论文标题
为基于ML卫星的野火检测和警报系统创建安全保证案例
Creating a Safety Assurance Case for an ML Satellite-Based Wildfire Detection and Alert System
论文作者
论文摘要
在世界许多地区，野火通常会带来灾难性的后果。已经创建了许多系统来提供野火的早期警告，包括使用卫星数据检测火灾的系统。小型卫星的可用性增加，例如立方体，使野火检测响应时间可以减少通过在感兴趣的区域内部署多个卫星的星座。通过在卫星上使用机器学习的组件，可以克服可以处理并发送回地面站的数据量的约束。与野火警报系统有关，例如未检测到野火的存在或在不正确的位置检测野火。因此，有必要为野火警报ML组件创建安全保证案例，以证明其足够安全。本文详细描述了如何创建ML野火警报系统的安全保证案例。这代表了ML组件的第一个完全开发的安全案例，其中包含有关机器学习安全性的明确论证和证据。
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.
