Landslide Identification Framework Using Seismic Signal and Image Segmentation

Mountainous places are at serious risk from landslides, especially in delicate regions like the Western Ghats that are extremely susceptible to these natural calamities. For instance, there have been numerous devastating landslides in Kerala’s Wayanad district, which have caused significant infrastructure damage and fatalities. Nevertheless, proactive landslide detection and early warning systems continue to have a significant deficit. A novel framework for landslide monitoring and detection in these areas is presented in this paper. The framework combines satellite image segmentation using a U-Net-based deep learning model with seismic signal analysis utilizing Continuous Wavelet Transform (CWT) and Empirical Mode Decomposition (EMD). The suggested framework seeks to improve the accuracy of landslide detection and enable prompt identification of landslide-prone areas by integrating these approaches. Although this framework presents a viable way to enhance early warning systems, more testing and improvement are required before it can be used in real-world applications.