Hawkins, Richard David orcid.org/0000-0001-7347-3413, Picardi, Chiara, Donnell, Lucy et al. (1 more author) (2023) Creating a Safety Assurance Case for a Machine Learned Satellite-Based Wildfire Detection and Alert System. Journal of Intelligent & Robotic Systems. 47. ISSN 0921-0296
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2023 |
Keywords: | Machine Learning,Safety Case,Safety assurance,Wildfire,Satellite |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 07 Jun 2023 08:00 |
Last Modified: | 28 Oct 2024 01:05 |
Published Version: | https://doi.org/10.1007/s10846-023-01905-3 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1007/s10846-023-01905-3 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200066 |