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

arXiv:2605.19179 · astro-ph.EP, astro-ph.IM, cs.LG · Submitted 2026-05-18 · Read on arXiv

Seamus L. Anderson, Hadrien A. R. Devillepoix, Lewis Lakerink, Sawitchaya Tippaya, Dale P. Giancono, Martin C. Towner, Iona Clemente, Martin Cupák, Ashley F. Rogers, John H. Fairweather, Mia Walker, Daniel Burgin, Michael A. Frazer, Sophie E. Deam, Veronika Pazderová, Eleanor K. Sansom, Benjamin A. D. Hartig, Hely C. Branco, Thomas Stevenson, Isabella Hatty, Anna Zappatini, Anthony Lagain, Tom Lovelock, Auriane Egal, Lucy Forman, David Belton, Simon Windsor, Shibli Saleheen, Asher Leslie, Gregory B. Poole, Andrew Langendam, Rachel S. Kirby, Andrew G. Tomkins

astro-ph.EP, astro-ph.IM, cs.LG

Submitted: 2026-05-18

Comments: 23 pages, 3 figures

Code: https://github.com/heartexlabs/label-studio

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: We present a cloud-based tool that uses drones and machine learning to help recover instrumentally observed meteorite falls.

Terminology

Abstract

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.

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