{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:07:42Z","timestamp":1778663262523,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42050202"],"award-info":[{"award-number":["42050202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAST Young Elite Foundation","award":["42050202"],"award-info":[{"award-number":["42050202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Road extraction from remote sensing images is significant for urban planning, intelligent transportation, and vehicle navigation. However, it is challenging to automatically extract roads from remote sensing images because the scale difference of roads in remote sensing images varies greatly, and slender roads are difficult to identify. Moreover, the road in the image is often blocked by the shadows of trees and buildings, which results in discontinuous and incomplete extraction results. To solve the above problems, this paper proposes a multiscale feature encoding and long-range context-aware network (MECA-Net) for road extraction. MECA-Net adopts an encoder\u2013decoder structure and contains two core modules. One is the multiscale feature encoding module, which aggregates multiscale road features to improve the recognition ability of slender roads. The other is the long-range context-aware module, which consists of the channel attention module and the strip pooling module, and is used to obtain sufficient long-range context information from the channel dimension and spatial dimension to alleviate road occlusion. Experimental results on the open DeepGlobe road dataset and Massachusetts road dataset indicate that the proposed MECA-Net outperforms the other eight mainstream networks, which verifies the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/rs14215342","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:17:48Z","timestamp":1666768668000},"page":"5342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["MECA-Net: A MultiScale Feature Encoding and Long-Range Context-Aware Network for Road Extraction from Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Yongshi","family":"Jie","sequence":"first","affiliation":[{"name":"Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"He","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Xing","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anzhi","family":"Yue","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6052-4119","authenticated-orcid":false,"given":"Wei","family":"Tan","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyu","family":"Yue","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,25]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Automatic road network recognition and extraction for urban planning","volume":"5","author":"Bong","year":"2009","journal-title":"Int. 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