Machine Learning for Semi-Automated Meteorite Recovery

• URL: http://arxiv.org/abs/2009.13852v1
• Date: Tue, 29 Sep 2020 08:27:41 GMT
• Title: Machine Learning for Semi-Automated Meteorite Recovery
• Authors: Seamus Anderson, Martin Towner, Phil Bland, Christopher Haikings, William Volante, Eleanor Sansom, Hadrien Devillepoix, Patrick Shober, Benjamin Hartig, Martin Cupak, Trent Jansen-Sturgeon, Robert Howie, Gretchen Benedix, Geoff Deacon
• Abstract summary: We present a novel methodology for recovering meteorite falls observed and constrained by fireball networks, using drones and machine learning algorithms. This approach uses images of the local terrain for a given fall site to train an artificial neural network, designed to detect meteorite candidates. We have field tested our methodology to show a meteorite detection rate between 75-97%, while also providing an efficient mechanism to eliminate false-positives.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel methodology for recovering meteorite falls observed and constrained by fireball networks, using drones and machine learning algorithms. This approach uses images of the local terrain for a given fall site to train an artificial neural network, designed to detect meteorite candidates. We have field tested our methodology to show a meteorite detection rate between 75-97%, while also providing an efficient mechanism to eliminate false-positives. Our tests at a number of locations within Western Australia also showcase the ability for this training scheme to generalize a model to learn localized terrain features. Our model-training approach was also able to correctly identify 3 meteorites in their native fall sites, that were found using traditional searching techniques. Our methodology will be used to recover meteorite falls in a wide range of locations within globe-spanning fireball networks.

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