Satellite Image Classification for Early Wildfire Detection Using Deep Learning

Wildfires have emerged as one of the most pressing environmental hazards, fueled by climate change, deforestation, and human activity. Early detection is critical to reducing damage to ecosystems and human settlements, yet traditional monitoring methods such as ground sensors or manual observation are slow and limited in scope. Recent advances in deep learning have shown promise, but many approaches rely on heavy segmentation or object detection models that are unsuitable for real-time or resource-constrained environments.

This study proposes a lightweight wildfire detection framework using EfficientNetV2-S with transfer learning for binary image classification. A publicly available satellite image dataset was preprocessed through resizing, normalization, and train–test splitting. Transfer learning was applied by freezing pretrained ImageNet weights and fine-tuning the classifier head for two categories: fire and no-fire. The model was trained using the AdamW optimizer with cross-entropy loss and evaluated through accuracy, precision, recall, F1-score, and confusion matrices.

The system achieved 97.54% validation accuracy and 92.65% test accuracy, with balanced precision and recall across both classes. Robustness testing on real-world satellite images confirmed strong generalization, while inference times of 15–20 ms per image demonstrated real-time viability. Unlike heavier segmentation-based pipelines, this lightweight model can be deployed on drones, edge devices, and early-warning platforms.

In conclusion, EfficientNetV2-S provides an efficient, accurate, and scalable solution for wildfire detection, offering a deployable alternative to computationally intensive models and supporting rapid-response systems for disaster prevention.