{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T02:51:10Z","timestamp":1784775070242,"version":"3.55.0"},"reference-count":52,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National NSF of China","award":["U19A2058"],"award-info":[{"award-number":["U19A2058"]}]},{"name":"National NSF of China","award":["41971362"],"award-info":[{"award-number":["41971362"]}]},{"name":"National NSF of China","award":["41871248"],"award-info":[{"award-number":["41871248"]}]},{"name":"National NSF of China","award":["62106276"],"award-info":[{"award-number":["62106276"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Road networks play a fundamental role in our daily life. It is of importance to extract the road structure in a timely and precise manner with the rapid evolution of urban road structure. Recently, road network extraction using deep learning has become an effective and popular method. The main shortcoming of the road extraction using deep learning methods lies in the fact that there is a need for a large amount of training datasets. Additionally, the datasets need to be elaborately annotated, which is usually labor-intensive and time-consuming; thus, lots of weak annotations (such as the centerline from OpenStreetMap) have accumulated over the past a few decades. To make full use of the weak annotations, we propose a novel semi-weakly supervised method based on adversarial learning to extract road networks from remote sensing imagery. Our method uses a small set of pixel-wise annotated data and a large amount of weakly annotated data for training. The experimental results show that the proposed approach can achieve a maintained performance compared with the methods that use a large number of full pixel-wise annotations while using less fully annotated data.<\/jats:p>","DOI":"10.3390\/rs14174145","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T02:55:34Z","timestamp":1661309734000},"page":"4145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["SW-GAN: Road Extraction from Remote Sensing Imagery Using Semi-Weakly Supervised Adversarial Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7880-3394","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"first","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Du","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songbing","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., and Alamri, A. 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