Wildfire Detection using Deep Learning

In order to lessen the destructive effects of uncontrolled wildfires on both human settlements and natural ecosystems, wildfire detection become more important. This project focuses on leveraging deep learning techniques for the detection of wildfires, emphasizing their severe threat to property, human life, and ecosystems. Timely and accurate wildfire detection is crucial for effective response and mitigation. The study utilizes convolutional neural networks (CNNs) to analyze diverse data sources like satellite photos, aerial images, and real-time video feeds, eliminating the need for human feature engineering. FLAME (Fire Luminosity Airborne based Machine Learning Evaluation) dataset along with fire detection and segmentation methods have been used for the proposed system. The project achieves a final accuracy of 96.36% in fire segmentation, with a training accuracy of 95.12%. During validation, the model reaches a final accuracy of 94.54% in fire classification. The outcomes demonstrate the potential of deep learning in improving wildfire response plans, and early warning systems. The project underscores the importance of continuous research and development in advancing real-time monitoring and proactive wildfire management strategies for securing lives, property, and natural surroundings.