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

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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.

fields

astro-ph.EP 1

years

2026 1

verdicts

CONDITIONAL 1

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  • Freo Doctor: Atmospheric Modelling for Meteorite Falls and Spacecraft Re-Entries astro-ph.EP · 2026-06-05 · conditional · none · ref 2 · internal anchor

    High-resolution WRF atmospheric models show that wind-induced shifts in meteorite ground positions are typically 143-307 m, exceeding observational uncertainties, with successful use in recovering 12 meteorites and open release of 1107 models for 302 events.