  Wildfires are a common problem in many areas of the world with often
catastrophic consequences. A number of systems have been created to provide
early warnings of wildfires, including those that use satellite data to detect
fires. The increased availability of small satellites, such as CubeSats, allows
the wildfire detection response time to be reduced by deploying constellations
of multiple satellites over regions of interest. By using machine learned
components on-board the satellites, constraints which limit the amount of data
that can be processed and sent back to ground stations can be overcome. There
are hazards associated with wildfire alert systems, such as failing to detect
the presence of a wildfire, or detecting a wildfire in the incorrect location.
It is therefore necessary to be able to create a safety assurance case for the
wildfire alert ML component that demonstrates it is sufficiently safe for use.
This paper describes in detail how a safety assurance case for an ML wildfire
alert system is created. This represents the first fully developed safety case
for an ML component containing explicit argument and evidence as to the safety
of the machine learning.
