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

Journal: Sensors (Basel, Switzerland)
Published Date: Mar 21, 2022

Abstract

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.

Authors

  • Liping Yang
    Department of Emergency, The First People's Hospital of Lianyungang, Lianyungang City, 222002, China.
  • Joshua Driscol
    Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.
  • Sarigai Sarigai
    Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.
  • Qiusheng Wu
    Department of Geography, University of Tennessee, Knoxville, TN 37996, USA.
  • Christopher D Lippitt
    Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.
  • Melinda Morgan
    Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA.

Keywords

External Resources

Popular Topics
Recent Journals