Deep learning-based road extraction from remote sensing imagery: Progress, problems, and perspectives

Accurate and up-to-date mapping and extraction of road networks are essential for maintaining urban functionality and fostering socioeconomic development, particularly in realizing intelligent transport systems and smart city management. Recent advancements in Earth observation and artificial intelligence technologies have facilitated more efficient and accurate extraction of road networks from large volumes of remote sensing imagery. To investigate these developments, we conducted a comprehensive review of peer-reviewed literature published between 2017 and 2024, by examining three aspects: data, methods, and applications. This review revealed key trends in deep learning-based road extraction from remote sensing imagery, including a shift from raster to vector approaches, from local-scale to global-scale studies, and from pixel-level recognition to practical applications. Additionally, to achieve high-precision, global-scale road vector extraction, we highlight three emerging research directions: 1) vectorized extraction of complex viaducts; 2) integration of multimodal remote sensing data; and 3) the development of novel applications to foster scientific discoveries. Advancing research in these areas will have profound implications for traffic management, urban planning, disaster response, and the analysis of socio-economic dynamics. Furthermore, this review collects and shares open-source datasets and code related to road extraction to support future research, available at https://github.com/RCAIG/GRE-Hub .