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 2000 high-resolution images.
Highlights
Soil erosion, defined as the detachment, transportation, and deposition of soil by water or wind, is the most important land degradation problem worldwide [1]
We introduce a new framework, based on Convolutional Network (ConvNet), to perform supervised erosion identification in railway lines using high-resolution aerial image sets
All deep learning-based models exploited in this work were implemented using TensorFlow [51], a Python framework conceived to allow efficient exploitation of deep learning with Graphics Processing Units (GPUs)
Summary
Soil erosion, defined as the detachment, transportation, and deposition of soil by water or wind, is the most important land degradation problem worldwide [1]. This section aims to explain all deep learning methods that were evaluated in this work for erosion identification Such semantic segmentation approaches were selected based on their popularity and performance for different applications and images, including computer vision [8,9,10,11,12,13], remote sensing [5,7,26,27,28,29,30], medical [31,32,33,34,35], and so on.
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