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Paper detail

A Cloud-Based Tool for Meteorite Recovery Using Drones and Machine Learning

We present a cloud-based tool that uses drones and machine learning to help recover instrumentally observed meteorite falls. We showcase a collection of improvements made upon previous iterations of our system, as well as detail the successes and limitations of this technique when applied to observed meteorite falls in South and Western Australia. This tool is available to the meteoritics research community upon request at https://find.gfo.rocks.

preprint2026arXivOpen access
Seamus L. AndersonHadrien A. R. DevillepoixLewis LakerinkSawitchaya TippayaDale P. GianconoMartin C. TownerIona ClementeMartin CupákAshley F. RogersJohn H. FairweatherMia WalkerDaniel BurginMichael A. FrazerSophie E. DeamVeronika PazderováEleanor K. SansomBenjamin A. D. HartigHely C. BrancoThomas StevensonIsabella HattyAnna ZappatiniAnthony LagainTom LovelockAuriane EgalLucy FormanDavid BeltonSimon WindsorShibli SaleheenAsher LeslieGregory B. PooleAndrew LangendamRachel S. KirbyAndrew G. Tomkins
Machine Learningastro-ph.IMastro-ph.EP
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Seamus L. AndersonHadrien A. R. DevillepoixLewis LakerinkSawitchaya TippayaDale P. GianconoMartin C. TownerIona ClementeMartin CupákAshley F. RogersJohn H. FairweatherMia WalkerDaniel BurginMichael A. FrazerSophie E. DeamVeronika PazderováEleanor K. SansomBenjamin A. D. HartigHely C. BrancoThomas StevensonIsabella HattyAnna ZappatiniAnthony LagainTom LovelockAuriane EgalLucy FormanDavid BeltonSimon WindsorShibli SaleheenAsher LeslieGregory B. PooleAndrew LangendamRachel S. KirbyAndrew G. Tomkins

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