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—ResNet50, ResNet101, and EfficientNetB0—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 128 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 0.5 has been incorporated. Finally, a Dense layer with 2 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 42,850 satellite images, categorized into wildfire and nowildfire areas. Experimental results indicate that the ResNet101 model achieved the highest accuracy of 99.60%, while ResNet50 and EfficientNetB0 achieved accuracies of 99.35% and 99.10%, respectively. These results highlight the high potential of deep learning-based methods in improving wildfire detection accuracy and their role in environmental crisis management.