Computer vision for landslide analysis integrates advanced deep learning techniques with high-resolution satellite, aerial, and drone imagery to enable automated detection, mapping, and monitoring of landslide events. By leveraging semantic segmentation, object detection, and change detection algorithms, this research focuses on identifying landslide-prone regions, assessing post-disaster impacts, and generating accurate susceptibility maps for risk mitigation. The integration of computer vision with geospatial data and remote sensing facilitates real-time environmental monitoring and supports early warning systems, disaster response, and sustainable land management. This research contributes to enhancing community resilience and informed decision-making in the face of natural hazards.

This research explores the application of computer vision and artificial intelligence for disaster management, with a strong emphasis on developing socially impactful technologies that enhance resilience and save lives. By integrating deep learning with satellite imagery, drone data, and remote sensing, the research focuses on automated damage assessment, flood and landslide mapping, infrastructure monitoring, and search-and-rescue support. Advanced vision models enable rapid identification of affected regions, facilitate real-time situational awareness, and assist emergency responders in resource allocation and decision-making. This research aims to build intelligent disaster response systems that support early warning, accelerate recovery efforts, and contribute to safer and more resilient communities.