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Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning

arXiv 2203.01466 ↗First posted 3 Mar 2022astro-ph.EP cs.LG physics.geo-phcited by 0

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Abstract (arXiv)

We report the first-time recovery of a fresh meteorite fall using a drone and a machine learning algorithm. A fireball on the 1st April 2021 was observed 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 using, demonstrating the effectiveness of this methodology which will facilitate the efficient collection of many more observed meteorite falls.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 18 references that are in the map. First posted on arXiv 3 Mar 2022.

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2 of 18 references.

References · 2

  1. 2020Machine Learning for Semi-Automated Meteorite Recoverycited by 2
  2. 2021Recovery of Meteorites Using an Autonomous Drone and Machine Learningcited by 1

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