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RSS FeedsRemote Sensing, Vol. 12, Pages 739: Facing Erosion Identification in Railway Lines Using Pixel-Wise Deep-Based Approaches (Remote Sensing)

 
 

23 february 2020 15:01:15

 
Remote Sensing, Vol. 12, Pages 739: Facing Erosion Identification in Railway Lines Using Pixel-Wise Deep-Based Approaches (Remote Sensing)
 


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.


 
150 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 12, Pages 740: Performance of TRMM Product in Quantifying Frequency and Intensity of Precipitation during Daytime and Nighttime across China (Remote Sensing)
Remote Sensing, Vol. 12, Pages 738: The Spatial and Spectral Resolution of ASTER Infrared Image Data: A Paradigm Shift in Volcanological Remote Sensing (Remote Sensing)
 
 
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