Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/117398
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorLai, WWLen_US
dc.creatorWong, PTWen_US
dc.creatorKwan, RLYen_US
dc.creatorLi, Yen_US
dc.creatorZhou, Yen_US
dc.creatorHe, Wen_US
dc.creatorHan, Yen_US
dc.creatorChen, Wen_US
dc.date.accessioned2026-02-23T03:39:48Z-
dc.date.available2026-02-23T03:39:48Z-
dc.identifier.issn0886-7798en_US
dc.identifier.urihttp://hdl.handle.net/10397/117398-
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.titleCity-scale subsurface utilities mapping by multi-channel ground penetrating radar (MCGPR) with mobile mapping system (MMS)en_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume170en_US
dc.identifier.doi10.1016/j.tust.2025.107325en_US
dcterms.abstractThe complex and tangled networks of spider-web utilities present a persistent engineering challenge in many cities, emphasizing the need for improved maintenance to minimize societal disruptions and risks. Their locations, sizes, and conditions are unknown to many cities’ municipal authorities. Given their large amount and varieties, stock check and its constant update are necessary but hard to achieve because surveys are costly, inefficient at city-scale, and interfered with by road traffic. In recent years, such a stock check by MCGPR can be achieved with high efficiency, although ‘how?’ is still uncertain. This paper develops two hybrid solutions to answer this ‘how’: (1) a multi-channel ground penetrating radar (MCGPR) workflow from its city navigation to processing to GIS, and (2) matching with quality levels in the international standard (BS PAS 128–2022). The first solution is divided into above-ground and underground. Above-ground is seamless city trajectories based on firstly building a query image-basemap database by mobile mapping system (MMS) and then matching with subsequent on-board panoramic cameras for generating trajectories during MCGPR survey. Sky-view must always be restricted in a city where seamless positioning by GNSS-RTK is challenging. The underground part is a hybrid GPR-GIS workflow integrating the desktop planning, field survey, and data processing. The second solution contains two workflows: data classification of MCGPR, three data qualities based on BS PAS 128–2022, and attribute categorization of four scenarios, which are good & fair match, uncharted utilities, and survey Unreliable (SU)/Survey Not Successful (SNS)/Patch Utilities based on an integrated utility database by the Government. After the MCGPR survey of about 23,000 sq.m. rural area and 20,000 sq.m. urban areas in Hong Kong, we concluded that 20% and 34% existing records of point features (manholes and valves) and 50% and 10% existing records of polyline features (pipes) are ‘good or fair match’, respectively. This paper demonstrates how a stock check of busy city underground utilities can be achieved in a scientific, effective, and logical approach.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationTunnelling and underground space technology, Apr. 2026, v. 170, 107325en_US
dcterms.isPartOfTunnelling and underground space technologyen_US
dcterms.issued2026-04-
dc.identifier.scopus2-s2.0-105024242294-
dc.identifier.eissn1878-4364en_US
dc.identifier.artn107325en_US
dc.description.validate202602 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.SubFormIDG000970/2026-01-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis research was supported by the Innovation and Technology Fund (ITF) under Grant No. ITP/010/25LP (“City-scale Road GPR’s Early Warning and Diagnosis of Invisible Void”). This work was also supported by the General Research Fund (GRF) of the Research Grants Council (RGC) of HKSARG, under Grant Nos. PolyU/15239525 (“Large-scale Mapping of Watermains Leakage by Ground Penetrating Radar and Instantaneous Frequency Analysis”) and PolyU/15218723 (“Uncertainties Modelling on Depth Measurements of Buried Circular Objects in Ground Penetrating Radar Survey”). Internal funding was generously provided by the Research Institute for Land and Space, The Hong Kong Polytechnic University (“City-scale Road GPR’s Early Warning and Diagnosis of Invisible Void: A change-detection and deep learning approach”) and by the Smart City Research Institute, The Hong Kong Polytechnic University (“City-scale Road GPR’s Early Warning and Diagnosis of Invisible Void: Building Road's digital elevation model”). The authors sincerely appreciate the support of all funding bodies.en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2028-04-30en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2028-04-30
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