_version_ 1866911696722853888
author Anderson, Seamus L.
Devillepoix, Hadrien A. R.
Lakerink, Lewis
Tippaya, Sawitchaya
Giancono, Dale P.
Towner, Martin C.
Clemente, Iona
Cupák, Martin
Rogers, Ashley F.
Fairweather, John H.
Walker, Mia
Burgin, Daniel
Frazer, Michael A.
Deam, Sophie E.
Pazderová, Veronika
Sansom, Eleanor K.
Hartig, Benjamin A. D.
Branco, Hely C.
Stevenson, Thomas
Hatty, Isabella
Zappatini, Anna
Lagain, Anthony
Lovelock, Tom
Egal, Auriane
Forman, Lucy
Belton, David
Windsor, Simon
Saleheen, Shibli
Leslie, Asher
Poole, Gregory B.
Langendam, Andrew
Kirby, Rachel S.
Tomkins, Andrew G.
author_facet Anderson, Seamus L.
Devillepoix, Hadrien A. R.
Lakerink, Lewis
Tippaya, Sawitchaya
Giancono, Dale P.
Towner, Martin C.
Clemente, Iona
Cupák, Martin
Rogers, Ashley F.
Fairweather, John H.
Walker, Mia
Burgin, Daniel
Frazer, Michael A.
Deam, Sophie E.
Pazderová, Veronika
Sansom, Eleanor K.
Hartig, Benjamin A. D.
Branco, Hely C.
Stevenson, Thomas
Hatty, Isabella
Zappatini, Anna
Lagain, Anthony
Lovelock, Tom
Egal, Auriane
Forman, Lucy
Belton, David
Windsor, Simon
Saleheen, Shibli
Leslie, Asher
Poole, Gregory B.
Langendam, Andrew
Kirby, Rachel S.
Tomkins, Andrew G.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2605_19179
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A Cloud-Based Tool for Meteorite Recovery Using Drones and Machine Learning
Anderson, Seamus L.
Devillepoix, Hadrien A. R.
Lakerink, Lewis
Tippaya, Sawitchaya
Giancono, Dale P.
Towner, Martin C.
Clemente, Iona
Cupák, Martin
Rogers, Ashley F.
Fairweather, John H.
Walker, Mia
Burgin, Daniel
Frazer, Michael A.
Deam, Sophie E.
Pazderová, Veronika
Sansom, Eleanor K.
Hartig, Benjamin A. D.
Branco, Hely C.
Stevenson, Thomas
Hatty, Isabella
Zappatini, Anna
Lagain, Anthony
Lovelock, Tom
Egal, Auriane
Forman, Lucy
Belton, David
Windsor, Simon
Saleheen, Shibli
Leslie, Asher
Poole, Gregory B.
Langendam, Andrew
Kirby, Rachel S.
Tomkins, Andrew G.
Earth and Planetary Astrophysics
Instrumentation and Methods for Astrophysics
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.
title A Cloud-Based Tool for Meteorite Recovery Using Drones and Machine Learning
topic Earth and Planetary Astrophysics
Instrumentation and Methods for Astrophysics
Machine Learning
url https://arxiv.org/abs/2605.19179