{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:47:46Z","timestamp":1768344466455,"version":"3.49.0"},"reference-count":69,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Virtual Red Sea Initiative","award":["REP\/1\/3268-01-01"],"award-info":[{"award-number":["REP\/1\/3268-01-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detecting mesoscale ocean eddies provides a better understanding of the oceanic processes that govern the transport of salt, heat, and carbon. Established eddy detection techniques rely on physical or geometric criteria, and they notoriously fail to predict eddies that are neither circular nor elliptical in shape. Recently, deep learning techniques have been applied for semantic segmentation of mesoscale eddies, relying on the outputs of traditional eddy detection algorithms to supervise the training of the neural network. However, this approach limits the network\u2019s predictions because the available annotations are either circular or elliptical. Moreover, current approaches depend on the sea-surface height, temperature, or currents as inputs to the network, and these data may not provide all the information necessary to accurately segment eddies. In the present work, we have trained a neural network for the semantic segmentation of eddies using human-based\u2014and expert-validated\u2014annotations of eddies in the Arabian Sea. Training with human-annotated datasets enables the network predictions to include more complex geometries, which occur commonly in the real ocean. We then examine the impact of different combinations of input surface variables on the segmentation performance of the network. The results indicate that providing additional surface variables as inputs to the network improves the accuracy of the predictions by approximately 5%. We have further fine-tuned another pre-trained neural network to segment eddies and achieved a reduced overall training time and higher accuracy compared to the results from a network trained from scratch.<\/jats:p>","DOI":"10.3390\/rs15061525","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:03:57Z","timestamp":1678676637000},"page":"1525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Semantic Segmentation of Mesoscale Eddies in the Arabian Sea: A Deep Learning Approach"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1896-4927","authenticated-orcid":false,"given":"Mohamad Abed El Rahman","family":"Hammoud","sequence":"first","affiliation":[{"name":"Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3996-7011","authenticated-orcid":false,"given":"Peng","family":"Zhan","sequence":"additional","affiliation":[{"name":"Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia"},{"name":"Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 510000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar","family":"Hakla","sequence":"additional","affiliation":[{"name":"Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut 1107, Lebanon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0973-1112","authenticated-orcid":false,"given":"Omar","family":"Knio","sequence":"additional","affiliation":[{"name":"Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia"},{"name":"Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3751-4393","authenticated-orcid":false,"given":"Ibrahim","family":"Hoteit","sequence":"additional","affiliation":[{"name":"Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia"},{"name":"Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"ref_1","unstructured":"Kundu, P.K., Cohen, I.M., and Dowling, D.R. 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