@techreport{oai:ipsj.ixsq.nii.ac.jp:00216932,
 author = {Yubo, Wang and Zhao, Wang and Yuusuke, Nakano and Ken, Nishimatsu and Katsuya, Hasegawa and Jun, Ohya and Yubo, Wang and Zhao, Wang and Yuusuke, Nakano and Ken, Nishimatsu and Katsuya, Hasegawa and Jun, Ohya},
 issue = {1},
 month = {Mar},
 note = {In this work, we present an end-to-end cascade neural network model called Deep Cascade Road Extraction Network to extract accurate road networks from aerial imagery. On the basis of the cascade structure consisting of three subnetworks (Surface Segmentation Network, Edge Detection Network, and Centreline Extraction Network) connected in a cascade manner, we simultaneously achieve the three tasks of the subnetworks for road extraction. Through comparison experiments, our method achieves state-of-the-art results for all the three subtasks. Meanwhile, our model demonstrates strong robustness to occlusions while accurately extracting complex road areas., In this work, we present an end-to-end cascade neural network model called Deep Cascade Road Extraction Network to extract accurate road networks from aerial imagery. On the basis of the cascade structure consisting of three subnetworks (Surface Segmentation Network, Edge Detection Network, and Centreline Extraction Network) connected in a cascade manner, we simultaneously achieve the three tasks of the subnetworks for road extraction. Through comparison experiments, our method achieves state-of-the-art results for all the three subtasks. Meanwhile, our model demonstrates strong robustness to occlusions while accurately extracting complex road areas.},
 title = {Deep Cascade Road Extraction Network:a Multi-task Method for Road Extraction},
 year = {2022}
}