Meteorites are valuable resources in planetary geoscience, since they provide invaluable insights into the composition and origin of planetary bodies, enhancing our understanding of planet formation and the solar system. Despite occurrences of meteorite-dropping fireballs observed every 2 years, traditional field search campaigns in the Netherlands have faced challenges in retrieving fragments. In this presentation we will outline an innovative approach for meteorite recovery through the integration of drones and machine learning algorithms. To enhance search efforts, drones can be used to gain access to restricted fields and increase efficiency. The proposed strategy employs Convolutional Neural Networks (CNNs), a type of machine learning algorithm, to analyze drone imagery and identify potential meteorites. In order to train the artificial neural network, we created an extensive library of image data of fusion-crusted meteorites from the Naturalis meteorite collection placed on a backdrop of various field and soil conditions. The algorithm was tested during a field experiment at Unmanned Valley, Valkenburg, South Holland, using real meteorites and ‘meteorwrongs’ (objects resembling meteorites). During the drone survey we captured images of the target area and then implemented our detection strategy based on machine learning to the new image data set. In this presentation we will discuss the results and future steps.

Meteorites are valuable resources in planetary geoscience, since they provide invaluable insights into the composition and origin of planetary bodies, enhancing our understanding of planet formation and the solar system. Despite occurrences of meteorite-dropping fireballs observed every 2 years, traditional field search campaigns in the Netherlands have faced challenges in retrieving fragments. In this presentation we will outline an innovative approach for meteorite recovery through the integration of drones and machine learning algorithms. To enhance search efforts, drones can be used to gain access to restricted fields and increase efficiency. The proposed strategy employs Convolutional Neural Networks (CNNs), a type of machine learning algorithm, to analyze drone imagery and identify potential meteorites. In order to train the artificial neural network, we created an extensive library of image data of fusion-crusted meteorites from the Naturalis meteorite collection placed on a backdrop of various field and soil conditions. The algorithm was tested during a field experiment at Unmanned Valley, Valkenburg, South Holland, using real meteorites and ‘meteorwrongs’ (objects resembling meteorites). During the drone survey we captured images of the target area and then implemented our detection strategy based on machine learning to the new image data set. In this presentation we will discuss the results and future steps.