AI-Enhanced Remote Sensing for Water Infrastructure Safety and Deformation Monitoring

Dear Colleagues,

The increasing pressure on natural resources, climate variability, and the growing frequency of floods, landslides, droughts, and other hydro-environmental hazards highlight the need for advanced approaches to monitor and manage water resources and related infrastructure. Reliable observation of changes in natural and engineered systems is essential for improving resource management, reducing the impacts of natural hazards, and supporting climate-resilient development.

Recent advances in Earth observation, remote sensing, and artificial intelligence (AI) provide new opportunities for continuous, large-scale, and cost-effective monitoring of natural resources and water-related systems. Technologies such as InSAR, multi-temporal SAR, optical satellite imagery, UAVs, LiDAR, and GNSS, combined with machine learning and deep learning, enable improved detection of surface deformation, hydrological changes, slope instability, flooding, and environmental disturbances.

This Special Issue aims to highlight recent advances in AI-enhanced remote sensing for the monitoring, assessment, and sustainable management of natural resources and water-related systems. We welcome interdisciplinary contributions that integrate remote sensing, geomorphology, hydrology, environmental sciences, geospatial analysis, and AI. Both methodological developments and practical applications are encouraged.

Particular attention is also given to karst environments, where complex groundwater–surface water interactions, heterogeneous recharge pathways, and high aquifer vulnerability create significant challenges for sustainable groundwater management. Recent advances in remote sensing, geospatial analysis, and AI offer new opportunities for characterizing karst systems, assessing groundwater vulnerability, monitoring recharge and water quality, and supporting climate-resilient management of karst aquifers.

Topics of interest include, but are not limited to:

• AI and remote sensing for natural resource monitoring and management;
• InSAR and multi-temporal SAR for deformation and infrastructure monitoring;
• Dam, reservoir, and water infrastructure safety assessment;
• Landslide and slope instability monitoring and prediction;
• Flood monitoring, mapping, and prediction;
• Remote sensing of hydrological processes and water resources;
• AI-based hazard detection, susceptibility mapping, and early-warning systems;
• Multi-source Earth observation and geospatial data fusion;
• Machine learning and deep learning for environmental monitoring;
• Climate-related impacts on water resources and natural hazards;
• UAV, LiDAR, GNSS, and satellite-based environmental monitoring;
• Physics-informed and hybrid AI approaches for natural hazard assessment;
• Sustainable and climate-resilient management of water and natural resources;
• Remote sensing, AI, GIS and geospatial approaches for karst aquifer characterization, vulnerability assessment, and sustainable groundwater management;
• Monitoring of karst groundwater recharge, quality, availability, and groundwater–surface water interactions under climate change.

We invite original research articles, review papers, and methodological studies that advance the use of remote sensing and AI for understanding, monitoring, and sustainably managing natural resources and associated hazards.

Dr. Mehrnoosh Ghadimi
Dr. Bing Liu
Guest Editors

Manuscript Submission Information

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