Automatic classification of underground utilities in Urban Areas: A novel method combining ground penetrating radar and image processing

被引:0
作者
Pasternak, Klaudia [1 ]
Fryskowska-Skibniewska, Anna [1 ]
机构
[1] Mil Univ Technol WAT, Fac Civil Engn & Geodesy, Dept Imagery Intelligence, S Kaliskiego 2, PL-00908 Warsaw, Poland
关键词
classification of hyperbolas; Ground penetrating radar; land surveying; the Ring-Projection method; underground infrastructure; urban areas; GPR; EXTRACTION; OBJECTS;
D O I
10.24425/ace.2024.149851
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Precise determination of the location of underground utility networks is crucial in the field of civil engineering for: the planning and management of space with densely urbanized areas, infrastructure modernization, during construction and building renovations. In this way, damage to underground utilities can be avoided, damage risks to neighbouring buildings can be minimized, and human and material losses can be prevented. It is important to determine not only the location but also the type of underground utility network. Information about location and network types improves the process of land use design and supports the sustainable development of urban areas, especially in the context of construction works in build-up areas and areas planned for development. The authors were inspired to conduct research on this subject by the development of a methodology for classifying network types based on images obtained in a non-invasive way using a Leica DS2000 ground penetrating radar. The authors have proposed a new classification algorithm based on the geometrical properties of hyperboles that represent underground utility networks. Another aim of the research was to automate the classification process, which may support the user in selecting the type of network in images that are sometimes highly noise -laden. The developed algorithm shortens the time required for image interpretation and the selection of underground objects, which is particularly important for inexperienced operators. The classification results revealed that the average effectiveness of the classification of network types ranged from 42% to 70%, depending on the type of infrastructure.
引用
收藏
页码:59 / 77
页数:19
相关论文
共 39 条
  • [1] A proposal for the reconstruction of a historical masonry building constructed in Ottoman Era (Istanbul)
    Akcay, Cemil
    Solt, Aysen
    Korkmaz, Nail Mahir
    Sayin, Baris
    [J]. JOURNAL OF BUILDING ENGINEERING, 2020, 32
  • [2] Processing Radargrams to Obtain Resistivity Sections
    Arevalo-Lomas, Lucia
    Biosca, Barbara
    Paredes-Palacios, David
    Diaz-Curiel, Jesus
    [J]. REMOTE SENSING, 2022, 14 (11)
  • [3] Birkenfeld S., 2010, WORLD AUT C KOB JAP, P1
  • [4] IMPLEMENTATION OF GPR AND TLS DATA FOR THE ASSESSMENT OF THE BRIDGE SLAB GEOMETRY AND REINFORCEMENT
    Cafiso, S.
    Di Graziano, A.
    Goulias, D.
    Mangiameli, M.
    Mussumeci, G.
    [J]. ARCHIVES OF CIVIL ENGINEERING, 2020, 66 (01) : 297 - 308
  • [5] Automatic key frame extraction in continuous videos from construction monitoring by using color, texture, and gradient features
    Chen, Ling
    Wang, Yuhong
    [J]. AUTOMATION IN CONSTRUCTION, 2017, 81 : 355 - 368
  • [6] Czechowicz A., 2010, Neural networks in aerial images matching process
  • [7] Neural detection of pipe signatures in ground penetrating radar images
    Gamba, P
    Lossani, S
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2000, 38 (02): : 790 - 797
  • [8] Gocal J., 2006, Geodesy and Cartography, V55, P47
  • [9] Guan-Chen P., 2011, A tutorial of wavelet for pattern recognition
  • [10] Non-stationary random noise removal in ground-penetrating radar images by using self-guided filtering
    He, Xingkun
    Yan, Hao
    Wang, Can
    Zheng, Rongyao
    Li, Yujin
    Li, Xiwen
    [J]. DIGITAL SIGNAL PROCESSING, 2022, 129