Streamlined Edge Computing for Fire Science and Management using WIFIRE Edge

In recent years, frequent and highly destructive megafires become one of the biggest climate-induced disasters. Fire behavior models using data from many emerging sources can inform decision support tools to respond to and mitigate such megafires. Emerging edge sensing and computing technologies within the fire environment can enhance the speed, reliability, and efficiency of wildland fire management, leading to better prevention, faster response times, and more effective mitigation of fire-related disasters. However, a unified system that streamlines the integration of edge technology advances within fire science and management workflows is needed. This paper presents the design and demonstrated case studies of the WIFIRE Edge Platform that facilitates the integration of sensing and AI capabilities at the edge. The initial attack and prescribed burn concept scenarios are described, highlighting the sensor deployment and utilization at the fire front.