Roberto Gorelli points our attention at a recently published meteor related paper:
Machine Learning for Semi-Automated Meteorite Recovery
This article has been submitted for publication by 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: 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 globespanning fireball networks.
You can download this paper for free: https://arxiv.org/ftp/arxiv/papers/2009/2009.13852.pdf (15 pages).
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