Revolutionizing Wildfire Detection with Convolutional Neural Networks: A VGG16 Model Approach
This work addresses the need for efficient early warning systems to reduce wildfire damage, though it is incremental as it adapts an existing method to a specific domain.
The study tackled wildfire detection by applying a VGG16-based convolutional neural network to the D-FIRE dataset, achieving a low false negative rate to minimize missed fires.
Over 8,024 wildfire incidents have been documented in 2024 alone, affecting thousands of fatalities and significant damage to infrastructure and ecosystems. Wildfires in the United States have inflicted devastating losses. Wildfires are becoming more frequent and intense, which highlights how urgently efficient warning systems are needed to avoid disastrous outcomes. The goal of this study is to enhance the accuracy of wildfire detection by using Convolutional Neural Network (CNN) built on the VGG16 architecture. The D-FIRE dataset, which includes several kinds of wildfire and non-wildfire images, was employed in the study. Low-resolution images, dataset imbalance, and the necessity for real-time applicability are some of the main challenges. These problems were resolved by enriching the dataset using data augmentation techniques and optimizing the VGG16 model for binary classification. The model produced a low false negative rate, which is essential for reducing unexplored fires, despite dataset boundaries. In order to help authorities execute fast responses, this work shows that deep learning models such as VGG16 can offer a reliable, automated approach for early wildfire recognition. For the purpose of reducing the impact of wildfires, our future work will concentrate on connecting to systems with real-time surveillance networks and enlarging the dataset to cover more varied fire situations.