{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:31:47Z","timestamp":1783783907954,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"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":["2020YFA0713503"],"award-info":[{"award-number":["2020YFA0713503"]}]},{"name":"National Key Research and Development Program of China","award":["42071427"],"award-info":[{"award-number":["42071427"]}]},{"name":"National Natural Science Foundation of China","award":["2020YFA0713503"],"award-info":[{"award-number":["2020YFA0713503"]}]},{"name":"National Natural Science Foundation of China","award":["42071427"],"award-info":[{"award-number":["42071427"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Road extraction from high-resolution remote sensing images is a critical task in image understanding and analysis, yet it poses significant challenges because of road occlusions caused by vegetation, buildings, and shadows. Deep convolutional neural networks have emerged as the leading approach for road extraction because of their exceptional feature representation capabilities. However, existing methods often yield incomplete and disjointed road extraction results. To address this issue, we propose CR-HR-RoadNet, a novel high-resolution road extraction network that incorporates local and global context reasoning. In this work, we introduce a road-adapted high-resolution network as the feature encoder, effectively preserving intricate details of narrow roads and spatial information. To capture multi-scale local context information and model the interplay between roads and background environments, we integrate multi-scale features with residual learning in a specialized multi-scale feature representation module. Moreover, to enable efficient long-range dependencies between different dimensions and reason the correlation between various road segments, we employ a lightweight coordinate attention module as a global context-aware algorithm. Extensive quantitative and qualitative experiments on three datasets demonstrate that CR-HR-RoadNet achieves superior extraction accuracy across various road datasets, delivering road extraction results with enhanced completeness and continuity. The proposed method holds promise for advancing road extraction in challenging remote sensing scenarios and contributes to the broader field of deep-learning-based image analysis for geospatial applications.<\/jats:p>","DOI":"10.3390\/rs15174177","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T08:33:09Z","timestamp":1692952389000},"page":"4177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Road Extraction from High-Resolution Remote Sensing Images via Local and Global Context Reasoning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3864-9265","authenticated-orcid":false,"given":"Jie","family":"Chen","sequence":"first","affiliation":[{"name":"The School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libo","family":"Yang","sequence":"additional","affiliation":[{"name":"The School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"The School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingru","family":"Zhu","sequence":"additional","affiliation":[{"name":"The School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geng","family":"Sun","sequence":"additional","affiliation":[{"name":"The School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Dai","sequence":"additional","affiliation":[{"name":"The School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Deng","sequence":"additional","affiliation":[{"name":"The School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9136-9764","authenticated-orcid":false,"given":"Yan","family":"Shi","sequence":"additional","affiliation":[{"name":"The School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"ref_1","first-page":"102341","article-title":"Adaboost-like End-to-End multiple lightweight U-nets for road extraction from optical remote sensing images","volume":"100","author":"Chen","year":"2021","journal-title":"Int. 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