Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning
We report the first-time recovery of a fresh meteorite fall using a drone and a machine-learning algorithm. The fireball was observed on 2021 April 1 over Western Australia by the Desert Fireball Network, for which a fall area was calculated for the predicted surviving mass. A search team arrived on...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
IOP Publishing Ltd
2022
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP170102529 http://hdl.handle.net/20.500.11937/90241 |
| _version_ | 1848765353407021056 |
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| author | Anderson, Seamus L. Towner, Martin Fairweather, John Bland, Philip Devillepoix, Hadrien Sansom, Eleanor Cupak, Martin Shober, Patrick M. Benedix, Gretchen |
| author_facet | Anderson, Seamus L. Towner, Martin Fairweather, John Bland, Philip Devillepoix, Hadrien Sansom, Eleanor Cupak, Martin Shober, Patrick M. Benedix, Gretchen |
| author_sort | Anderson, Seamus L. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We report the first-time recovery of a fresh meteorite fall using a drone and a machine-learning algorithm. The fireball was observed on 2021 April 1 over Western Australia by the Desert Fireball Network, for which a fall area was calculated for the predicted surviving mass. A search team arrived on-site and surveyed 5.1 km2 area over a 4 day period. A convolutional neural network, trained on previously recovered meteorites with fusion crusts, processed the images on our field computer after each flight. Meteorite candidates identified by the algorithm were sorted by team members using two user interfaces to eliminate false positives. Surviving candidates were revisited with a smaller drone, and imaged in higher resolution, before being eliminated or finally being visited in person. The 70 g meteorite was recovered within 50 m of the calculated fall line, demonstrating the effectiveness of this methodology, which will facilitate the efficient collection of many more observed meteorite falls. |
| first_indexed | 2025-11-14T11:33:54Z |
| format | Journal Article |
| id | curtin-20.500.11937-90241 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:33:54Z |
| publishDate | 2022 |
| publisher | IOP Publishing Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-902412023-02-22T07:49:32Z Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning Anderson, Seamus L. Towner, Martin Fairweather, John Bland, Philip Devillepoix, Hadrien Sansom, Eleanor Cupak, Martin Shober, Patrick M. Benedix, Gretchen Science & Technology Physical Sciences Astronomy & Astrophysics FIREBALL NETWORK We report the first-time recovery of a fresh meteorite fall using a drone and a machine-learning algorithm. The fireball was observed on 2021 April 1 over Western Australia by the Desert Fireball Network, for which a fall area was calculated for the predicted surviving mass. A search team arrived on-site and surveyed 5.1 km2 area over a 4 day period. A convolutional neural network, trained on previously recovered meteorites with fusion crusts, processed the images on our field computer after each flight. Meteorite candidates identified by the algorithm were sorted by team members using two user interfaces to eliminate false positives. Surviving candidates were revisited with a smaller drone, and imaged in higher resolution, before being eliminated or finally being visited in person. The 70 g meteorite was recovered within 50 m of the calculated fall line, demonstrating the effectiveness of this methodology, which will facilitate the efficient collection of many more observed meteorite falls. 2022 Journal Article http://hdl.handle.net/20.500.11937/90241 10.3847/2041-8213/ac66d4 English http://purl.org/au-research/grants/arc/DP170102529 http://purl.org/au-research/grants/arc/DP200102073 http://creativecommons.org/licenses/by/4.0/ IOP Publishing Ltd fulltext |
| spellingShingle | Science & Technology Physical Sciences Astronomy & Astrophysics FIREBALL NETWORK Anderson, Seamus L. Towner, Martin Fairweather, John Bland, Philip Devillepoix, Hadrien Sansom, Eleanor Cupak, Martin Shober, Patrick M. Benedix, Gretchen Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning |
| title | Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning |
| title_full | Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning |
| title_fullStr | Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning |
| title_full_unstemmed | Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning |
| title_short | Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning |
| title_sort | successful recovery of an observed meteorite fall using drones and machine learning |
| topic | Science & Technology Physical Sciences Astronomy & Astrophysics FIREBALL NETWORK |
| url | http://purl.org/au-research/grants/arc/DP170102529 http://purl.org/au-research/grants/arc/DP170102529 http://hdl.handle.net/20.500.11937/90241 |