Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30819
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Facing Erosion Identification in Railway Lines Using Pixel-wise Deep-based Approaches
Author(s): Nogueira, Keiller
Machado, Gabriel L S
Gama, Pedro H T
Da Silva, Caio C V
Balaniuk, Remis
Santos, Jefersson A Dos
Keywords: Deep Learning
Remote Sensing
Erosion Identification
High-Resolution Images 14
Issue Date: Feb-2020
Citation: Nogueira K, Machado GLS, Gama PHT, Da Silva CCV, Balaniuk R & Santos JAD (2020) Facing Erosion Identification in Railway Lines Using Pixel-wise Deep-based Approaches. Remote Sensing, 12 (4), Art. No.: 739. https://doi.org/10.3390/rs12040739
Abstract: 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 2,000 high-resolution images.
DOI Link: 10.3390/rs12040739
Rights: This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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