  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%.
