This paper describes in detail the development of a ground-penetrating radar (GPR) model for the acquisition, processing and visualisation of underground utility infrastructure (UUI) in a controlled environment. The initiative was to simulate a subsurface urban environment through the construction of regional road, local road and pedestrian pavement in real urban field/testing pools (RUTPs). The RUTPs represented a controlled environment in which the most commonly used utilities were installed. The accuracy of the proposed kinematic GPR-TPS (terrestrial positioning system) model was analysed using all the available data about the materials, whilst taking into account the thickness of the pavement as well as the materials, dimensions and 3D position of the UUI as given reference values. To determine the reference 3D position of the UUI, a terrestrial geodetic surveying method based on the established positional and height geodetic network was used. In the first phase of the model, the geodetic network was used as a starting point for determining the 3D position of the GPR antenna with the efficient kinematic GPR surveying setup using a GPR and self-tracking (robotic) TPS. In the second phase, GPR-TPS system latency was quantified by matching radargram pairs with a set of fidelity measures based on a correlation coefficient and mean squared error. This was followed by the most important phase, where, by combining sets of “standard” processing routines of GPR signals with the support of advanced algorithms for signal processing, UUI were interpreted and visualised semi-automatically. As demonstrated by the results, the proposed GPR model with a kinematic GPR-TPS surveying setup for data acquisition is capable of achieving an accuracy of less than ten centimetres.


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    Title :

    Kinematic GPR-TPS model for infrastructure asset identification with high 3D georeference accuracy developed in a real urban test field


    Contributors:

    Publication date :

    2021-10-19


    Remarks:

    Remote sensing, vol. 11, no. 12, art 1457, 2019. ; ISSN: 2072-4292


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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