China University of Mining and Technology), Yirui Wei (China University of Mining and Technology), Ruishuan Zhu (Remote Sensing Solutions (United States)), Yi Liu (0000-0001-7733-3290, China University of Mining and Technology), Yuhao Liu (0000-0002-2442-5690, China University of Mining and Technology), Kunliang Xiao (China University of Mining and Technology), Sheng Zhang (0000-0001-8885-119X, China University of Mining and Technology), Xiran Zhou (0000-0002-2567-0313, China University of Mining and Technology, corresponding author)
Year2026
Volume15
Issue3
Pages117
Publication date2026-03-11
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi15030117
OpenAlexW7134973726
LanguageEN
References cited32

Amid rapid urbanization, the rapid increase in urban vehicles has exacerbated parking scarcity, particularly in areas surrounding hospitals. As the core city of the Huaihai Economic Zone, Xuzhou’s medical institutions serve a broad region spanning 178,000 square kilometers. The pronounced mismatch between parking supply and demand in these areas severely impacts traffic efficiency and public service quality. To address this challenge, this study proposes a data-driven parking resource planning methodology for the identification and planning of informal/shared parking spaces (utilizing underutilized idle spaces) in hospital vicinities, integrating multi-source geospatial data from OpenStreetMap, remote sensing imagery, and field surveys. The methodology involves data preprocessing (e.g., format conversion, building boundary calibration), parking space identification and classification (e.g., buffer zone delineation, vacant land categorization, shape-based division), and layout optimization using a genetic algorithm combined with manual refinement. Applied within a 1 km radius around two hospitals in Xuzhou, the results demonstrate significant improvements in space utilization and provide a scientific basis for temporary parking facility planning. The results provide practical decision support for urban spatial management and temporary parking governance in high-demand public service areas

Geomatics · Geospatial analysis · Parking guidance and information · Reservation · Urban planning · 3D Modeling in Geospatial Applications · Smart Parking Systems Research · Vehicle License Plate Recognition

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