A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images

Abstract

Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people’s lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided.

A Review of Deep Learning-Based Methods for Road Extraction from High-Resolution Remote Sensing Images

“…Nonetheless, our analysis shows most networks concentrate on road surface extraction. Liu et al [22] and Mo et al [23] classified methods into fully supervised learning, semi-supervised learning, and unsupervised learning based on the type of data annotation and learning approach.…”

Artificial Intelligence and Deep Learning in Sensors and Applications

“…In this context, the Special Issue "Artificial Intelligence and Deep Learning in Sensors and Applications" collected high-quality original contributions on new developments in AI (specifically deep learning) and sensor technology in various fields, as well as sharing ideas, designs, data-driven applications, and production and deployment experiences and challenges. The call for papers for this Special Issue included topics such as applications and sensors for manufacturing, machinery, and semiconductors; smart applications and sensors for architecture, construction, buildings, e-learning; recommendation systems; applications and sensors for autonomous vehicles, traffic monitoring, and transportation; object recognition, image classification, object detection, speech processing, human behavior analysis; and other related sensing applications [13,14].…”