Recovery of Meteorites Using an Autonomous Drone and Machine Learning
Robert I. Citron, Peter Jenniskens, Christopher Watkins, Sravanthi Sinha, Amar Shah, Chedy Raissi, Hadrien Devillepoix, Jim Albers
The recovery of freshly fallen meteorites from tracked and triangulated meteors is critical to determining their source asteroid families. However, locating meteorite fragments in strewn fields remains a challenge with very few meteorites being recovered from the meteors triangulated in past and ongoing meteor camera networks. We examined if locating meteorites can be automated using machine learning and an autonomous drone. Drones can be programmed to fly a grid search pattern and take systematic pictures of the ground over a large survey area. Those images can be analyzed using a machine learning classifier to identify meteorites in the field among many other features. Here, we describe a proof-of-concept meteorite classifier that deploys off-line a combination of different convolution neural networks to recognize meteorites from images taken by drones in the field. The system was implemented in a conceptual drone setup and tested in the suspected strewn field of a recent meteorite fall near Walker Lake, Nevada.
Comments: 16 pages, 9 Figures
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Journal reference: Meteoritics & Planetary Science (2021)
DOI: 10.1111/maps.13663
Cite as: arXiv:2106.06523 [astro-ph.EP (or arXiv:2106.06523v1 [astro-ph.EP] for this version)
Submission history
From: Robert Citron
[v1] Fri, 11 Jun 2021 17:36:33 UTC (15,733 KB)
