AI-Driven Integration of UAV-Based Remote Sensing and Geotechnical Monitoring for Early Detection of Landslide Hazards in Mountainous Regions: A Comprehensive Review

Abstract

ABSTRACTLandslides represent one of the most catastrophic geo-hazards in mountainous terrains worldwide, causing severe loss of human lives, destruction of critical civil infrastructure, and substantial socio-economic disruption. Traditional slope monitoring frameworks rely primarily on point-based geotechnical instrumentation or periodic spaceborne satellite remote sensing. While geotechnical sensors offer continuous temporal precision regarding subsurface hydromechanical behavior, they suffer from extreme spatial sparsity. Conversely, satellite remote sensing provides macro-scale spatial coverage but is hampered by orbit revisit latencies, atmospheric cloud cover, and insufficient spatial resolution for localized slope creep. Recent advances in Unmanned Aerial Vehicle (UAV) platforms and Artificial Intelligence (AI) have enabled a transformative paradigm shift in mountain hazard governance. This review paper presents an exhaustive critical analysis of AI-driven frameworks that integrate UAV multi-sensor remote sensing with in-situ geotechnical monitoring for early landslide detection and predictive Early Warning Systems (EWS). We evaluate optical Photogrammetry, dual-return Light Detection and Ranging (LiDAR), Synthetic Aperture Radar (InSAR), and thermal infrared imaging payloads mounted on UAVs. Furthermore, we examine modern deep learning models including Deep Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Spatiotemporal Graph Neural Networks (ST-GNNs), and Physics-Informed Neural Networks (PINNs). Fusing multi-modal spatial point clouds and continuous geotechnical telemetry via spatiotemporal graph architectures governed by soil mechanics loss constraints achieves predictive accuracies exceeding 95% with lead times up to 48 hours prior to catastrophic slope collapse. Key operational challenges, technical limitations, and future directions toward autonomous edge-computing drone swarms and Digital Twin platforms are thoroughly synthesized. KEYWORDS: Landslide Early Warning Systems (EWS); UAV Remote Sensing; Geotechnical Monitoring; Artificial Intelligence; Spatiotemporal Graph Neural Networks; Multi-Sensor Data Fusion; Slope Stability. 

ABSTRACT

Landslides represent one of the most catastrophic geo-hazards in mountainous terrains worldwide, causing severe loss of human lives, destruction of critical civil infrastructure, and substantial socio-economic disruption. Traditional slope monitoring frameworks rely primarily on point-based geotechnical instrumentation or periodic spaceborne satellite remote sensing. While geotechnical sensors offer continuous temporal precision regarding subsurface hydromechanical behavior, they suffer from extreme spatial sparsity. Conversely, satellite remote sensing provides macro-scale spatial coverage but is hampered by orbit revisit latencies, atmospheric cloud cover, and insufficient spatial resolution for localized slope creep. Recent advances in Unmanned Aerial Vehicle (UAV) platforms and Artificial Intelligence (AI) have enabled a transformative paradigm shift in mountain hazard governance. This review paper presents an exhaustive critical analysis of AI-driven frameworks that integrate UAV multi-sensor remote sensing with in-situ geotechnical monitoring for early landslide detection and predictive Early Warning Systems (EWS). We evaluate optical Photogrammetry, dual-return Light Detection and Ranging (LiDAR), Synthetic Aperture Radar (InSAR), and thermal infrared imaging payloads mounted on UAVs. Furthermore, we examine modern deep learning models including Deep Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Spatiotemporal Graph Neural Networks (ST-GNNs), and Physics-Informed Neural Networks (PINNs). Fusing multi-modal spatial point clouds and continuous geotechnical telemetry via spatiotemporal graph architectures governed by soil mechanics loss constraints achieves predictive accuracies exceeding 95% with lead times up to 48 hours prior to catastrophic slope collapse. Key operational challenges, technical limitations, and future directions toward autonomous edge-computing drone swarms and Digital Twin platforms are thoroughly synthesized.

KEYWORDS: Landslide Early Warning Systems (EWS); UAV Remote Sensing; Geotechnical Monitoring; Artificial Intelligence; Spatiotemporal Graph Neural Networks; Multi-Sensor Data Fusion; Slope Stability.