Reference : Directional Statistics and Machine Learning for crater detection in Space

	Document type :	Scientific Presentations in Universities or Research Centers : Scientific presentation in universities or research centers
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Mathematics Physical, chemical, mathematical & earth Sciences : Space science, astronomy & astrophysics Engineering, computing & technology : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/10993/55718

Title :	Directional Statistics and Machine Learning for crater detection in Space
Language :	English
Author, co-author :	Palmirotta, Guendalina [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) >]
	Loizidou, Sophia [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) >]
	Nagarajan, Senthil Murugan [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) >]
Publication date :	18-Jul-2023
Event name :	Data Science Summer School 2023
Event date :	17-07-2023 to 19-07-2023
Event organizer :	University of Luxembourg
Event place (city) :	Luxembourg
Abstract :	[en] Craters are distinctive features on the surfaces of most terrestrial planets such as Mars and Venus. The distribution of craters reveals the relative ages of surface units and provides information on surface geology. Extracting craters is one of the fundamental tasks in planetary research. Although many automated crater detection algorithms have been developed to extract craters from image or topographic data, most of them are applicable only in particular regions, and only a few can be widely used, especially in complex surface settings. On the other side, once we have a reasonable craters data, statistics play an important role in better understanding their features, in particular their distribution. In this workshop, we will demonstrate to participants how basic methodologies with directional statistics and machine learning/deep learning models help in the detection and analysis of craters in our Universe.
Funders :	SanDAL
Target :	Students ; General public
Permalink :	http://hdl.handle.net/10993/55718
Other URL :	https://math.uni.lu/datascienceschool/
	https://www.uni.lu/fstm-en/news/data-science-summer-school-2023-young-students-play-with-data/

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Limited access Craters_SummerSchool23.pptx Publisher postprint 33.06 MB Request a copy Additional material(s): File Commentary Size Access Limited access Activity_SpotCraters.pdf 2.49 MB Request a copy Private access Python_Code_Workshop.pdf 164.36 kB Request a copy Limited access Description_Workshop23_Craters.pdf 67.17 kB Request a copy

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