  Analysis of high-resolution satellite images has been an important research
topic for traffic management, city planning, and road monitoring. One of the
problems here is automatic and precise road extraction. From an original image,
it is difficult and computationally expensive to extract roads due to presences
of other road-like features with straight edges. In this paper, we propose an
approach for automatic road extraction based on a fully convolutional neural
network of U-net family. This network consists of ResNet-34 pre-trained on
ImageNet and decoder adapted from vanilla U-Net. Based on validation results,
leaderboard and our own experience this network shows superior results for the
DEEPGLOBE - CVPR 2018 road extraction sub-challenge. Moreover, this network
uses moderate memory that allows using just one GTX 1080 or 1080ti video cards
to perform whole training and makes pretty fast predictions.
