Edge-computing for Real-time and Persistent Automated, Multi-sensor Collection, Processing, and Dissemination of Imagery-derived Fire Line and Hotspot Detections

Fire location direction speed and intensity are critical pieces of information when planning for andengaging in forest wildland firefighting operations. Real-time and post mission imagery areavailable to Incident Management Teams but are generally not disseminated to front linefirefighters. Image-derived fire maps are used to plan and guide operations; however the imagery isgenerally collected at night hours before missions and the resulting maps are not widelydistributed. The lack of broadly distributed data is likely attributable to the sizes and volumes ofavailable 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 interfacewith common optical sensors and communication equipment used on aircraft. Custom MachineLearning (ML) algorithms and software will locate and characterize fire lines and hotspots oncollected imagery and convert these detections into small files in common Geographic InformationSystem (GIS) formats. These files will be useable by a variety of COP visualization tools such asCivTAK Enterprise Global Portal (EGP) and Skyward's Pursuer application. The device will meetthe requirements for use on Unmanned Aerial Vehicles (UAVs) and manned aircraft to ensurebroad platform suitability. The result will be a widely usable tool which produces small real-timeimage-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 learningapproaches to identify optimal solutions for Phase II.