{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:10:08Z","timestamp":1775146208407,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Agency for Technology and Standards","award":["K_G012002073401"],"award-info":[{"award-number":["K_G012002073401"]}]},{"name":"Korea Agency for Technology and Standards","award":["K_G012002234001"],"award-info":[{"award-number":["K_G012002234001"]}]},{"name":"Korea Agency for Technology and Standards","award":["GCU-202008460006"],"award-info":[{"award-number":["GCU-202008460006"]}]},{"name":"Gachon University","award":["K_G012002073401"],"award-info":[{"award-number":["K_G012002073401"]}]},{"name":"Gachon University","award":["K_G012002234001"],"award-info":[{"award-number":["K_G012002234001"]}]},{"name":"Gachon University","award":["GCU-202008460006"],"award-info":[{"award-number":["GCU-202008460006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, there is a growing population around the world, and this is particularly true in developing countries, where food security is becoming a major problem. Therefore, agricultural land monitoring, land use classification and analysis, and achieving high yields through efficient land use are important research topics in precision agriculture. Deep learning-based algorithms for the classification of satellite images provide more reliable and accurate results than traditional classification algorithms. In this study, we propose a transfer learning based residual UNet architecture (TL-ResUNet) model, which is a semantic segmentation deep neural network model of land cover classification and segmentation using satellite images. The proposed model combines the strengths of residual network, transfer learning, and UNet architecture. We tested the model on public datasets such as DeepGlobe, and the results showed that our proposed model outperforms the classic models initiated with random weights and pre-trained ImageNet coefficients. The TL-ResUNet model outperforms other models on several metrics commonly used as accuracy and performance measures for semantic segmentation tasks. Particularly, we obtained an IoU score of 0.81 on the validation subset of the DeepGlobe dataset for the TL-ResUNet model.<\/jats:p>","DOI":"10.3390\/s22249784","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T03:21:52Z","timestamp":1670988112000},"page":"9784","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6612-8176","authenticated-orcid":false,"given":"Furkat","family":"Safarov","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea"}]},{"given":"Kuchkorov","family":"Temurbek","sequence":"additional","affiliation":[{"name":"Department of Computer Systems, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan"}]},{"given":"Djumanov","family":"Jamoljon","sequence":"additional","affiliation":[{"name":"Department of Computer Systems, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1707-4316","authenticated-orcid":false,"given":"Ochilov","family":"Temur","sequence":"additional","affiliation":[{"name":"Department of Computer Systems, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5675-4747","authenticated-orcid":false,"given":"Jean Chamberlain","family":"Chedjou","sequence":"additional","affiliation":[{"name":"Institute of Smart Systems Technologies, University of Klagenfurt, 9020 Klagenfurt, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5923-8695","authenticated-orcid":false,"given":"Akmalbek Bobomirzaevich","family":"Abdusalomov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-7599","authenticated-orcid":false,"given":"Young-Im","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Neupane, B., Horanont, T., and Aryal, J. 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