{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T02:43:05Z","timestamp":1772851385690,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T00:00:00Z","timestamp":1653350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008383","name":"Federal Ministry of Transport and Digital Infrastructure","doi-asserted-by":"publisher","award":["19H18011C"],"award-info":[{"award-number":["19H18011C"]}],"id":[{"id":"10.13039\/100008383","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Access Fund of the Leibniz University Hannover","award":["19H18011C"],"award-info":[{"award-number":["19H18011C"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense the entire below-water infrastructure by hand. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose scanning the above and below-water port structure with a multi-sensor system, and by a fully automated process to classify the point cloud obtained into damaged and undamaged zones. We make use of simulated training data to test our approach because not enough training data with corresponding class labels are available yet. Accordingly, we build a rasterised height field of a point cloud of a sheet pile wall by subtracting a computer-aided design model. The latter is propagated through a convolutional neural network, which detects anomalies. We make use of two methods: the VGG19 deep neural network and local outlier factors. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection, which can analyse the whole structure instead of the sample-wise manual method with divers. We were able to achieve valuable results for our application. The accuracy of the proposed method is 98.8% following a desired recall of 95%. The proposed strategy is also applicable to other infrastructure objects, such as bridges and high-rise buildings.<\/jats:p>","DOI":"10.3390\/rs14112518","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:14:14Z","timestamp":1653437654000},"page":"2518","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Using Machine-Learning for the Damage Detection of Harbour Structures"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7424-2270","authenticated-orcid":false,"given":"Frederic","family":"Hake","sequence":"first","affiliation":[{"name":"Geodetic Institute, Leibniz Universit\u00e4t Hannover, Nienburger Str. 1, 30167 Hannover, Germany"}]},{"given":"Leonard","family":"G\u00f6ttert","sequence":"additional","affiliation":[{"name":"Geodetic Institute, Leibniz Universit\u00e4t Hannover, Nienburger Str. 1, 30167 Hannover, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9110-7345","authenticated-orcid":false,"given":"Ingo","family":"Neumann","sequence":"additional","affiliation":[{"name":"Geodetic Institute, Leibniz Universit\u00e4t Hannover, Nienburger Str. 1, 30167 Hannover, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4480-1067","authenticated-orcid":false,"given":"Hamza","family":"Alkhatib","sequence":"additional","affiliation":[{"name":"Geodetic Institute, Leibniz Universit\u00e4t Hannover, Nienburger Str. 1, 30167 Hannover, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"ref_1","first-page":"26","article-title":"3D HydroMapper: Automatisierte 3D-Bauwerksaufnahme und Schadens-erkennung unter Wasser f\u00fcr die Bauwerksinspektion und das Building Information Modelling","volume":"113","author":"Hesse","year":"2019","journal-title":"Hydrogr. 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