Serverless Computing Architecture for Enhanced Martian Aurora Detection in the Emirates Mars Mission
- 1Universidad Complutense de Madrid, Facultad de Informática, Arquitectura de Computadores y Automática, Spain (dpacios@ucm.es, lvazquez@fdi.ucm.es, rmoreno@ucm.es, jjgomez@ucm.es)
- 2Center for Space Science, New York University Abu Dhabi, Saadiyat Marina District , Abu Dabi, 129188, Abu Dabi, United Arab Emirates (dbd7602@nyu.edu, atri@nyu.edu)
- 3University of California, Gauss Way, 7, Berkeley, 94720, California, United States of America (rlillis@berkeley.edu)
- 4Quantum Innovation Pc., Aristotelous, 84, Chania, 73100, Greece (nsx@alma-sistemi.com)
- 5Alma Sistemi Srl, University of California, Via dei Nasturzi n.4, Guidonia Montecelio, 00012, Lazio, Italy ( adi@alma-sistemi.com, lvazquez@fdi.ucm.es)
- 6School of Production Engineering and Management, Computational Mechanics and Optimization Laboratory, Technical University of Crete, 73100 Chania, Greece (nsx@alma-sistemi.com)
This work introduces a novel serverless computing architecture designed to analyze Martian auroras for the Emirates Mars Mission (Hope probe). Utilizing OpenCV and machine learning algorithms, the architecture offers efficient and scalable image classification, object detection, and segmentation. It leverages cloud computing's scalability and elasticity, handling large volumes of image data and adapting to varying workloads. Our study highlights the system's capacity to process and analyze images of Martian auroras swiftly while maintaining cost-effectiveness. The application of this technology within the HOPE Mission not only addresses the complexities involved in detecting Martian auroras but also sets a precedent for future remote sensing applications. Our results demonstrate the potential of serverless computing in enhancing the analysis of extraterrestrial phenomena and contributing significantly to planetary science.
This contribution has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.101007638 (Project EYE - Economy bY spacE) .
How to cite: Pacios, D., Vázquez-Poletti, J. L., Dhurri, D. B., Atri, D., Moreno Vozmediano, R., Lillis, R. J., Schetakis, N., Gómez-Sanz, J., Di Iorio, A., and Vazquez, L.: Serverless Computing Architecture for Enhanced Martian Aurora Detection in the Emirates Mars Mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9364, https://doi.org/10.5194/egusphere-egu24-9364, 2024.
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