FEASIBILITY STUDY ON THE CHARACTERIZATION OF MARS ATMOSPHERIC AND SURFACE FEATURES IN SPACECRAFT IMAGES BY MACHINE LEARNING AND SIMILAR AUTOMATED METHODS

Aug 5, 2026

Automated detection of Martian atmospheric and surface features from spacecraft imagery is investigated through machine learning and related automated approaches using a crater detection algorithm (CDA) software called DeepMars2. The study performs machine and deep learning to identify coarse- and high-resolution topography craters on Mars by applying the method to two digital elevation models (DEMs). The DEMs are derived from MOLA/MGS and HRSC/MEX instruments with differing resolutions, validated using the Robbins and Hynek crater catalogue. Results indicate better matches for high-resolution topography than coarse resolution by 1,686 craters.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages51

Reading Time2 h 9 min

AccessFree to download

Abstract

Automated detection approach

Data sources and DEM resolution

Validation and crater matching results

United Arab Emirates University Scholarworks@UAEU | Theses | Electronic Theses and Dissertations | | --- | --- | 3-2023 FEASIBILITY STUDY ON THE CHARACTERIZATION OF MARS ATMOSPHERIC AND SURFACE FEATURES IN SPACECRAFT IMAGES BY MACHINE LEARNING AND SIMILAR AUTOMATED METHODS Hind Hassan AlRiyami Follow this and additional works at: [https://scholarworks.uaeu.ac.ae/all_theses](https://scholarworks.uaeu.ac.ae/all_theses) Part of the Physics Commons MASTER THESIS NO. 2023: 22 College of Science Department of Physics FEASIBILITY STUDY ON THE CHARACTERIZATION OF MARS ATMOSPHERIC AND SURFACE FEATURES IN SPACECRAFT IMAGES BY MACHINE LEARNING AND SIMILAR AUTOMATED METHODS Hind Hassan Ali MattarAlRiyami United Arab Emirates University College of Science Department of Physics FEASIBILITY STUDY ON THE CHARACTERIZATION OF MARS ATMOSPHERIC AND SURFACE FEATURES IN SPACECRAFT IMAGES BY MACHINE LEARNING AND SIMILAR AUTOMATED METHODS Hind Hassan Ali Matar AlRiyami This thesis is submitted in partial fulfilment of the requirements for the degree of Master of Science in Space Science March 2023 United Arab Emirates University Master Thesis 2023: 22 Cover: A plot of crater distribution of coarse and high resolution digital elevation model Imported by Java Mission-planning and Analysis for Remote Sensing (JMARS) (Photo: By Hind Hassan Al Riyami) © 2023 Hind Hassan Al Riyami, Abu Dhabi, UAE All Rights Reserved Print: University Print Service, UAEU 2023 Declaration of Original Work I, Hind Hassan Ali AlRiyami, the undersigned, a graduate student at the United Arab Emirates University (UAEU), and the author of this thesis entitled “Feasibility Study On The Characterization Of Mars Atmospheric And Surface Features In Spacecraft Images By Machine Learning And Similar Automated Methods” hereby, solemnly declare that this thesis is my own original research work that has been done and prepared by me under the supervision of Dr. Claus Gebhardt, in the College of Science at UAEU. This work has not previously formed the basis for the award of any academic degree, diploma or a similar title at this or any other university. Any materials borrowed from other sources (whether published or unpublished) and relied upon or included in my thesis have been properly cited and acknowledged in accordance with appropriate academic conventions. I further declare that there is no potential conflict of interest with respect to the research, data collection, authorship, presentation and/or publication of this thesis. Student’s Signature: Date: 21 March 2023 Advisory Committee 1) Advisor: Abdelgadir Abuelgasim Title: Associate Professor Department of Geography and Urban Sustain College of Humanities and Social Science 2) Co-advisor: Claus Gebhardt Title: Assistant Professor Department of Physics College of Science 3) Member: Christopher Lee Title: Tenure-Stream Faculty Department of Physics University of Toronto, Canada Approval of the Master Thesis This Master Thesis is approved by the following Examining Committee Members: 1) Advisor (Committee Chair): Abdelgadir Abuelgasim Title: Associate Professor Department of Geography and Urban Sustainability College of Humanities and Social Science Signature Date 8 May 2023 2) Member: Claus Gebhardt Title: Assistant Professor Department of Physics College of Science Signature Date 15 May 2023 3) Member (External Examiner): Christopher Lee Title: Tenure-Stream Faculty Department of Physics College of University of Toronto, Canada Signature Date 15 May 2023 This Master Thesis is accepted by: Dean of the College of Science: Professor Maamar Benkraouda Signature Date May 24, 2023 Dean of the College of Graduate Studies: Professor Ali Al-Marzouqi Signature Date 24/05/2023 Abstract This thesis is focused on automated detection of Mars atmospheric and surface features in spacecraft images based on machine lea

FAQs

What is the main objective of the thesis?

To assess the feasibility of automatically detecting Martian atmospheric and surface features in spacecraft images using machine learning and similar automated methods.

Which algorithm and software are used for crater detection?

The study uses a crater detection algorithm (CDA) software named DeepMars2.

What data and ground-truth are used to evaluate the method?

Two Mars digital elevation models (from MOLA/MGS and HRSC/MEX) are tested, and the Robbins and Hynek crater catalogue provides ground-truth data.

Aug 5, 2026

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