{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:24:04Z","timestamp":1780385044375,"version":"3.54.1"},"reference-count":24,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4172001 and KZ201610005007"],"award-info":[{"award-number":["4172001 and KZ201610005007"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Research Program of Qinghai Province","award":["2020-ZJ-709 and 2021-ZJ-704"],"award-info":[{"award-number":["2020-ZJ-709 and 2021-ZJ-704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>River system is critical for the future sustainability of our planet but is always under the pressure of food, water and energy demands. Recent advances in machine learning bring a great potential for automatic river mapping using satellite imagery. Surface river mapping can provide accurate and timely water extent information that is highly valuable for solid policy and management decisions. However, accurate large-scale river mapping remains challenging given limited labels, spatial heterogeneity and noise in satellite imagery (e.g., clouds and aerosols). In this paper, we propose a new multi-source data-driven method for large-scale river mapping by combining multi-spectral imagery and synthetic aperture radar data. In particular, we build a multi-source data segmentation model, which uses contrastive learning to extract the common information between multiple data sources while also preserving distinct knowledge from each data source. Moreover, we create the first large-scale multi-source river imagery dataset based on Sentinel-1 and Sentinel-2 satellite data, along with 1013 handmade accurate river segmentation mask (which will be released to the public). In this dataset, our method has been shown to produce superior performance (F1-score is 91.53%) over multiple state-of-the-art segmentation algorithms. We also demonstrate the effectiveness of the proposed contrastive learning model in mapping river extent when we have limited and noisy data.<\/jats:p>","DOI":"10.3390\/rs13152893","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T10:31:44Z","timestamp":1627036304000},"page":"2893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Large-Scale River Mapping Using Contrastive Learning and Multi-Source Satellite Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1607-2725","authenticated-orcid":false,"given":"Zhihao","family":"Wei","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Laboratory of Advanced Information Network, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kebin","family":"Jia","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Laboratory of Advanced Information Network, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Laboratory of Advanced Information Network, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqun","family":"Xie","sequence":"additional","affiliation":[{"name":"Geospatial Information Science Department, University of Maryland, College Park, MD 20742, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tarpanelli, A., Iodice, F., Brocca, L., Restano, M., and Benveniste, J. 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