A Two-Step Deep Convolution Neural Network for Road Extraction from Aerial Images

Road extraction has been one of the important research topics in field of remote sensing imagery due to its significant role in various areas such as traffic management, urban planning, GPS navigation, disaster management etc. In this paper, we investigate and exploit a deep convolution network, U-net, for road extraction from aerial images. We propose a model which is a union of a high precision network and a high recall network. Both the networks are based on deep U-net. Massachusetts road dataset is used in the experiments. The results demonstrate that our proposed model outperforms state-of-the-art frameworks in terms of accuracy, precision, recall, and F-score.