{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:17:20Z","timestamp":1776118640733,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006752","name":"U.S. Department of the Army\u2013U.S. Army Corps of Engineers (USACE)","doi-asserted-by":"publisher","award":["W912HZ-23-2-0004"],"award-info":[{"award-number":["W912HZ-23-2-0004"]}],"id":[{"id":"10.13039\/100006752","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To the best of our knowledge, this is the first work in this domain. Applying deep learning techniques for semantic segmentation tasks in real-world scenarios has its own challenges, especially the difficulty for models to effectively learn from complex backgrounds while focusing on simpler objects of interest. This challenge is particularly evident in the task of detecting seepages in levee systems, where the fault is relatively simple compared to the complex and varied background. We addressed this problem by introducing negative images and a controlled transfer learning approach for semantic segmentation for accurate seepage segmentation in levee systems.<\/jats:p>","DOI":"10.3390\/rs16132441","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T08:45:34Z","timestamp":1719996334000},"page":"2441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Application of Deep Learning for Segmenting Seepages in Levee Systems"],"prefix":"10.3390","volume":"16","author":[{"given":"Manisha","family":"Panta","sequence":"first","affiliation":[{"name":"Canizaro Livingston Gulf States Center for Environmental Informatics, The University of New Orleans, New Orleans, LA 70148, USA"},{"name":"Department of Computer Science, The University of New Orleans, New Orleans, LA 70148, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Padam Jung","family":"Thapa","sequence":"additional","affiliation":[{"name":"Canizaro Livingston Gulf States Center for Environmental Informatics, The University of New Orleans, New Orleans, LA 70148, USA"},{"name":"Department of Computer Science, The University of New Orleans, New Orleans, LA 70148, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0110-2194","authenticated-orcid":false,"given":"Md Tamjidul","family":"Hoque","sequence":"additional","affiliation":[{"name":"Canizaro Livingston Gulf States Center for Environmental Informatics, The University of New Orleans, New Orleans, LA 70148, USA"},{"name":"Department of Computer Science, The University of New Orleans, New Orleans, LA 70148, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kendall N.","family":"Niles","sequence":"additional","affiliation":[{"name":"US Army Corps of Engineers, Engineer Research and Development Center, Vicksburg, MS 39180, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4038-119X","authenticated-orcid":false,"given":"Steve","family":"Sloan","sequence":"additional","affiliation":[{"name":"US Army Corps of Engineers, Engineer Research and Development Center, Vicksburg, MS 39180, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maik","family":"Flanagin","sequence":"additional","affiliation":[{"name":"US Army Corps of Engineers, New Orleans, LA 70118, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ken","family":"Pathak","sequence":"additional","affiliation":[{"name":"US Army Corps of Engineers, Engineer Research and Development Center, Vicksburg, MS 39180, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahdi","family":"Abdelguerfi","sequence":"additional","affiliation":[{"name":"Canizaro Livingston Gulf States Center for Environmental Informatics, The University of New Orleans, New Orleans, LA 70148, USA"},{"name":"Department of Computer Science, The University of New Orleans, New Orleans, LA 70148, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"ref_1","unstructured":"National Research Council (2013). 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