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A serverless computing architecture for Martian aurora detection with the Emirates Mars Mission
David Pacios1, José Luis Vázquez-Poletti2, Dattaraj B Dhuri3
1Facultad de Informática, Universidad Complutense de Madrid, Calle del Prof. José García Santesmases, 9, 28040, Madrid, Spain.
Scientific Reports
|February 6, 2024
Summary
This study introduces a serverless cloud architecture for analyzing Martian auroras from the Emirates Mars Mission (Hope probe). This approach enhances remote sensing image analysis efficiency and cost-effectiveness for planetary science.
Area of Science:
- Planetary Science
- Remote Sensing
- Computer Science
Background:
- Remote sensing is crucial for environmental monitoring, requiring scalable image analysis methods.
- Understanding Martian auroras is key to studying the Martian atmosphere.
Purpose of the Study:
- To develop an efficient and scalable serverless computing architecture for analyzing Martian aurora images from the Emirates Mars Mission (Hope probe).
Main Methods:
- Utilized serverless cloud computing architecture.
- Employed OpenCV and machine learning algorithms for image classification, object detection, and segmentation.
- Leveraged cloud scalability and elasticity for high-volume data management.
Main Results:
- Achieved swift and cost-effective image analysis of Martian auroras.
- Demonstrated the system's capability to manage fluctuating workloads and large datasets.
- Successfully applied the architecture to the Hope Mission's data.
Conclusions:
- The developed serverless architecture provides an efficient solution for analyzing Martian aurora images.
- This approach enhances remote sensing capabilities for planetary science missions.
- The technology has potential for broader applications in remote sensing image analysis.
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