dc.contributor.author | Zamanoglu, Esref Samil | |
dc.contributor.author | Erbay, Sergen | |
dc.contributor.author | Cengil, Emine | |
dc.contributor.author | Kosunalp, Selahattin | |
dc.contributor.author | Tumen, Vedat | |
dc.contributor.author | Demir, Kubilay | |
dc.date.accessioned | 2024-03-04T09:50:30Z | |
dc.date.available | 2024-03-04T09:50:30Z | |
dc.date.issued | 2023 | en_US |
dc.identifier.citation | Esref Samil Zamanoglu, Sergen Erbay, Emine Cengil, & Demir, K. (2023, November 23). Land Cover Segmentation using DeepLabV3 and ResNet50. Retrieved March 4, 2024, from ResearchGate website: https://www.researchgate.net/publication/377133018_Land_Cover_Segmentation_using_DeepLabV3_and_ResNet50
| en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.14002/2395 | |
dc.description.abstract | Land cover segmentation has a great importance in various fields, including remote sensing, environmental monitoring, urban planning, agriculture, and natural resource management. It involves a division process of a landscape or region into different classes or categories with respect to the type of land cover in each place. With the recent developments in remote sensing area, high-resolution satellite images can be simply acquired. For an efficient land cover segmentation, in this study, a hybrid approach using deep learning architectures DeepLabV3 and ResNet34 is proposed. The proposed method has been trained and tested using the LandCover AI dataset. As a result, 88.2% F1-score value was obtained with the proposed hybrid approach. © 2023 IEEE. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | Land Cover Segmentation using DeepLabV3 and ResNet50 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | artificial intelligence; DeepLabV3; LandCoverAI; ResNet34; semantic segmentation | en_US |
dc.subject | Deep learning; Environmental management; Natural resources management; Semantic Segmentation; Semantics; Deeplabv3; Different class; Environmental Monitoring; Hybrid approach; Land cover; Landcoverai; Remote-sensing; Resnet34; Semantic segmentation; Sensing areas; Remote sensing | en_US |
dc.title | Land Cover Segmentation using DeepLabV3 and ResNet50 | en_US |
dc.type | article | en_US |
dc.authorid | 0000-0003-3281-3375 | en_US |
dc.department | Fakülteler, Teknoloji Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | en_US |
dc.institutionauthor | Zamanoglu, Esref Samil | |
dc.institutionauthor | Erbay, Sergen | |
dc.identifier.doi | 10.1109/CIEES58940.2023.10378824 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 58852939700 | en_US |
dc.authorscopusid | 58852869600 | en_US |
dc.identifier.scopus | 2-s2.0-85183581038 | en_US |