Fire location, direction, speed and intensity are critical pieces of information when planning for and engaging in forest wildland firefighting operations

Real-time and post mission imagery are available to incident management teams, but are generally not disseminated to front line firefighters. Image-derived fire maps are used to plan and guide operations; however, the imagery is generally collected at night, hours before missions and the resulting maps are not widely distributed. The lack of broadly distributed data is likely attributable to the sizes and volumes of available data and a deficiency in commonly used field deployable common operating picture (COP) visualization applications.skyward will address these challenges by developing a small edge-computing device to interface with common optical sensors and communication equipment used on aircraft. Custom machine learning (ML) algorithms and software will locate and characterize fire lines and hotspots on collected imagery andconvert these detections into small files in common geographic information system (GIS) formats. These files will be useable by a variety of COP visualization tools such as civtak, enterprise global portal (EGP), and skyward's pursuer application. The device will meet the requirements for use on unmanned aerial vehicles (UAVS) and manned aircraft to ensure broad platform suitability. The result will be a widely usable tool which produces small, real-time, image-derived fire detection GIS files for dissemination to a variety of COP applications.during phase I, skyward will benchmark and evaluate hardware, software, and machine learning approaches to identify optimal solutions for phase II.