A critical analysis of road network extraction using remote sensing images with deep learning

The Extraction of Roads from Remote Sensing Imagery is a rapidly developing field that has significant impacts on both the economic and social domains. In the fields of urban planning, transportation management, and disaster response, accurate and up-to-date road information obtained from satellite and aerial images is essential. Through an in depth-analysis of the existing research, this study identified the research gaps and proposed a framework for Road Extraction. The data for review is collected from the IEEE Xplore, Scopus and Web of Science where 2018–2023 publications are considered. To review the facts, 1198 articles are extracted based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. After meeting the exclusion and inclusion criteria, 44 articles are identified for final considerations. In this study, a thorough investigation on road network model and road features in the context of Remote Sensing Images is discussed. Additionally, we identified a clear gap in the literature where these important elements have either not been thoroughly investigated or not mentioned at all. This paper contributes to the field of Road Extraction by providing accessible datasets with links for researchers. A comparative analysis of existing Deep Learning models is conducted, aiding researchers in model selection. Furthermore, limitations and challenges faced by researchers are highlighted, offering valuable insights for future work.