DV3+HED+: a DCNN-based framework to monitor temporary works and ESAs in railway construction project using VHR satellite images

Abstract

Current VHR(Very High Resolution) satellite images enable the detailed monitoring of the earth and can capture the ongoing works of railway construction. In this paper, we present an integrated framework applied to monitoring the railway construction in China, using QuickBird, GF-2 and Google Earth VHR satellite images. We also construct a novel DCNNs-based (Deep Convolutional Neural Networks) semantic segmentation network to label the temporary works such as borrow & spoil area, camp, beam yard and ESAs(Environmental Sensitive Areas) such as resident houses throughout the whole railway construction project using VHR satellite images. In addition, we employ HED edge detection sub-network to refine the boundary details and attention cross entropy loss function to fit the sample class disequilibrium problem. Our semantic segmentation network is trained on 572 VHR true color images, and tested on the 15 QuickBird true color images along Ruichang-Jiujiang railway during 2015-2017. The experiment results show that compared with the existing state-of-the-art approach, our approach has obvious improvements with an overall accuracy of more than 80%.

A Deep Learning Approach Towards Railway Safety Risk Assessment

“…Such studies have been widely reported in the recent literature and use many data sources; they cover management, maintenance, safety and operations [141]. Image-processing approaches for implementing automatic detection have been suggested for monitoring railway infrastructure [128], rail track maintenance [133], railway track inspections and train component inspections [142]- [152] such as the rolling bearings of trains [153].…”

“…A CNN can be used to estimate crowd density at railway stations [173],to detect intrusions in track areas, such as pedestrians or large livestock via images captured in railway areas [174], to monitor railway construction [152] and for intrusion detection at railway crossings [175]. From the security side, the method been used for detecting violent crowd flows [176], protect the critical infrastructure [177], and identifying tools wielding by attackers such as knives, guns and Explosives [178].…”