Ildar Gilmutdinov, Ingrid Schlögel, Alois Hinterleitner, Peter Wonka, Michael Wimmer
Assessment of Material Layers in Building Walls Using GeoRadar
Remote Sensing, 14(19), October 2022. [paper]

Information

Abstract

Assessing the structure of a building with non-invasive methods is an important problem. One of the possible approaches is to use GeoRadar to examine wall structures by analyzing the data obtained from the scans. However, so far, the obtained data have to be assessed manually, relying on the experience of the user in interpreting GPR radargrams. We propose a data-driven approach to evaluate the material composition of a wall from its GPR radargrams. In order to generate training data, we use gprMax to model the scanning process. Using simulation data, we use a convolutional neural network to predict the thicknesses and dielectric properties of walls per layer. We evaluate the generalization abilities of the trained model on the data collected from real buildings.

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BibTeX

@article{gilmutdinov-2022-aomlbwug,
  title =      "Assessment of Material Layers in Building Walls Using
               GeoRadar",
  author =     "Ildar Gilmutdinov and Ingrid Schl\"{o}gel and Alois
               Hinterleitner and Peter Wonka and Michael Wimmer",
  year =       "2022",
  abstract =   "Assessing the structure of a building with non-invasive
               methods is an important problem. One of the possible
               approaches is to use GeoRadar to examine wall structures by
               analyzing the data obtained from the scans. However, so far,
               the obtained data have to be assessed manually, relying on
               the experience of the user in interpreting GPR radargrams.
               We propose a data-driven approach to evaluate the material
               composition of a wall from its GPR radargrams. In order to
               generate training data, we use gprMax to model the scanning
               process. Using simulation data, we use a convolutional
               neural network to predict the thicknesses and dielectric
               properties of walls per layer. We evaluate the
               generalization abilities of the trained model on the data
               collected from real buildings.",
  month =      oct,
  doi =        "10.3390/rs14195038",
  issn =       "2072-4292",
  journal =    "Remote Sensing",
  number =     "19",
  volume =     "14",
  event =      "Radar Techniques for Structures Characterization and
               Monitoring",
  pages =      "12",
  publisher =  "MDPI",
  pages =      "5038--",
  keywords =   "deep learning, ground-penetrating radar, non-destructive
               evaluation",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2022/gilmutdinov-2022-aomlbwug/",
}