{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T06:10:35Z","timestamp":1770876635305,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFC3000400"],"award-info":[{"award-number":["2021YFC3000400"]}]},{"name":"Key Laboratory of Airborne Geophysics and Remote Sensing Geology Foundation","award":["2023YFL16"],"award-info":[{"award-number":["2023YFL16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the continuous improvement of remote sensing image resolution, accurately extracting road information from complex backgrounds remains challenging. This is because roads present diverse morphological characteristics across regions and scales, and their spectral features are highly similar to those of surrounding objects, such as buildings and bare soil, making them hard to distinguish. Occlusion by buildings and trees leads to incomplete road extraction. To solve the above problems, this paper proposed the atrous\u2013strip\u2013Unet (ASUNet), an encoder\u2013decoder network into which atrous and strip convolution modules are inserted to extract roads with weak features and complex backgrounds from high-resolution remote sensing images. In this study, we construct the Zhouqu Road Dataset from high-resolution aerial imagery, covering representative road types (rural, suburban, and urban) characteristic of county-level settlements in western China. By comparing several advanced algorithms with excellent learning performance\u2014including BiSeNet and LinkNet\u2014on both the Zhouqu Road and DeepGlobe Datasets, the improved and optimized model presented in this paper demonstrates better extraction accuracy and effectiveness; it achieves F1 scores of 0.7292 and 0.7134 on the two datasets, respectively. It is particularly worth mentioning that our proposed algorithm shows better performance in scenarios where road features are weak or backgrounds are complex.<\/jats:p>","DOI":"10.3390\/s26041134","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T09:16:08Z","timestamp":1770801368000},"page":"1134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Road Extraction with Weak Features and Complex Backgrounds Based on Atrous\u2013Strip\u2013UNet"],"prefix":"10.3390","volume":"26","author":[{"given":"Yanni","family":"Ma","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"China Natural Resources Aerial Geophysical and Remote Sensing Center, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2987-0504","authenticated-orcid":false,"given":"Junchuan","family":"Yu","sequence":"additional","affiliation":[{"name":"China Natural Resources Aerial Geophysical and Remote Sensing Center, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiu","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Beijing University of Science and Technology, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Chen","sequence":"additional","affiliation":[{"name":"China Natural Resources Aerial Geophysical and Remote Sensing Center, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"China Natural Resources Aerial Geophysical and Remote Sensing Center, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Wu","sequence":"additional","affiliation":[{"name":"China Natural Resources Aerial Geophysical and Remote Sensing Center, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanbiao","family":"Dong","sequence":"additional","affiliation":[{"name":"China Natural Resources Aerial Geophysical and Remote Sensing Center, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MGRS.2024.3491014","article-title":"Deep Learning-Based Methods for Road Extraction From Remote Sensing Images: A vision, survey, and future directions","volume":"13","author":"Wang","year":"2025","journal-title":"IEEE Geosci. 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