A Review on Landslide detection Systems using Machine Learning Techniques

This paper presents a comprehensive review of landslide detection systems using machine learning and deep learning techniques. Landslides are a major natural hazard that cause significant loss of life, property damage, and environmental impact, particularly in regions experiencing heavy rainfall and rapid urbanization. Early detection and accurate monitoring of landslides are critical for effective disaster management and risk reduction. This study analyses existing approaches in landslide detection, focusing on key domains such as deep learning, transfer learning, and aerial image processing. Various methodologies, including convolutional neural networks (CNNs), remote sensing techniques, and UAV-based image analysis, are examined and compared in terms of accuracy, efficiency, and practical applicability. The review highlights the strengths and limitations of current systems and identifies gaps in existing research. The findings suggest that transfer learning-based approaches significantly improve detection accuracy and reduce the need for large training datasets. Additionally, the integration of aerial imaging and advanced machine learning models enhances the speed and reliability of landslide identification. This review serves as a foundation for future research aimed at developing more efficient and scalable landslide detection systems. The insights gained from this study can support disaster response teams in making faster and more informed decisions, ultimately contributing to improved safety and risk mitigation strategies.