Wildfire Detection and Monitoring: A Drone-Based Approach and Comparative Analysis

Abstract

Wildfires are an increasing global threat, exacerbated by climate change. Traditional detection methods, such as ground patrols and satellites, often face delays, limited coverage, or high costs. This paper presents Evolonic, an autonomous drone-based wildfire detection system developed by Fraunhofer IISB, Fraunhofer IIS and Friedrich- Alexander University Erlangen-Nuremberg. The system integrates a fixed-wing VTOL UAV, computer vision- based smoke detection, a web-based alert platform, and an automated base station for continuous operations. A comparative analysis with existing detection technologies (sensor networks, cameras, and satellites) highlights the advantages of UAV-based monitoring in detection speed, flexibility, and real-time verification. Initial results confirm its potential for rapid wildfire detection and improved emergency response. While challenges remain, such as regulatory constraints and nighttime detection, this research demonstrates UAVs' role in enhancing wildfire monitoring and response, offering a scalable and adaptable solution to mitigate wildfire risks.