{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T08:55:23Z","timestamp":1777625723899,"version":"3.51.4"},"reference-count":85,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U21A2013"],"award-info":[{"award-number":["U21A2013"]}]},{"name":"National Natural Science Foundation of China","award":["42201415"],"award-info":[{"award-number":["42201415"]}]},{"name":"National Natural Science Foundation of China","award":["41925007"],"award-info":[{"award-number":["41925007"]}]},{"name":"National Natural Science Foundation of China","award":["2019CFA023"],"award-info":[{"award-number":["2019CFA023"]}]},{"name":"National Natural Science Foundation of China","award":["162301212697"],"award-info":[{"award-number":["162301212697"]}]},{"name":"Hubei Natural Science Foundation of China","award":["U21A2013"],"award-info":[{"award-number":["U21A2013"]}]},{"name":"Hubei Natural Science Foundation of China","award":["42201415"],"award-info":[{"award-number":["42201415"]}]},{"name":"Hubei Natural Science Foundation of China","award":["41925007"],"award-info":[{"award-number":["41925007"]}]},{"name":"Hubei Natural Science Foundation of China","award":["2019CFA023"],"award-info":[{"award-number":["2019CFA023"]}]},{"name":"Hubei Natural Science Foundation of China","award":["162301212697"],"award-info":[{"award-number":["162301212697"]}]},{"name":"China University of Geosciences (Wuhan)","award":["U21A2013"],"award-info":[{"award-number":["U21A2013"]}]},{"name":"China University of Geosciences (Wuhan)","award":["42201415"],"award-info":[{"award-number":["42201415"]}]},{"name":"China University of Geosciences (Wuhan)","award":["41925007"],"award-info":[{"award-number":["41925007"]}]},{"name":"China University of Geosciences (Wuhan)","award":["2019CFA023"],"award-info":[{"award-number":["2019CFA023"]}]},{"name":"China University of Geosciences (Wuhan)","award":["162301212697"],"award-info":[{"award-number":["162301212697"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mountain roads are of great significance to traffic navigation and military road planning. Extracting mountain roads based on high-resolution remote sensing images (HRSIs) is a hot spot in current road extraction research. However, massive terrain objects, blurred road edges, and sand coverage in complex environments make it challenging to extract mountain roads from HRSIs. Complex environments result in weak research results on targeted extraction models and a lack of corresponding datasets. To solve the above problems, first, we propose a new dataset: Road Datasets in Complex Mountain Environments (RDCME). RDCME comes from the QuickBird satellite, which is at an elevation between 1264 m and 1502 m with a resolution of 0.61 m; it contains 775 image samples, including red, green, and blue channels. Then, we propose the Light Roadformer model, which uses a transformer module and self-attention module to focus on extracting more accurate road edge information. A post-process module is further used to remove incorrectly predicted road segments. Compared with previous related models, the Light Roadformer proposed in this study has higher accuracy. Light Roadformer achieved the highest IoU of 89.5% for roads on the validation set and 88.8% for roads on the test set. The test on RDCME using Light Roadformer shows that the results of this study have broad application prospects in the extraction of mountain roads with similar backgrounds.<\/jats:p>","DOI":"10.3390\/rs14194729","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T23:07:55Z","timestamp":1663888075000},"page":"4729","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8159-3132","authenticated-orcid":false,"given":"Xinyu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3882-1616","authenticated-orcid":false,"given":"Wei","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruyi","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runyu","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.isprsjprs.2020.08.019","article-title":"BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery","volume":"168","author":"Zhou","year":"2020","journal-title":"ISPRS J. 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