Meteorite Search and Recovery: a drone-assisted machine learning approach to recover fusion-crusted meteorites

Meteorite falls can occur at any given place on Earth, and instrument observations of their fireballs can help constrain search areas to recover freshly fallen fragments. Rapid recovery is essential in European field settings, where exposure to soil, moisture, and vegetation can quickly affect meteorite preservation. Drone-assisted aerial surveys of strewn fields combined with machine learning can offer a novel approach to assist in search and recovery efforts. Other studies have explored drone-based meteorite detection as well [1-4], yet existing approaches are often tied to specific terrains and are difficult to adapt to the diverse land-cover types that we encounter across Europe.

Here we present a flexible training-data generation strategy and detection pipeline that leverages transfer learning with a pre-trained Convolutional Neural Network (CNN) architecture to identify fusion-crusted meteorites. Museum specimens were photographed from multiple orientations on an automated rotating stage and composited onto representative ground backgrounds to synthesize a varied training set. After fine-tuning the network on this dataset, we validated it on drone imagery containing hidden meteorites in grassy environments at multiple flight levels and in a demarcated search area that was surveyed by drone. The system consistently detected meteorites across these controlled field tests, while also highlighting the importance of tuning image scale, flight altitude, and detection-window size.

As our approach to training data generation is adaptive, we can tune the pipeline to strewn field-specific land cover types, providing a practical route toward deployment in future search campaigns. With the pipeline tested in a controlled field setting, we now await the next meteorite-dropping fireball event in the Netherlands or surrounding countries to conduct real-world field trials to validate this novel airborne detection strategy.

References
[1] Anderson, et al. "Machine learning for semi‐automated meteorite recovery." Meteoritics & Planetary Science 55.11 (2020): 2461-2471. [2] Anderson et al. "Successful recovery of an observed meteorite fall using drones and machine learning." The Astrophysical Journal Letters 930.2 (2022): L25. [3] Citron, Robert I., et al. "Recovery of meteorites using an autonomous drone and machine learning." Meteoritics & Planetary Science 56.6 (2021): 1073-1085. [4] Zender et al. (2018). Meteorite detection with airborne support—A study case. In International Meteor Conference, Petnica, Serbia (pp. 145-152).