The intensity and frequency of bushfires have increased significantly, destroying property and living species in recent years. Presently, unmanned aerial vehicle (UAV) technology advancements are becoming increasingly popular in bushfire management systems because of their fundamental characteristics, such as manoeuvrability, autonomy, ease of deployment, and low cost. UAVs with remote-sensing capabilities are used with artificial intelligence, machine learning, and deep-learning algorithms to detect fire regions, make predictions, make decisions, and optimize fire-monitoring tasks. Moreover, UAVs equipped with various advanced sensors, including LIDAR, visual, infrared (IR), and monocular cameras, have been used to monitor bushfires due to their potential to provide new approaches and research opportunities. This review focuses on the use of UAVs in bushfire management for fire detection, fire prediction, autonomous navigation, obstacle avoidance, and search and rescue to improve the accuracy of fire prediction and minimize their impacts on people and nature. The objective of this paper is to provide valuable information on various UAV-based bushfire management systems and machine-learning approaches to predict and effectively respond to bushfires in inaccessible areas using intelligent autonomous UAVs. This paper aims to assemble information about the use of UAVs in bushfire management and to examine the benefits and limitations of existing techniques of UAVs related to bushfire handling. However, we conclude that, despite the potential benefits of UAVs for bushfire management, there are shortcomings in accuracy, and solutions need to be optimized for effective bushfire management.

Autonomous navigation in forest environments poses significant challenges for Unmanned Aerial Vehicles (UAVs), where reliable obstacle detection is essential for safe operation. This study presents a detailed performance evaluation of recent YOLO models (v8–v11) using a realistic simulation environment built on Unreal Engine and AirSim. To the best of our knowledge, this is the first study to comprehensively benchmark YOLOv8–v11 in a UAV-based obstacle detection context, incorporating both onboard deployment factors and the impact of transfer learning (TL) using EfficientNet backbones. We analyze detection accuracy (mAP@50, precision, recall), inference time, FPS, and memory usage across all YOLO configurations, from lightweight (YOLOv8s) to high-performance models (YOLOv11x). TL significantly improved model performance; for instance, YOLOv8s showed a recall increase from 0.6318 to 0.7804 and mAP@50 from 0.7228 to 0.8447. YOLOv8m and YOLOv10b emerged as optimal models, offering the best trade-off between speed and detection accuracy for real-time UAV deployment. Larger models like YOLOv9c and YOLOv10x delivered superior accuracy but required more computational resources. The results offer practical guidance for selecting YOLO architectures in UAV-based obstacle detection tasks, balancing accuracy, speed, and deployment feasibility in complex environments.

Accurate and real-time fire area estimation is critical for effective wildfire monitoring and response. This study presents a simulation-based framework for real-time fire area estimation using UAV-acquired RGB imagery, introducing two lightweight modelling approaches: a contour-based geometric method and a Ground Sampling Distance (GSD)-based scaling technique. The framework was implemented and validated in a high-fidelity simulation environment using Unreal Engine 4.27 and AirSim, and evaluated across multiple fire sizes, UAV altitudes, and positions. In addition, a controlled outdoor validation experiment was conducted to assess the transferability of the proposed methods under real-world imaging conditions. Performance was assessed using statistical error metrics (RMSE, R²) and latency analysis to ensure operational viability. Results demonstrate that the GSD-based method achieves higher consistency across varying scales, while the contour-based method offers ultra-low latency for rapid situational awareness. This work contributes a validated simulation modelling approach that can be integrated into UAV based wildfire monitoring systems, bridging the gap between proof-of-concept and operational deployment.