Real-Time Wildfire Progression Analysis and Prediction using Hybrid Model

Wildfires pose a major threat to forests, ecosystems, and human life, making early detection essential for effective disaster management. This research proposes a satellite-based wildfire detection system that utilizes deep learning techniques to identify fire from satellite imagery. The system collects geographical coordinates and date inputs from the user and retrieves corresponding satellite images from the NASA repository. These images are then processed and analyzed using the YOLO (You Only Look Once) object detection model trained with datasets prepared through the Roboflow open-source platform. The model detects potential fire regions in the images and provides results that support decision making and fire propagation analysis. The proposed approach improves the efficiency and speed of wildfire monitoring, helping authorities take timely actions to reduce environmental damage and enhance emergency response.