Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recovery of Meteorites Using an Autonomous Drone and Machine Learning   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.

11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga