RoadCT: A Hybrid CNN-Transformer Network for Road Extraction From Satellite Imagery
Wei Liu, Shufeng Gao, Chun Zhang, Bijia Yang
Abstract
Electronic road map is essential to support many intelligent transportation applications, and extracting roads from satellite images is a promising approach for map service providers to update their road networks efficiently. Hence, this letter proposes a hybrid deep neural network called RoadCT to improve the performance of road extraction. RoadCT not only integrates the strengths of both convolution and transformer neural networks, but also adopts a relational fusion block to merge the road features with different receptive fields. Extensive evaluations based on two public datasets have illustrated that RoadCT outperforms other state-of-art algorithms by 1.1% - 3.9% on F1 Score and 1.6% - 6.0% on intersection over union.