Recovery of Meteorites Using an Autonomous Drone and Machine Learning

TL;DR

This paper presents a novel approach combining autonomous drones and machine learning to automate the detection and recovery of meteorites in strewn fields, improving efficiency and success rates.

Contribution

It introduces a proof-of-concept convolutional neural network classifier integrated with drone technology for meteorite detection in the field.

Findings

Successful deployment of a CNN-based classifier in a drone setup

Effective identification of meteorites in field images

Potential to enhance meteorite recovery efficiency

Abstract

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.