{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:30:18Z","timestamp":1760146218542,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"TUOHAI special project 2020 from Bohai Rim Energy Research Institute of Northeast Petroleum University","award":["HBHZX202002","SJGY20200125","2022YFC330160204"],"award-info":[{"award-number":["HBHZX202002","SJGY20200125","2022YFC330160204"]}]},{"name":"Heilongjiang Province Higher Education Teaching Reform Project","award":["HBHZX202002","SJGY20200125","2022YFC330160204"],"award-info":[{"award-number":["HBHZX202002","SJGY20200125","2022YFC330160204"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["HBHZX202002","SJGY20200125","2022YFC330160204"],"award-info":[{"award-number":["HBHZX202002","SJGY20200125","2022YFC330160204"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fugitive dust is an important source of total suspended particulate matter in urban ambient air. The existing segmentation methods for dust sources face challenges in distinguishing key and secondary features, and they exhibit poor segmentation at the image edge. To address these issues, this paper proposes the Dust Source U-Net (DSU-Net), enhancing the U-Net model by incorporating VGG16 for feature extraction, and integrating the shuffle attention module into the jump connection branch to enhance feature acquisition. Furthermore, we combine Dice Loss, Focal Loss, and Activate Boundary Loss to improve the boundary extraction accuracy and reduce the loss oscillation. To evaluate the effectiveness of our model, we selected Jingmen City, Jingzhou City, and Yichang City in Hubei Province as the experimental area and established two dust source datasets from 0.5 m high-resolution remote sensing imagery acquired by the Jilin-1 satellite. Our created datasets include dataset HDSD-A for dust source segmentation and dataset HDSD-B for distinguishing the dust control measures. Comparative analyses of our proposed model with other typical segmentation models demonstrated that our proposed DSU-Net has the best detection performance, achieving a mIoU of 93% on dataset HDSD-A and 92% on dataset HDSD-B. In addition, we verified that it can be successfully applied to detect dust sources in urban areas.<\/jats:p>","DOI":"10.3390\/rs16203772","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T08:10:16Z","timestamp":1728634216000},"page":"3772","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Attention-Enhanced Urban Fugitive Dust Source Segmentation in High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Xiaoqing","family":"He","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China"},{"name":"Bohai-Rim Energy Research Institute, Northeast Petroleum University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1242-5412","authenticated-orcid":false,"given":"Lu","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Electronics, Electrical Engineering and Computer Science, Queen\u2019s University Belfast, Belfast BT9 6SB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-0181","authenticated-orcid":false,"given":"Meng","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9245-1102","authenticated-orcid":false,"given":"Yuanlin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangfu","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109603","DOI":"10.1016\/j.jenvman.2019.109603","article-title":"Defending blue sky in China: Effectiveness of the \u201cAir Pollution Prevention and Control Action Plan\u201d on air quality improvements from 2013 to 2017","volume":"252","author":"Feng","year":"2019","journal-title":"J. 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