{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:17:35Z","timestamp":1760231855029,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T00:00:00Z","timestamp":1665273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004955","name":"Austrian Research Promotion Agency (FFG)","doi-asserted-by":"publisher","award":["879401"],"award-info":[{"award-number":["879401"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]},{"name":"TU Wien Bibliothek","award":["879401"],"award-info":[{"award-number":["879401"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs14195038","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"5038","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Assessment of Material Layers in Building Walls Using GeoRadar"],"prefix":"10.3390","volume":"14","author":[{"given":"Ildar","family":"Gilmutdinov","sequence":"first","affiliation":[{"name":"Institute of Visual Computing & Human-Centered Technology, TU Wien, 1040 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0796-158X","authenticated-orcid":false,"given":"Ingrid","family":"Schl\u00f6gel","sequence":"additional","affiliation":[{"name":"ZAMG (Zentralanstalt f\u00fcr Meteorologie und Geodynamik), 1190 Vienna, Austria"}]},{"given":"Alois","family":"Hinterleitner","sequence":"additional","affiliation":[{"name":"ZAMG (Zentralanstalt f\u00fcr Meteorologie und Geodynamik), 1190 Vienna, Austria"}]},{"given":"Peter","family":"Wonka","sequence":"additional","affiliation":[{"name":"Computer, Electrical and Mathematical Science and Engineering Division, KAUST (King Abdullah University of Science and Technology), Thuwal 23955, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9370-2663","authenticated-orcid":false,"given":"Michael","family":"Wimmer","sequence":"additional","affiliation":[{"name":"Institute of Visual Computing & Human-Centered Technology, TU Wien, 1040 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.jappgeo.2013.04.010","article-title":"Ground Penetrating Radar (GPR) attribute analysis for archaeological prospection","volume":"97","author":"Zhao","year":"2013","journal-title":"J. Appl. Geophys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1190\/tle38070526.1","article-title":"Applications of supervised deep learning for seismic interpretation and inversion","volume":"38","author":"Zheng","year":"2019","journal-title":"Lead. Edge"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Morris, I., Abdel-Jaber, H., and Glisic, B. (2019). Quantitative Attribute Analyses with Ground Penetrating Radar for Infrastructure Assessments and Structural Health Monitoring. Sensors, 19.","DOI":"10.3390\/s19071637"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"025017","DOI":"10.1088\/1742-2132\/10\/2\/025017","article-title":"A new combined wavelet methodology: Implementation to GPR and ERT data obtained in the Montagnole experiment","volume":"10","author":"Alperovich","year":"2013","journal-title":"J. Geophys. 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