# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 1 Introduction  
Fire detection is a critical component of fire safety and prevention, playing a pivotal role in mitigating the devastating consequences of fires. Fires, whether in residential, industrial, or natural settings, pose a significant threat to life, property, and the environment. Rapid and accurate detection of fires is essential to enable timely response measures that can minimize damage, save lives, and safeguard valuable resources. Forest fires have emerged as a significant issue, exemplified in the context of various countries such as Algeria, the USA, and Canada in 2023. The prevalence of combustible materials like shrublands and forests, coupled with a climate conducive to ignition and rapid spread, contributes to the widespread occurrence of fires [1]. Numerous fires ravaged the Mediterranean vegetation, resulting in the destruction of valuable olive trees. The scarcity of water resources posed challenges for distant villages in combating the fires. Some residents fled their homes, while others made valiant attempts to control the flames using rudimentary tools, such as buckets and branches, as the availability of firefighting aircraft was limited. Regrettably, this catastrophe stands as one of the most severe in recent memory [2; 3]. The need for effective fire detection systems has become increasingly apparent in recent years due to various factors, including the rising frequency and severity of wildfires, climate change-induced environmental shifts, and the expanding urban landscape. As fires continue to emerge as a global concern, the development and deployment of advanced firedetection technologies have gained considerable attention. Historically, various methodologies have been employed to detect forest fires, including smoke and thermal detectors [4, 5], manned aircraft [6], and satellite imagery [7]. However, each of these techniques carries its limitations, as highlighted in [8]. For instance, smoke and thermal sensors necessitate proximity to the fire, failing to determine the fire's dimensions or location. Ground-based equipment might have restricted surveillance coverage, while the deployment of human patrols becomes impractical in expansive and remote forest areas. Satellite imagery, although valuable, falls short in detecting nascent fires due to insufficient clarity and an inability to provide continuous forest monitoring, primarily due to constrained path planning flexibility. The use of manned aircraft entails high costs and demands skilled pilots, who are exposed to hazardous environments and the risk of operator fatigue, as indicated by [9]. In recent times, an extensive body of research has been conducted in the domain of utilizing unmanned aerial vehicles (UAVs) for monitoring and detecting fires [10]. Within the context of forest fire detection endeavors, UAVs have the potential to fulfill several roles. Initially, UAVs were employed to navigate forest areas, capture video footage, and subsequently analyze the recordings to ascertain the occurrence of fires. As technological advancements have unfolded in the UAV domain, cost-effective commercial UAVs have become accessible for a multitude of research ventures. These UAVs are capable of entering high-risk zones, delivering an elevated vantage point over challenging terrain, and executing nocturnal missions devoid of endangering human lives. The integration of UAVs into these operations presents an array of substantial advantages [11]. * They can cover expansive regions, even in various weather conditions. * Their operational scope encompasses both day and night periods, with extended mission durations. * They offer ease of retrieval and relative cost-effectiveness compared to alternative methods. * In the case of electric UAVs, an additional environmental benefit is realized. * These UAVs can transport diverse and sizable payloads, catering to varied missions and benefiting from space and weight efficiencies due to the absence of pilot-related safety gear.