INTELLIGENT LANDSLIDE MONITORING AND PREDICTION USING AI AND SATELLITE OBSERVATIONS

Abstract

Landslides pose a significant threat to human lives, infrastructure, and the environment, necessitating the development of accurate and timely prediction systems. This study explores an AI-powered landslide monitoring and prediction framework that leverages satellite observations and machine learning techniques to enhance early warning capabilities. By integrating deep learning algorithms with remote sensing data, the proposed system identifies potential landslide-prone areas based on terrain features, rainfall patterns, vegetation indices, and historical landslide occurrences. Advanced image processing and geospatial analytics enable the extraction of critical patterns from multispectral and synthetic aperture radar (SAR) satellite imagery, ensuring high prediction accuracy. The model is trained on diverse datasets and validated against real-world landslide events to assess its effectiveness. The results demonstrate that AI-based predictive models outperform traditional methods in detection accuracy, response time, and risk assessment, making them highly suitable for real-time landslide monitoring. The proposed system aims to enhance disaster preparedness, minimize economic losses, and improve decision-making for hazard mitigation.