Revolutionizing Wildfire Detection with Convolutional Neural Networks: A VGG16 Model Approach

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.

Wildfire Detection Using Vision Transformer with the Wildfire Dataset

The critical need for sophisticated detection techniques has been highlighted by the rising frequency and intensity of wildfires in the US, especially in California. In 2023, wildfires caused 130 deaths nationwide, the highest since 1990. In January 2025, Los Angeles wildfires which included the Palisades and Eaton fires burnt approximately 40,000 acres and 12,000 buildings, and caused loss of human lives. The devastation underscores the urgent need for effective detection and prevention strategies. Deep learning models, such as Vision Transformers (ViTs), can enhance early detection by processing complex image data with high accuracy. However, wildfire detection faces challenges, including the availability of high-quality, real-time data. Wildfires often occur in remote areas with limited sensor coverage, and environmental factors like smoke and cloud cover can hinder detection. Additionally, training deep learning models is computationally expensive, and issues like false positives/negatives and scaling remain concerns. Integrating detection systems with real-time alert mechanisms also poses difficulties. In this work, we used the wildfire dataset consisting of 10.74 GB high-resolution images categorized into 'fire' and 'nofire' classes is used for training the ViT model. To prepare the data, images are resized to 224 x 224 pixels, converted into tensor format, and normalized using ImageNet statistics.