Yolo Landslide Sentry: A Framework for Rapid Detection and Response

Landslides in hairpin bend regions pose unique challenges, demanding innovative solutions for effective detection and suppression. This paper introduces a new approach employing the YOLO object detection framework to address this issue. High-resolution satellite imagery is utilized to create detailed terrain models, enabling the identification of potential landslide triggers such as slope instability and changes in vegetation cover. YOLO's real-time capabilities facilitate early detection within these complex landscapes. Furthermore, ground-based sensors and weather stations provide continuous monitoring of environmental conditions, seamlessly integrated with the YOLO-based detection model. Community engagement is emphasized, involving local residents in evacuation plans and preparedness strategies. This interdisciplinary approach offers a comprehensive solution for proactively managing landslide risks in hairpin bend regions, enhancing disaster resilience and safeguarding vulnerable communities. The proposed model provides an F1 score of 0.89 with classification outcome of range of hazard levels.