{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T17:37:00Z","timestamp":1777484220722,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFA075104"],"award-info":[{"award-number":["2021YFA075104"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["KM202110005024"],"award-info":[{"award-number":["KM202110005024"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["XDB41000000"],"award-info":[{"award-number":["XDB41000000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Project of Beijing Educational Committee","award":["2021YFA075104"],"award-info":[{"award-number":["2021YFA075104"]}]},{"name":"Scientific Research Project of Beijing Educational Committee","award":["KM202110005024"],"award-info":[{"award-number":["KM202110005024"]}]},{"name":"Scientific Research Project of Beijing Educational Committee","award":["XDB41000000"],"award-info":[{"award-number":["XDB41000000"]}]},{"name":"Strategic Priority Program of the Chinese Academy of Sciences","award":["2021YFA075104"],"award-info":[{"award-number":["2021YFA075104"]}]},{"name":"Strategic Priority Program of the Chinese Academy of Sciences","award":["KM202110005024"],"award-info":[{"award-number":["KM202110005024"]}]},{"name":"Strategic Priority Program of the Chinese Academy of Sciences","award":["XDB41000000"],"award-info":[{"award-number":["XDB41000000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water is a vital component of life that exists in a variety of forms, including oceans, rivers, ponds, streams, and canals. The automated methods for detecting, segmenting, and mapping surface water have improved significantly with the advancements in satellite imagery and remote sensing. Many strategies and techniques to segment water resources have been presented in the past. However, due to the variant width and complex appearance, the segmentation of the water channel remains challenging. Moreover, traditional supervised deep learning frameworks have been restricted by the scarcity of water channel datasets that include precise water annotations. With this in mind, this research presents the following three main contributions. Firstly, we curated a new dataset for water channel mapping in the Pakistani region. Instead of employing pixel-level water channel annotations, we used a weakly trained method to extract water channels from VHR pictures, relying only on OpenStreetMap (OSM) waterways to create sparse scribbling annotations. Secondly, we benchmarked the dataset on state-of-the-art semantic segmentation frameworks. We also proposed AUnet, an atrous convolution inspired deep learning network for precise water channel segmentation. The experimental results demonstrate the superior performance of the proposed AUnet model for segmenting using weakly supervised labels, where it achieved a mean intersection over union score of 0.8791 and outperformed state-of-the-art approaches by 5.90% for the extraction of water channels.<\/jats:p>","DOI":"10.3390\/rs14143283","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:06:21Z","timestamp":1657497981000},"page":"3283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["AUnet: A Deep Learning Framework for Surface Water Channel Mapping Using Large-Coverage Remote Sensing Images and Sparse Scribble Annotations from OSM Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Sarah","family":"Mazhar","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Faculty of Engineering and Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangmin","family":"Sun","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7760-3374","authenticated-orcid":false,"given":"Anas","family":"Bilal","sequence":"additional","affiliation":[{"name":"Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Education Department of Guangxi Zhuang Autonomous Region, Qinzhou 535011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3672-8100","authenticated-orcid":false,"given":"Bilal","family":"Hassan","sequence":"additional","affiliation":[{"name":"Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"},{"name":"Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5693-5353","authenticated-orcid":false,"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8769-8506","authenticated-orcid":false,"given":"Yinyi","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5364-645X","authenticated-orcid":false,"given":"Ali","family":"Khan","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramsha","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5896-8677","authenticated-orcid":false,"given":"Taimur","family":"Hassan","sequence":"additional","affiliation":[{"name":"Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"},{"name":"Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. 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