为ML卫星野火探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
摘要：
野火是世界许多地区的常见问题，往往会造成灾难性后果。已经建立了一些系统来提供野火预警，包括那些使用卫星数据来探测火灾的系统。由于立方体卫星等小型卫星的可用性增加，通过在感兴趣的区域部署多个卫星星座，可以缩短野火探测响应时间。通过使用卫星上的机器学习组件，可以克服限制可以处理并发送回地面站的数据量的限制。野火警报系统存在与野火警报系统相关的危险，例如未能检测到野火的存在，或者在错误的位置检测到野火。因此，有必要能够为野火警报ML组件创建一个安全保证案例，以证明它的使用足够安全。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，其中包含关于机器学习安全性的明确参数和证据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
