A serverless computing architecture for Martian aurora detection with the Emirates Mars Mission

Abstract

Remote sensing technologies are experiencing a surge in adoption for monitoring Earth’s environment, demanding more efficient and scalable methods for image analysis. This paper presents a new approach for the Emirates Mars Mission (Hope probe); A serverless computing architecture designed to analyze images of Martian auroras, a key aspect in understanding the Martian atmosphere. Harnessing the power of OpenCV and machine learning algorithms, our architecture offers image classification, object detection, and segmentation in a swift and cost-effective manner. Leveraging the scalability and elasticity of cloud computing, this innovative system is capable of managing high volumes of image data, adapting to fluctuating workloads. This technology, applied to the study of Martian auroras within the HOPE Mission, not only solves a complex problem but also paves the way for future applications in the broad field of remote sensing.

EMM EMUS Observations of FUV Aurora on Mars: Dependence on Magnetic Topology, Local Time, and Season

“…Lillis et al (2022) classified the auroral features captured by EMUS as crustal field aurora in regions of strong crustal magnetic fields (e.g., Figure 1a), non-crustal field sinuous aurora which are elongated, filamentary emissions usually formed away from strong crustal fields (e.g., Figure 1b), and non-crustal field patchy aurora in spatially extended weak crustal field regions often with less defined edges (e.g., Figure 1c). Note that all three types of aurora can appear simultaneously at different locations on the nightside (Atri et al, 2022;Harada et al, 2024;Lillis et al, 2022;Pacios et al, 2024). However, these studies used limited data sets and did not provide in-depth analyses of Mars' FUV aurora, its variability, and drivers.…”