Automatic Detection and Mapping of Dolines Using U-Net Model from Orthophoto Images

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dc.authoridkeskin, inan/0000-0003-2977-4352
dc.authoridPolat, Ozlem/0000-0002-9395-4465
dc.contributor.authorPolat, Ali
dc.contributor.authorKeskin, Inan
dc.contributor.authorPolat, Ozlem
dc.date.accessioned2024-09-29T16:08:06Z
dc.date.available2024-09-29T16:08:06Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractA doline is a natural closed depression formed as a result of karstification, and it is the most common landform in karst areas. These depressions damage many living areas and various engineering structures, and this type of collapse event has created natural hazards in terms of human safety, agricultural activities, and the economy. Therefore, it is important to detect dolines and reveal their properties. In this study, a solution that automatically detects dolines is proposed. The proposed model was employed in a region where many dolines are found in the northwestern part of Sivas City, Turkey. A U-Net model with transfer learning techniques was applied for this task. DenseNet121 gave the best results for the segmentation of the dolines via ResNet34, and EfficientNetB3 and DenseNet121 were used with the U-Net model. The Intersection over Union (IoU) and F-score were used as model evaluation metrics. The IoU and F-score of the DenseNet121 model were calculated as 0.78 and 0.87 for the test data, respectively. Dolines were successfully predicted for the selected test area. The results were converted into a georeferenced vector file. The doline inventory maps can be easily and quickly created using this method. The results can be used in geomorphology, susceptibility, and site selection studies. In addition, this method can be used to segment other landforms in earth science studies.en_US
dc.identifier.doi10.3390/ijgi12110456
dc.identifier.issn2220-9964
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85178257869en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/ijgi12110456
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7365
dc.identifier.volume12en_US
dc.identifier.wosWOS:001113887900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofIsprs International Journal of Geo-Informationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdoline segmentationen_US
dc.subjectdeep learningen_US
dc.subjectorthophoto imagesen_US
dc.subjectgeocomputationen_US
dc.titleAutomatic Detection and Mapping of Dolines Using U-Net Model from Orthophoto Imagesen_US
dc.typeArticleen_US

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