Abstract: Wildfires pose a significant threat to ecosystems, human settlements, and air quality, necessitating advanced detection and mitigation strategies. Traditional wildfire detection methods often rely on manual observation and conventional machine learning approaches, which may lack efficiency and accuracy. This study proposes a novel deep learning model based on the ConvNeXt-Small architecture, a hybrid design that fuses the strengths of Convolutional Neural Networks (CNNs) and Transformer-inspired mechanisms, enabling more comprehensive analysis of wildfire patterns in satellite imagery. The model was trained using the Adam optimizer, which provides efficient convergence and adaptive learning. The dataset used consists of real-world satellite images collected from wildfire-affected regions in Canada, covering various geographic and seasonal conditions to reflect real environmental diversity. The results underscore the potential of ConvNeXt-based architecture for real-time, high-precision wildfire detection, offering a powerful tool for early intervention, disaster mitigation, and environmental monitoring efforts.

Shereen Essam Elbohy, Mona M. Nasr and Farid Ali Mousa, “Fusion of CNN and Transformer Architectures for Proactive Wildfire Detection in Satellite Imagery” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160634

@article{Elbohy2025,
title = {Fusion of CNN and Transformer Architectures for Proactive Wildfire Detection in Satellite Imagery},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160634},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160634},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Shereen Essam Elbohy and Mona M. Nasr and Farid Ali Mousa}
}