The deep learning method applied to the detection and mapping of stone deterioration in open-air sanctuaries of the Hittite period in Anatolia

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dc.authoridince, ismail/0000-0002-6692-7584
dc.contributor.authorHatir, Ergun
dc.contributor.authorKorkanc, Mustafa
dc.contributor.authorSchachner, Andreas
dc.contributor.authorInce, Ismail
dc.date.accessioned2024-11-07T13:34:28Z
dc.date.available2024-11-07T13:34:28Z
dc.date.issued2021
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe detection of deterioration in archeological heritage sites is a very time-consuming task that requires expertise. Furthermore, vision-based approaches can cause errors, considering the complex types of de-terioration that develop in different scales and forms in monuments. This problem can be solved effec-tively owing to computer vision algorithms, commonly used in different areas nowadays. This study aims to develop a model that automatically detects and maps deteriorations (biological colonization, contour scaling, crack, higher plant, impact damage, microkarst, missing part) and restoration interventions using the Mask R-CNN algorithm, which has recently come to the fore with its feature of recognizing small and large-sized objects. To this end, a total of 2460 images of Yazilikaya monuments in the Hattusa archeo-logical site, which is on the UNESCO heritage list, were gathered. In the training phase of the proposed method, it was trained in model 1 to distinguish deposit deterioration commonly observed on the surface of monuments from other anomalies. Other anomalies trained were model 2. In this phase of the models, the average precision values with high accuracy rates ranging from 89.624% to 100% were obtained for the deterioration classes. The developed algorithms were tested on 4 different rock reliefs in Yazilikaya, which were not used in the training phase. In addition, an image of the Eflatunpinar water monument, which is on the UNESCO tentative list, was used to test the model's universality. According to the test results, it was determined that the models could be successfully applied to obtain maps of deterioration and restoration interventions in monuments in different regions. (c) 2021 Elsevier Masson SAS. All rights reserved.
dc.identifier.doi10.1016/j.culher.2021.07.004
dc.identifier.endpage49
dc.identifier.issn1296-2074
dc.identifier.issn1778-3674
dc.identifier.scopus2-s2.0-85111321583
dc.identifier.scopusqualityQ1
dc.identifier.startpage37
dc.identifier.urihttps://doi.org/10.1016/j.culher.2021.07.004
dc.identifier.urihttps://hdl.handle.net/11480/16004
dc.identifier.volume51
dc.identifier.wosWOS:000709736800005
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier France-Editions Scientifiques Medicales Elsevier
dc.relation.ispartofJournal of Cultural Heritage
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectHittite
dc.subjectHattusa
dc.subjectStone deterioration
dc.subjectDeterioration map
dc.subjectMask R-CNN
dc.titleThe deep learning method applied to the detection and mapping of stone deterioration in open-air sanctuaries of the Hittite period in Anatolia
dc.typeArticle

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