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dc.contributor.authorBenedix, Gretchen
dc.contributor.authorLagain, Anthony
dc.contributor.authorChai, Kevin
dc.contributor.authorMeka, S.
dc.contributor.authorAnderson, S.
dc.contributor.authorNorman, C.
dc.contributor.authorBland, Phil
dc.contributor.authorPaxman, Jonathan
dc.contributor.authorTowner, Martin
dc.contributor.authorTan, Tele
dc.identifier.citationBenedix, G. and Lagain, A. and Chai, K. and Meka, S. and Anderson, S. and Norman, C. and Bland, P. et al. 2020. Deriving Surface Ages on Mars using Automated Crater Counting. American Geophysical Union: Journal of Earth and Space Science. 7 (3): UNSP e2019EA001005.

Impact craters on solar system bodies are used to determine the relative ages of surfaces. The smaller the limiting primary crater size, the higher the spatial resolution in surface/resurfacing age dating. A manually counted database (Robbins & Hynek, 2012, of >384,000 craters on Mars >1 km in diameter exists. But because crater size scales as a power law, the number of impact craters in the size range 10 m to 1 km is in the tens of millions, a number making precise analysis of local variations of age, over an entire surface, impossible to perform by manual counting. To decode this crater size population at a planetary scale, we developed an automated Crater Detection Algorithm based on the You Only Look Once v3 object detection system. The algorithm was trained by annotating images of the controlled Thermal Emission Imaging System daytime infrared data set. This training data set contains 7,048 craters that the algorithm used as a learning benchmark. The results were validated against the manually counted database as the ground truth data set. We applied our algorithm to the Thermal Emission Imaging System global mosaic between ±65° of latitude, returning a true positive detection rate of 91% and a diameter estimation error (~15%) consistent with typical manual count variation. Importantly, although a number of automated crater counting algorithms have been published, for the first time we demonstrate that automatic counting can be routinely used to derive robust surface ages.

dc.subjectScience & Technology
dc.subjectPhysical Sciences
dc.subjectAstronomy & Astrophysics
dc.subjectGeosciences, Multidisciplinary
dc.titleDeriving Surface Ages on Mars using Automated Crater Counting
dc.typeJournal Article
dcterms.source.titleAmerican Geophysical Union: Journal of Earth and Space Science
curtin.departmentSchool of Earth and Planetary Sciences (EPS)
curtin.departmentCurtin School of Population Health
curtin.departmentSchool of Civil and Mechanical Engineering
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.facultyFaculty of Health Sciences
curtin.contributor.orcidPaxman, Jonathan [0000-0002-5671-9992]
curtin.contributor.orcidBenedix, Gretchen [0000-0003-0990-8878]
curtin.contributor.orcidLagain, Anthony [0000-0002-5391-3001]
curtin.contributor.orcidBland, Phil [0000-0002-4681-7898]
curtin.contributor.orcidTowner, Martin [0000-0002-8240-4150]
curtin.contributor.orcidChai, Kevin [0000-0003-1645-0922]
curtin.contributor.orcidTan, Tele [0000-0003-3195-3480]
curtin.contributor.researcheridBenedix, Gretchen [L-1953-2018]
curtin.contributor.researcheridBland, Phil [M-9392-2018]
curtin.contributor.researcheridChai, Kevin [F-1015-2013]
curtin.identifier.article-numberUNSP e2019EA001005
curtin.contributor.scopusauthoridPaxman, Jonathan [24725318500]
curtin.contributor.scopusauthoridBenedix, Gretchen [6603638882]
curtin.contributor.scopusauthoridLagain, Anthony [57194439282]
curtin.contributor.scopusauthoridBland, Phil [7005534334]
curtin.contributor.scopusauthoridTowner, Martin [6602160346]
curtin.contributor.scopusauthoridChai, Kevin [23396028100]
curtin.contributor.scopusauthoridTan, Tele [7402022415]

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