Facing Erosion Identification in Railway Lines Using Pixel-wise Deep-based Approaches

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.

Keywords
Deep Learning; Remote Sensing; Erosion Identification; High-Resolution Images 14

Journal
Remote Sensing: Volume 12, Issue 4

• Published version