The Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) on board Mars Express has been sampling the topside ionosphere of Mars since mid-2005. The analysis of the main reflection (nadir) of the ionosphere through the ionograms provided by the MARSIS instrument is typically performed manually due to the high noise level in the lower frequencies. This task, which involves pattern recognition, turns out to be unfeasible for the >2 million ionograms available at the European Planetary Science Archive. In the present contribution, we propose a modular architecture based on serverless computing (a paradigm that stands on the cloud) for optimal processing of these ionograms. In particular, we apply serverless computing to detect oblique echoes in the ionosphere, which are nonnadir reflections produced when MARSIS is sounding regions above or nearby crustal magnetic fields, where the ionosphere loses the spherical symmetry. Oblique echoes are typically observed at similar frequencies to the nadir reflections but at different times delays, sometimes even overlaying the nadir reflection. Oblique echoes are difficult to analyze with the standard technique due to their nonconstant and highly variable appearance, but they harbor essential information on the state of the ionosphere over magnetized regions. In this work we compare the proposed serverless architecture with two local alternatives while processing a representative data subset and finally provide a study by means of cost and performance.

Satellite image analysis is a critical component of Earth observation and satellite data analysis, providing detailed information on the effects of global events such as the COVID-19 pandemic. Cloud computing offers a flexible way to allocate resources and simplifies the management of infrastructure. In this study, we propose a cross-cloud system for ML-based satellite image detection, focusing on the financial and performance aspects of utilizing Amazon Web Service (AWS) Lambda and Amazon SageMaker for advanced machine learning tasks. Our system utilizes Google Apps Script (GAS) to create a web-based control panel, providing users with access to our AWS-hosted satellite detection models. Additionally, we utilize AWS to manage expenses through a strategic combination of Google Cloud and AWS, providing not only economic advantages, but also enhanced resilience. Furthermore, our approach capitalizes on the synergistic capabilities of AWS and Google Cloud to fortify our defenses against data loss and ensure operational resilience. Our goal is to demonstrate the effectiveness of a cloud environment in addressing complex and interdisciplinary challenges, particularly in the field of object analysis using spatial imagery.

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.

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) .