Advanced Wildfire Detection Using Deep Learning Algorithms: A Comparative Study of CNN Variants

Abstract

This paper presents a new approach for wildfire detection using advanced deep learning algorithms, including computer vision by evaluating the performance of different processes on airborne satellite imagery that produces dens imposed by wildfire events. The algorithm used is Convolutional Neural Networks (CNN) and its advanced variants in the monitoring environment: InceptionV3, DenseNet121, Xception, MobileNetV2, and NASNetMobile. Using the powerful capabilities of these algorithms, we thoroughly analyze extracted features from images to improve detection accuracy to improve performance we introduce additional techniques such as advanced data enhancement to prevent overfitting, adjusting the number of studies to support model convergence, fine-. Gradually unfreezing the layers for adjustment, and using class weights to deal with data set imbalances This study uses a well-curated dataset to train and test models, and provides detailed analysis of their performance in wildfire detection is possible, including accuracy, recall, and F1 scores The addition of these different algorithms to metrics provides a better understanding of their comparative advantages and limitations a it is available in wildfire detection, enhances environmental monitoring and provides valuable insights in selecting optimal algorithms for similar classification task.