KAUST DepartmentComputer Science Program
Visual Computing Center (VCC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/680629
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AbstractAssessing 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.
CitationGilmutdinov, I., Schlögel, I., Hinterleitner, A., Wonka, P., & Wimmer, M. (2022). Assessment of Material Layers in Building Walls Using GeoRadar. Remote Sensing, 14(19), 5038. https://doi.org/10.3390/rs14195038
SponsorsThis research was funded by the Austrian Research Promotion Agency (FFG), project no. 879401 (BIMStocks). We also acknowledge financial support by TU Wien Bibliothek through its Open Access Funding Programme.
Except where otherwise noted, this item's license is described as Archived with thanks to REMOTE SENSING under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/