Landslide detection, mapping, and damage assessment utilizing InSAR and machine learning techniques: A case study of Wayanad District, Kerala, India.

Landslides represent some of the most destructive natural hazards encountered in unstable mountainous regions, such as the Western Ghats in India. The examination of landslides has garnered significant global attention due to their profound impacts on socio-economic activities. The utilization of remote sensing and geographic information systems has proven valuable for integrating the spatial factors that contribute to landslide occurrences. In this study, satellite imagery from Sentinel 1-C Band has been employed, along with further interferometry techniques for detecting the landslide event in 2024 in the Wayanad district, Kerala. Leveraging Artificial Intelligence techniques in RADAR remote sensing, such as machine learning algorithms, particularly the Random Forest (RF) model, were utilized to classify the study area into affected and non-affected regions. The findings also indicate the affected land use and land cover in the given study area. In the end, it can be concluded that significant landslides on 30th July, 2024, in the Wayanad district were primarily precipitated by anthropogenic interventions, compounded by heavy precipitation and unstable topography. Activities such as stone quarrying and infrastructure development emerged as critical factors contributing to these landslides. This research provides valuable insights aimed at mitigating landslide hazards in the Wayanad district, thereby fostering sustainable development and opening a new path to disaster damage mapping with better accuracy.