{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T23:43:55Z","timestamp":1777419835943,"version":"3.51.4"},"reference-count":22,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2022R1I1A1A01069664"],"award-info":[{"award-number":["2022R1I1A1A01069664"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm\/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.<\/jats:p>","DOI":"10.3390\/s23198021","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T05:32:45Z","timestamp":1695360765000},"page":"8021","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Construction of Asbestos Slate Deep-Learning Training-Data Model Based on Drone Images"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6936-0675","authenticated-orcid":false,"given":"Seung-Chan","family":"Baek","sequence":"first","affiliation":[{"name":"Department of Architecture, Kyungil University, Gyeongsan 38428, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwang-Hyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Architecture, Kyungil University, Gyeongsan 38428, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5665-9902","authenticated-orcid":false,"given":"In-Ho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Kunsan National University, Kunsan 54150, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3666-2431","authenticated-orcid":false,"given":"Dong-Min","family":"Seo","sequence":"additional","affiliation":[{"name":"School of Architecture, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kiyong","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Big Data, Chungbuk National University, Cheongju 28644, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"ref_1","first-page":"27","article-title":"Asbestos Management Plan According to the Investigation on the Actual Conditions of Asbestos in Public Buildings","volume":"20","author":"Jang","year":"2014","journal-title":"Korean Soc. 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