{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:48:22Z","timestamp":1775738902159,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,23]],"date-time":"2020-02-23T00:00:00Z","timestamp":1582416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004901","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais","doi-asserted-by":"publisher","award":["APQ-00449-17"],"award-info":[{"award-number":["APQ-00449-17"]}],"id":[{"id":"10.13039\/501100004901","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["311395\/2018-0 and 424700\/2018-2"],"award-info":[{"award-number":["311395\/2018-0 and 424700\/2018-2"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2000 high-resolution images.<\/jats:p>","DOI":"10.3390\/rs12040739","type":"journal-article","created":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T03:33:43Z","timestamp":1582515223000},"page":"739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Facing Erosion Identification in Railway Lines Using Pixel-Wise Deep-Based Approaches"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3308-6384","authenticated-orcid":false,"given":"Keiller","family":"Nogueira","sequence":"first","affiliation":[{"name":"Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte-MG 31270-901, Brazil"},{"name":"Computing Science and Mathematics, University of Stirling, Stirling, Scotland FK9 4LA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7133-6324","authenticated-orcid":false,"given":"Gabriel","family":"L. S. Machado","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte-MG 31270-901, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9802-593X","authenticated-orcid":false,"given":"Pedro","family":"H. T. Gama","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte-MG 31270-901, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8964-9724","authenticated-orcid":false,"given":"Caio","family":"C. V. da Silva","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte-MG 31270-901, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Remis","family":"Balaniuk","sequence":"additional","affiliation":[{"name":"Universidade Cat\u00f3lica de Bras\u00edlia, Taguatinga, Bras\u00edlia-DF 71966-700, Brazil"},{"name":"Tribunal de Contas da Uni\u00e3o (TCU), Setor de Administra\u00e7\u00e3o Federal Sul (SAFS), Bras\u00edlia-DF 70042-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8889-1586","authenticated-orcid":false,"given":"Jefersson","family":"A. dos Santos","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte-MG 31270-901, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,23]]},"reference":[{"key":"ref_1","first-page":"20","article-title":"Land degradation: An overview","volume":"1","author":"Eswaran","year":"2001","journal-title":"Responses Land Degrad."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.earscirev.2016.07.009","article-title":"A century of gully erosion research: Urgency, complexity and study approaches","volume":"160","author":"Castillo","year":"2016","journal-title":"Earth-Sci. 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