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Review

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

1
Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA
2
Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM 87131, USA
3
Department of Computer Science, University of New Mexico, Albuquerque, NM 87106, USA
4
Department of Geography, University of Tennessee, Knoxville, TN 37996, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Miquel À. Cugueró-Escofet and Vicenç Puig
Sensors 2022, 22(6), 2416; https://doi.org/10.3390/s22062416
Received: 2 January 2022 / Revised: 1 March 2022 / Accepted: 15 March 2022 / Published: 21 March 2022
Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed. View Full-Text
Keywords: surface water; water body detection; surface water extraction; water quality monitoring; remote sensing; artificial intelligence; computer vision; machine learning; deep learning; convolutional neural networks surface water; water body detection; surface water extraction; water quality monitoring; remote sensing; artificial intelligence; computer vision; machine learning; deep learning; convolutional neural networks
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MDPI and ACS Style

Yang, L.; Driscol, J.; Sarigai, S.; Wu, Q.; Lippitt, C.D.; Morgan, M. Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing. Sensors 2022, 22, 2416. https://doi.org/10.3390/s22062416

AMA Style

Yang L, Driscol J, Sarigai S, Wu Q, Lippitt CD, Morgan M. Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing. Sensors. 2022; 22(6):2416. https://doi.org/10.3390/s22062416

Chicago/Turabian Style

Yang, Liping, Joshua Driscol, Sarigai Sarigai, Qiusheng Wu, Christopher D. Lippitt, and Melinda Morgan. 2022. "Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing" Sensors 22, no. 6: 2416. https://doi.org/10.3390/s22062416

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