Transfer Learning-Enhanced Gradient Boosting Models for Wildfire Detection Using UAV Imagery

Wildfires pose a serious hazard to both life and the environment, necessitating accurate and timely detection methods to minimize their impact. However, traditional approaches have limitations in precision, response time, and adaptability to environmental factors. To address this gap, machine learning (ML) and deep learning (DL) approaches are increasingly being adopted for fire detection. This study proposes a hybrid approach that combines deep feature extraction using Transfer Learning (TL) with EfficientNetB0 and traditional Gradient Boosting Models (GBM), specifically LightGBM, XGBoost, and CatBoost, for wildfire detection using UAV imagery. EfficientNetB0 is used to extract features, which are then dimensionally reduced using Principal Component Analysis (PCA). Optuna framework is used for hyperparameter tuning, to maximize the performance of the model. The results show that the GBM models enhanced with TL outperform their baseline counterparts, with CatBoost + TL achieving the highest precision (92. 37%). These findings confirm the role of TL in improving model precision, efficiency, and computational resource utilization, making it ideal for real-time wildfire detection.