  Road extraction from aerial images has been a hot research topic in the field
of remote sensing image analysis. In this letter, a semantic segmentation
neural network which combines the strengths of residual learning and U-Net is
proposed for road area extraction. The network is built with residual units and
has similar architecture to that of U-Net. The benefits of this model is
two-fold: first, residual units ease training of deep networks. Second, the
rich skip connections within the network could facilitate information
propagation, allowing us to design networks with fewer parameters however
better performance. We test our network on a public road dataset and compare it
with U-Net and other two state of the art deep learning based road extraction
methods. The proposed approach outperforms all the comparing methods, which
demonstrates its superiority over recently developed state of the arts.
