## 2 Literature Review
### Discussion
#### 2.2.2 Unmanned Aerial Vehicles (UAV)
[PERSON] and [PERSON] designed a Fire Perception Box (FPB) with RGB/IR cameras and an ARM microcontroller that can be installed on UAVs in a plug-and-play manner [62]. At a suspected fire scene, the RGB image is first fed to a convolutional neural network (CNN) classifier to calculate an RGB fire score, followed by the IR image being fed to its corresponding pipeline to calculate an IR fire score. Both scores are combined to establish the presence of fire, and subsequently find the fire localization hotspots and RGB + IR heatmaps. Researchers have also demonstrated a large-scale YOLOv3- and YOLOv5-based deep-learning network for fire identification from images and video feeds captured by UAVs [63; 64]. After training the YOLO network on annotated fire images, the researchers were able to obtain high fire detection accuracies in real-time analysis of UAV video feeds. The only disadvantage of this method is that the YOLO model needs to be implemented on a ground-based high-performance computer, therefore the performance of this system is contingent on an uninterrupted data transmission between base station and UAVs. Besides these methods, other deep-learning methods such as recurrent neural networks (RNN), long short-term memory neural network (LSTM), generative adversarial network (GAN), deep belief network (DBN), etc., have also been shown to have good accuracy with identifying wildfires [65; 66]. Another active avenue of research is building efficient algorithms for coordinating the communication between multiple UAVs and controlling their flight patterns, with the aim of optimizing coverage of the area within the expected flight endurance period. The methods vary from classic linear controllers such as proportional-integral-derivative (PID), Hoo and linear quadratic regulators to complicated non-linear implementations, such as neural networks, genetic algorithms, etc. [67]. [PERSON] et al., designed a control framework by incorporating the following multiple objectives into the cost function: deployment of UAVs to the first location of fire detection, avoiding in-flight collision with other UAVs by maintaining a minimum separation distance, maximizing coverage of the wildfire and tracking progression of the fire front [68]. [PERSON] et al., discussed a real-time cooperative and adaptive fire monitoring method for multiple UAVs [69]. They trained a radial basis function neural network (RBFNN) to counteract system uncertainties, aerodynamic drag, etc. and a non-singular fast terminal sliding mode control (NFTSMC) function to dynamically track the fire. A similar evolutionary multi-objective algorithm was also formulated for dynamically tracking fire boundary with minimal resources, and the algorithm was able to achieve 100% fire coverage with 15 UAVs in the FARSITE-simulated model [70]. There have been numerous examples of detailed control strategies for quadrotors, but the detailed review of these methods is beyond the scope of this review paper. The reader is encouraged to review the papers by [PERSON] et al. [71] and [PERSON] et al. [72] for a study of such techniques. UAVs have limited flight endurance, as the size of the power source on board is constrained by the payload capacity of the UAV. To extend the flight time of UAVs, solar panels have been carefully integrated into the design. Airbus' Zephyr and Titan Aerospace's Solara set the record for longest flight times, with several days of autonomous flight. Both are equipped with solar panels and are intended to be used as high-altitude long-endurance (HALE) UAVs for atmospheric monitoring [54]. HALEs fly at altitudes of about 30,000 feet and cost millions of dollars. In contrast, solar-powered low-altitude low-endurance (LALE) UAVs have been able to achieve maximum flight times of a few days at an altitude of only <10,000 ft, with the record set by ETH Zurich's AtlantikSolar at 81 h of continuous flight [73].