Title: Firefighter Drones
Presenting Author: Fatemeh Afghah
Organization: Clemson University
Co-Author(s): Hossein Rajoli, Xiaolong Ma, Eric Rowell, Janice Coen, Alireza Tavakoli

Abstract:
The project leverages Unmanned Aerial Systems (UAS) to overcome existing limitations faced in wildfire monitoring, primarily bandwidth constraints and the dependency on human operators for piloting individual drones. By employing a hierarchical platform of multiple drones, the initiative seeks to offer long-term coverage of wildland fires. It aims to develop low-computation, real-time collaborative learning methods for onboard fire detection and mapping, enabling drones to autonomously navigate challenging terrains and atmospheric conditions. The project addresses the critical need for high-resolution, frequent coverage in high-risk fire zones by integrating sophisticated data fusion techniques and deep learning algorithms. These technologies are designed to autonomously navigate drones through challenging environments, enhancing the precision of fire detection and behavior prediction. The synergy of AI-processed data with physics-based simulations will provide a comprehensive, data-rich operational tool for fire management teams. This initiative stands at the forefront of integrating artificial intelligence with advanced drone technology, transforming wildfire management into a more precise and efficient practice. By refining the capabilities of drones for enhanced fire detection and mapping, the project enables a deeper understanding of its dynamics, fostering improved strategies for environmental monitoring and disaster mitigation. This approach exemplifies a powerful synergy between technology and science, highlighting a progressive step toward addressing complex ecological challenges.