Creating a Safety Assurance Case for a Machine Learned Satellite-Based Wildfire Detection and Alert System

Abstract

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.

The Safety of Autonomy: A Systematic Approach

“…Here, AMLAS was used on the ML elements to demonstrate safety, which includes giving accurate predictions of the size and locations of wildfires, necessary for the safety of those fighting the fires. 22 An example of the independent use of AMLAS (involving none of the AMLAS developers) was for the safety assurance of an emergency braking system for an AV, intended to protect pedestrians. 23 This can be seen as an example of assuring the SOTIF.…”

“…25 While there have been some initial successes in applying the approach, there remain limitations and issues of maturity. As with the standards, our approach is not precise about the level of evidence needed for the safety casealthough both the definitions of SACE 11 and AMLAS 12 and the examples of the use of AMLAS 22,23,24 illustrate the approach and thus assist in interpreting and applying it. But this is work in progress, and there is more to be done.…”

Trustworthiness Assurance Assessment for High-Risk AI-Based Systems

“…Advancements have been made in safety assurance cases for machine learning components, taking requirements, data management, and model learning into account. Yet, these approaches are currently limited to specific machine learning implementations tailored for particular use cases [67]. Robustness, monitoring, steering of ML systems, and hazard reduction for deployment are identified as primary challenges for ML systems in terms of safety [68], [69].…”