{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:07:44Z","timestamp":1771330064479,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T00:00:00Z","timestamp":1559692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFA0604100"],"award-info":[{"award-number":["2017YFA0604100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFA0601803"],"award-info":[{"award-number":["2016YFA0601803"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41476022"],"award-info":[{"award-number":["41476022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41490643"],"award-info":[{"award-number":["41490643"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Oceanic eddies play an important role in global energy and material transport, and contribute greatly to nutrient and phytoplankton distribution. Deep learning is employed to identify oceanic eddies from sea surface height anomalies data. In order to adapt to segmentation problems for multi-scale oceanic eddies, the pyramid scene parsing network (PSPNet), which is able to satisfy the fusion of semantics and details, is applied as the core algorithm in the eddy detection methods. The results of eddies identified from this artificial intelligence (AI) method are well compared with those from a traditional vector geometry-based (VG) method. More oceanic eddies are detected by the AI algorithm than the VG method, especially for small-scale eddies. Therefore, the present study demonstrates that the AI algorithm is applicable of oceanic eddy detection. It is one of the first few of efforts to bridge AI techniques and oceanography research.<\/jats:p>","DOI":"10.3390\/rs11111349","type":"journal-article","created":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T09:37:58Z","timestamp":1559727478000},"page":"1349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":82,"title":["Oceanic Eddy Identification Using an AI Scheme"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4248-1397","authenticated-orcid":false,"given":"Guangjun","family":"Xu","sequence":"first","affiliation":[{"name":"Oceanic Modeling and Observation Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"UNIVER-NUIST Joint AI Oceanography Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Cheng","family":"Cheng","sequence":"additional","affiliation":[{"name":"UNIVER-NUIST Joint AI Oceanography Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Zhoutong Technology LLC (UNIVER), Beijing 100020, China"}]},{"given":"Wenxian","family":"Yang","sequence":"additional","affiliation":[{"name":"UNIVER-NUIST Joint AI Oceanography Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Zhoutong Technology LLC (UNIVER), Beijing 100020, China"}]},{"given":"Wenhong","family":"Xie","sequence":"additional","affiliation":[{"name":"Oceanic Modeling and Observation Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"UNIVER-NUIST Joint AI Oceanography Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Lingmei","family":"Kong","sequence":"additional","affiliation":[{"name":"UNIVER-NUIST Joint AI Oceanography Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Zhoutong Technology LLC (UNIVER), Beijing 100020, China"}]},{"given":"Renlong","family":"Hang","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Furong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Changming","family":"Dong","sequence":"additional","affiliation":[{"name":"Oceanic Modeling and Observation Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"UNIVER-NUIST Joint AI Oceanography Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7514-3212","authenticated-orcid":false,"given":"Jingsong","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR, Hangzhou 310012, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1146\/annurev-marine-010814-015606","article-title":"Mechanisms of physical-biological-biogeochemical interaction at the oceanic mesoscale","volume":"8","author":"McGillicuddy","year":"2016","journal-title":"Annu. 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