Enhanced Deep Learning Approaches for Wildfire Detection Using Satellite Imagery

wildfires are among the most serious environmental and socio-economic threats worldwide, significantly impacting ecosystems and climate patterns. In recent years, deep learning-based methods, particularly Convolutional Neural Networks (CNNs), have played a crucial role in improving wildfire detection accuracy. This study presents an enhanced approach for identifying wildfire-affected areas using deep learning models. Specifically, three models—ResNet۵۰, ResNet۱۰۱, and EfficientNetB۰—have been examined. To improve accuracy and reduce model complexity, the Flatten layer in all three architectures has been replaced with a Global Average Pooling (GAP) layer. This modification reduces the number of features and enhances the extraction of meaningful patterns from images. Additionally, a Dense layer with ۱۲۸ neurons has been added after the GAP layer to enhance the learning and integration of the extracted features. To prevent overfitting, a Dropout layer with a rate of ۰.۵ has been incorporated. Finally, a Dense layer with ۲ neurons serves as the output layer, responsible for the final classification. These optimizations have led to improved model accuracy and enhanced performance in wildfire detection. The dataset used consists of ۴۲,۸۵۰ satellite images, categorized into wildfire and nowildfire areas. Experimental results indicate that the ResNet۱۰۱ model achieved the highest accuracy of ۹۹.۶۰%, while ResNet۵۰ and EfficientNetB۰ achieved accuracies of ۹۹.۳۵% and ۹۹.۱۰%, respectively. These results highlight the high potential of deep learning-based methods in improving wildfire detection accuracy and their role in environmental crisis management.

کلیدواژه ها:

wildfire detection ، deep learning ، ResNet۵۰ ، ResNet۱۰۱ ، EfficientNetB۰