Survey of Road Extraction Methods in Remote Sensing Images Based on Deep Learning

Road information plays a fundamental role in application fields such as map updating, traffic management, and road monitoring. Extracting road features from remote sensing images is a hot and frontier issue in the remote sensing field, and it is also one of the most challenging research topics. In view of this, this research systematically reviews the deep learning technology applied to road extraction in remote sensing images and summarizes the existing theories and methods. According to the different annotation types and learning methods, they can be divided into three methods: fully supervised, weakly supervised and unsupervised learning. Then, the datasets and performance evaluation metrics related to road extraction from remote sensing images are summarized, and on this basis, the effects of common road extraction methods are analysed. Finally, suggestions and prospects for the development of road extraction are proposed.