Detection of road extraction from satellite images with deep learning method

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dc.authorid0000-0002-0181-3658en_US
dc.authorid0000-0001-8461-8702en_US
dc.contributor.authorAslan, Emrah
dc.contributor.authorÖzüpak, Yıldırım
dc.date.accessioned2025-02-20T10:43:07Z
dc.date.available2025-02-20T10:43:07Z
dc.date.issued2025en_US
dc.departmentDicle Üniversitesi, Silvan Meslek Yüksek Okulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractRoad extraction from satellite data is of great importance in various fields such as climate change, urban planning, forestry and sustainable development. In addition, fast and accurate road detection plays a critical role in disaster management and smart city applications, especially in emergency situations. In this context, U-net architecture provides an effective solution for tasks such as semantic segmentation and urban planning support. In this work, Edge U-net, a different adaptation of the U-net architecture, is used to map roads and streets and to detect changes over time. When the performance of the architecture is evaluated using Mean Intersection over Union (mIoU) and global accuracy metrics, superior results are obtained compared to other studies in the literature. In addition, the performance of the model was improved by applying transfer learning to the ImageNet dataset and various hyperparameter settings were performed. The results of the study show that path inferences are detected with 98.4% accuracy. These results show that Edge U-Net architecture and deep learning methods can be effectively used in road detection applications from satellite imagery.en_US
dc.identifier.citationAslan, E. ve Özüpak, Y. (2025). Detection of road extraction from satellite images with deep learning method. Cluster Computing, 28(1), 1-10.en_US
dc.identifier.endpage10en_US
dc.identifier.issn1386-7857
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85209696419
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://link.springer.com/article/10.1007/s10586-024-04880-y
dc.identifier.urihttps://hdl.handle.net/11468/29473
dc.identifier.volume28en_US
dc.identifier.wosWOS:001351543100002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAslan, Emrah
dc.institutionauthorÖzüpak, Yıldırım
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofCluster Computing
dc.relation.isversionof10.1007/s10586-024-04880-yen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEdge U-neten_US
dc.subjectRoad extractionsen_US
dc.subjectSatellite imageryen_US
dc.subjectSegmentatioen_US
dc.subjectTransfer learningen_US
dc.titleDetection of road extraction from satellite images with deep learning methoden_US
dc.titleDetection of road extraction from satellite images with deep learning method
dc.typeArticleen_US

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