A Comprehensive Review and Analysis of Landslide Detection Through Advanced Image Analysis Techniques

Landslides are among the most destructive natural hazards, causing substantial loss of life and property damage worldwide. Remote sensing through satellite imagery has emerged as a valuable tool for studying and monitoring landslides over large areas. This review paper provides a systematic examination of the application of satellite data and image processing techniques for landslide detection and mapping. The literature review conducted across major scientific databases yielded over 500 initial results, of which 35 papers were selected for final review based on relevance, rigor, and innovation. The review summarizes key trends, datasets, methods, and challenges related to landslide detection using optical, radar, and LiDAR data. Analysis reveals that integration of multiple sensors, use of machine learning algorithms, and object-based image analysis have significantly improved landslide detection accuracy. However, challenges remain in distinguishing landslide signals from noise, handling data gaps, and transferring models across different geographical settings. Satellite-based landslide detection has immense potential for hazard assessment and disaster response if current limitations can be addressed through multi-temporal analysis, model generalization, and combination with ground data. This review provides a synthesis of current research and identifies promising directions for future work toward an operational landslide detection system using satellite imagery.