Smart Landslide Detection and Early Warning System Using IOT and Machine Learning

Landslides are significant natural disasters that pose threats to human life, infrastructure, and the environment. Timely detection and accurate identification of landslides are crucial for disaster management and mitigation. This project introduces a smart landslide detection system powered by a YOLO (You Only Look Once) deep learning model. Designed as a software solution, the system enables users to detect and label landslides in images, videos, and live feeds with high precision and speed. The software includes a user-friendly interface for registration, login, and access to a detection dashboard. Users can upload images and videos for analysis or connect to live feeds for real-time detection. It integrates the YOLO model to process the inputs and identify landslide-prone areas by labelling them with bounding boxes. The results are then displayed to the user, providing actionable insights. This system combines advanced deep learning algorithms with a scalable software design to offer a reliable and efficient solution for landslide detection. By facilitating accurate identification and real-time monitoring, the project supports disaster preparedness and contributes to reducing the adverse impacts of landslides.