Wildfire Detection Using Convolutional Neural Network

Abstract

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. The project achieves a final accuracy of 96.36% and a loss of 0.1013 in fire segmentation, with training accuracy at 95.12% and a loss of 0.1418. During validation, the model reaches a final accuracy of 94.54% with a loss of 0.2612 in fire classification. The outcomes demonstrate the potential of deep learning in improving wildfire response plans, and early warning systems, and reducing devastation. 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.