{Ming Wu Chuang Zhang Lichen Zhou}

Abstract

Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the pastdecade. In this paper, we propose a semantic segmentationneural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task. The network is built withLinkNet architecture and has dilated convolution layers inits center part. Linknet architecture is efficient in computation and memory. Dilation convolution is a powerful toolthat can enlarge the receptive field of feature points withoutreducing the resolution of the feature maps. In the CVPRDeepGlobe 2018 Road Extraction Challenge, our best IoUscores on the validation set and the test set are 0.6466 and0.6342 respectively.