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

Area of Science:

• Environmental Science
• Computer Science
• Geoscience

Background:

• Water quantity and quality are critical for climate-change resilience.
• Remote sensing (RS) and artificial intelligence (AI) are increasingly vital for water information extraction and monitoring.
• Intelligent monitoring of water resources is essential for effective environmental management.

Purpose of the Study:

• To systematically review literature on AI and computer vision in water resources.
• To focus on intelligent water body extraction and water quality detection using RS.
• To identify challenges and research priorities in AI-driven water resource management.

Main Methods:

• Systematic literature review of AI and computer vision in the water sector.
• Analysis of studies focusing on remote sensing for water information extraction.
• Development of an interactive web application for literature review.

Main Results:

• AI and RS significantly advance automated water information extraction and monitoring.
• Key challenges in leveraging AI and RS for water resources were identified.
• Research priorities for intelligent water management were established.

Conclusions:

• AI and RS integration offers powerful tools for enhancing climate-change resilience through improved water management.
• Further research is needed to overcome current challenges in AI-driven water monitoring.
• The developed web application facilitates dynamic exploration of relevant scientific literature.