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

被引：36
|
作者：
Yang, Liping [1 ,2 ,3 ]
Driscol, Joshua [1 ,2 ]
Sarigai, Sarigai [1 ,2 ]
Wu, Qiusheng [4 ]
Lippitt, Christopher D. [1 ,2 ]
Morgan, Melinda [1 ]
机构：
[1] Univ New Mexico, Dept Geog & Environm Studies, Albuquerque, NM 87131 USA
[2] Univ New Mexico, Ctr Adv Spatial Informat Res & Educ ASPIRE, Albuquerque, NM 87131 USA
[3] Univ New Mexico, Dept Comp Sci, Albuquerque, NM 87106 USA
[4] Univ Tennessee, Dept Geog, Knoxville, TN 37996 USA
来源：
SENSORS | 2022年 / 22卷 / 06期
关键词：
surface water; water body detection; surface water extraction; water quality monitoring; remote sensing; artificial intelligence; computer vision; machine learning; deep learning; convolutional neural networks; GEOVISUAL ANALYTICS; ACCURACY ASSESSMENT; CRISIS MANAGEMENT; DECISION-SUPPORT; LEARNING-METHOD; SURFACE-WATER; INDEX NDWI; DEEP; EXTRACTION; IDENTIFICATION;
D O I：
10.3390/s22062416
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
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.
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页数：48
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