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dc.contributor.authorAnderson, Seamus L.
dc.contributor.authorTowner, Martin
dc.contributor.authorFairweather, John
dc.contributor.authorBland, Philip
dc.contributor.authorDevillepoix, Hadrien
dc.contributor.authorSansom, Eleanor
dc.contributor.authorCupak, Martin
dc.contributor.authorShober, Patrick M.
dc.contributor.authorBenedix, Gretchen
dc.date.accessioned2023-01-30T01:08:18Z
dc.date.available2023-01-30T01:08:18Z
dc.date.issued2022
dc.identifier.citationAnderson, S.L. and Towner, M.C. and Fairweather, J. and Bland, P.A. and Devillepoix, H.A.R. and Sansom, E.K. and Cupák, M. et al. 2022. Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning. Astrophysical Journal Letters. 930 (2): ARTN L25.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/90241
dc.identifier.doi10.3847/2041-8213/ac66d4
dc.description.abstract

We report the first-time recovery of a fresh meteorite fall using a drone and a machine-learning algorithm. The fireball was observed on 2021 April 1 over Western Australia by the Desert Fireball Network, for which a fall area was calculated for the predicted surviving mass. A search team arrived on-site and surveyed 5.1 km2 area over a 4 day period. A convolutional neural network, trained on previously recovered meteorites with fusion crusts, processed the images on our field computer after each flight. Meteorite candidates identified by the algorithm were sorted by team members using two user interfaces to eliminate false positives. Surviving candidates were revisited with a smaller drone, and imaged in higher resolution, before being eliminated or finally being visited in person. The 70 g meteorite was recovered within 50 m of the calculated fall line, demonstrating the effectiveness of this methodology, which will facilitate the efficient collection of many more observed meteorite falls.

dc.languageEnglish
dc.publisherIOP Publishing Ltd
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/DP170102529
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/DP200102073
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & Technology
dc.subjectPhysical Sciences
dc.subjectAstronomy & Astrophysics
dc.subjectFIREBALL NETWORK
dc.titleSuccessful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning
dc.typeJournal Article
dcterms.source.volume930
dcterms.source.number2
dcterms.source.issn2041-8205
dcterms.source.titleAstrophysical Journal Letters
dc.date.updated2023-01-30T01:08:07Z
curtin.departmentSchool of Earth and Planetary Sciences (EPS)
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidDevillepoix, Hadrien [0000-0001-9226-1870]
curtin.contributor.orcidTowner, Martin [0000-0002-8240-4150]
curtin.contributor.orcidBland, Philip [0000-0002-4681-7898]
curtin.contributor.orcidSansom, Eleanor [0000-0003-2702-673X]
curtin.contributor.orcidBenedix, Gretchen [0000-0003-0990-8878]
curtin.contributor.orcidCupak, Martin [0000-0003-2193-0867]
curtin.contributor.orcidAnderson, Seamus L. [h0000-0002-8914-3264]
curtin.contributor.orcidShober, Patrick M. [0000-0003-4766-2098]
curtin.contributor.researcheridBland, Philip [M-9392-2018]
curtin.contributor.researcheridBenedix, Gretchen [L-1953-2018]
curtin.identifier.article-numberARTN L25
dcterms.source.eissn2041-8213
curtin.contributor.scopusauthoridDevillepoix, Hadrien [56703315600]
curtin.contributor.scopusauthoridTowner, Martin [6602160346]
curtin.contributor.scopusauthoridBland, Philip [7005534334]
curtin.contributor.scopusauthoridSansom, Eleanor [56460192900]
curtin.contributor.scopusauthoridBenedix, Gretchen [6603638882]
curtin.contributor.scopusauthoridCupak, Martin [56460108800]


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