{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T18:35:09Z","timestamp":1785954909952,"version":"3.56.0"},"reference-count":155,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20201387"],"award-info":[{"award-number":["BK20201387"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["QL2021"],"award-info":[{"award-number":["QL2021"]}]},{"name":"QingLan Project of Jiangsu Province","award":["BK20201387"],"award-info":[{"award-number":["BK20201387"]}]},{"name":"QingLan Project of Jiangsu Province","award":["QL2021"],"award-info":[{"award-number":["QL2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people\u2019s 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.<\/jats:p>","DOI":"10.3390\/s24051708","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T08:43:14Z","timestamp":1709714594000},"page":"1708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1173-0031","authenticated-orcid":false,"given":"Shaoyi","family":"Mo","sequence":"first","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6487-0713","authenticated-orcid":false,"given":"Yufeng","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Foreign Studies, Nanjing Forestry University, Nanjing 210047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, M., Wang, F., and Ji, X. 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