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However, accurate road extraction is a challenging task due to factors such as high road density, building occlusion, and complex traffic environments. In this study, a Spatial Attention Swin Transformer (SASwin Transformer) architecture is proposed to create a robust encoder capable of extracting roads from remote sensing imagery. In this architecture, we have developed a spatial self-attention (SSA) module that captures efficient and rich spatial information through spatial self-attention to reconstruct the feature map. Following this, the module performs residual connections with the input, which helps reduce interference from unrelated regions. Additionally, we designed a Spatial MLP (SMLP) module to aggregate spatial feature information from multiple branches while simultaneously reducing computational complexity. Two public road datasets, the Massachusetts dataset and the DeepGlobe dataset, were used for extensive experiments. The results show that our proposed model has an improved overall performance compared to several state-of-the-art algorithms. In particular, on the two datasets, our model outperforms D-LinkNet with an increase in Intersection over Union (IoU) metrics of 1.88% and 1.84%, respectively.<\/jats:p>","DOI":"10.3390\/rs16071183","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T12:09:40Z","timestamp":1711627780000},"page":"1183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Road Extraction from Remote Sensing Imagery with Spatial Attention Based on Swin Transformer"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8349-4144","authenticated-orcid":false,"given":"Xianhong","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7269-4484","authenticated-orcid":false,"given":"Xiaohui","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6038-8014","authenticated-orcid":false,"given":"Weijia","family":"Cao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2458-6774","authenticated-orcid":false,"given":"Xiaofei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Communication Engineering, Guangzhou University, Guangzhou 511370, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3606-7620","authenticated-orcid":false,"given":"Yunfei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Shaokai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang 330013, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TVT.2013.2281199","article-title":"A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios","volume":"63","author":"Li","year":"2014","journal-title":"IEEE Trans. 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