[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126079-en":3,"doc-seo-126079-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126079,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","FEASIBILITY STUDY ON THE CHARACTERIZATION OF MARS ATMOSPHERIC AND SURFACE FEATURES IN SPACECRAFT IMAGES BY MACHINE LEARNING AND SIMILAR AUTOMATED METHODS","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.","United Arab Emirates University  \nScholarworks@UAEU  \n\n| Theses | Electronic Theses and Dissertations |\n| --- | --- |\n\n3-2023  \nFEASIBILITY STUDY ON THE CHARACTERIZATION OF MARS ATMOSPHERIC AND SURFACE FEATURES IN SPACECRAFT IMAGES BY MACHINE LEARNING AND SIMILAR AUTOMATED METHODS  \nHind Hassan AlRiyami  \nFollow this and additional works at: [https://scholarworks.uaeu.ac.ae/all_theses](https://scholarworks.uaeu.ac.ae/all_theses)  \n Part of the Physics Commons  \nMASTER THESIS NO. 2023: 22  \nCollege of Science  \nDepartment of Physics  \nFEASIBILITY STUDY ON THE CHARACTERIZATION OF MARS ATMOSPHERIC AND SURFACE FEATURES IN SPACECRAFT IMAGES BY MACHINE LEARNING AND SIMILAR AUTOMATED METHODS  \nHind Hassan Ali MattarAlRiyami  \nUnited Arab Emirates University  \nCollege of Science  \nDepartment of Physics  \nFEASIBILITY STUDY ON THE CHARACTERIZATION OF MARS ATMOSPHERIC AND SURFACE FEATURES IN SPACECRAFT IMAGES BY MACHINE LEARNING AND SIMILAR AUTOMATED METHODS  \nHind Hassan Ali Matar AlRiyami  \nThis thesis is submitted in partial fulfilment of the requirements for the degree of Master  \nof Science in Space Science  \nMarch 2023  \nUnited Arab Emirates University Master Thesis  \n2023: 22  \nCover: A plot of crater distribution of coarse and high resolution digital elevation model Imported by Java Mission-planning and Analysis for Remote Sensing (JMARS)  \n(Photo: By Hind Hassan Al Riyami)  \n© 2023 Hind Hassan Al Riyami, Abu Dhabi, UAE All Rights Reserved  \nPrint: University Print Service, UAEU 2023  \nDeclaration of Original Work  \nI, 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.  \nStudent’s Signature:  \nDate: 21 March 2023  \nAdvisory Committee  \n1) Advisor: Abdelgadir Abuelgasim  \nTitle: Associate Professor  \nDepartment of Geography and Urban Sustain College of Humanities and Social Science  \n2) Co-advisor: Claus Gebhardt Title: Assistant Professor Department of Physics College of Science  \n3) Member: Christopher Lee Title: Tenure-Stream Faculty Department of Physics University of Toronto, Canada  \nApproval of the Master Thesis  \nThis Master Thesis is approved by the following Examining Committee Members:  \n1) Advisor (Committee Chair): Abdelgadir Abuelgasim  \nTitle: Associate Professor  \nDepartment of Geography and Urban Sustainability  \nCollege of Humanities and Social Science  \nSignature  Date 8 May 2023   \n2) Member: Claus Gebhardt  \nTitle: Assistant Professor  \nDepartment of Physics  \nCollege of Science  \nSignature Date  15 May 2023  \n3) Member (External Examiner): Christopher Lee  \nTitle: Tenure-Stream Faculty  \nDepartment of Physics  \nCollege of University of Toronto, Canada  \nSignature  Date 15 May 2023  \nThis Master Thesis is accepted by:  \nDean of the College of Science: Professor Maamar Benkraouda  \nSignature  \nDate  \nMay 24, 2023  \nDean of the College of Graduate Studies: Professor Ali Al-Marzouqi  \nSignature     \nDate  24/05/2023   \nAbstract  \nThis thesis is focused on automated detection of Mars atmospheric and surface features in spacecraft images based on machine lea","cbCaismuDYZUFNud","https://ap.wps.com/l/cbCaismuDYZUFNud","pdf",3351658,7,1,51,"English","en",105,"# Abstract\n## Automated detection approach\n## Data sources and DEM resolution\n## Validation and crater matching results","[{\"question\":\"What is the main objective of the thesis?\",\"answer\":\"To assess the feasibility of automatically detecting Martian atmospheric and surface features in spacecraft images using machine learning and similar automated methods.\"},{\"question\":\"Which algorithm and software are used for crater detection?\",\"answer\":\"The study uses a crater detection algorithm (CDA) software named DeepMars2.\"},{\"question\":\"What data and ground-truth are used to evaluate the method?\",\"answer\":\"Two Mars digital elevation models (from MOLA/MGS and HRSC/MEX) are tested, and the Robbins and Hynek crater catalogue provides ground-truth data.\"}]","FEASIBILITY STUDY ON THE CHARACTERIZATION OF MARS ATMOSPHERIC AND SURFACE FEATURES IN SPACECRAFT IMAGES BY MACHINE LEARNING AND SIMILAR AUTOMATED METHODS | 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is the main objective of the thesis?","Question",{"text":77,"@type":78},"To assess the feasibility of automatically detecting Martian atmospheric and surface features in spacecraft images using machine learning and similar automated methods.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which algorithm and software are used for crater detection?",{"text":82,"@type":78},"The study uses a crater detection algorithm (CDA) software named DeepMars2.",{"name":84,"@type":75,"acceptedAnswer":85},"What data and ground-truth are used to evaluate the method?",{"text":86,"@type":78},"Two Mars digital elevation models (from MOLA/MGS and HRSC/MEX) are tested, and the Robbins and Hynek crater catalogue provides ground-truth 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