{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:57:58Z","timestamp":1775138278995,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Research Council (ERC)","award":["679097"],"award-info":[{"award-number":["679097"]}]},{"DOI":"10.13039\/501100000781","name":"European Union\u2019s Horizon 2020 research and innovation program","doi-asserted-by":"publisher","award":["679097"],"award-info":[{"award-number":["679097"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use\/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies.<\/jats:p>","DOI":"10.3390\/rs14184558","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"4558","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-494X","authenticated-orcid":false,"given":"Elif","family":"Sertel","sequence":"first","affiliation":[{"name":"Geomatics Engineering Department, Faculty of Civil Engineering, Istanbul Technical University, Istanbul 34469, Turkey"},{"name":"Department of History, College of Social Sciences and Humanities, Ko\u00e7 University, Rumelifeneri Yolu, Istanbul 34450, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7014-1907","authenticated-orcid":false,"given":"Burak","family":"Ekim","sequence":"additional","affiliation":[{"name":"Department of History, College of Social Sciences and Humanities, Ko\u00e7 University, Rumelifeneri Yolu, Istanbul 34450, Turkey"},{"name":"Department of Aerospace Engineering, University of the Bundeswehr Munich, 85577 Neubiberg, Germany"}]},{"given":"Paria","family":"Ettehadi Osgouei","sequence":"additional","affiliation":[{"name":"Department of History, College of Social Sciences and Humanities, Ko\u00e7 University, Rumelifeneri Yolu, Istanbul 34450, Turkey"},{"name":"Department of Communication Systems, Institute of Informatics, Istanbul Technical University, Istanbul 34469, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3206-0190","authenticated-orcid":false,"given":"M. Erdem","family":"Kabadayi","sequence":"additional","affiliation":[{"name":"Department of History, College of Social Sciences and Humanities, Ko\u00e7 University, Rumelifeneri Yolu, Istanbul 34450, Turkey"},{"name":"School of Geographical and Earth Sciences, University of Glasgow, Glasgow G12 8QQ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sertel, E., Topalo\u011flu, R.H., \u015eall\u0131, B., Yay Algan, I., and Aksu, G.A. (2018). Comparison of landscape metrics for three different level land cover\/land use maps. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100408"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. 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