Articles | Volume 3, issue 1
https://doi.org/10.5194/gchron-3-383-2021
https://doi.org/10.5194/gchron-3-383-2021
Research article
 | 
30 Jun 2021
Research article |  | 30 Jun 2021

AI-Track-tive: open-source software for automated recognition and counting of surface semi-tracks using computer vision (artificial intelligence)

Simon Nachtergaele and Johan De Grave

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Cited articles

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: A System for Large-Scale Machine Learning, in: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI'16), 2–4 November 2016, Savannah, GA, USA, 265–284, 2016. 
Ansberque, C., Chew, D. M., and Drost, K.: Apatite fission-track dating by LA-Q-ICP-MS imaging, Chem. Geol., 560, 119977, https://doi.org/10.1016/j.chemgeo.2020.119977, 2021. 
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Short summary
Artificial intelligence techniques are capable of automatically detecting fission tracks in minerals. The AI-Track-tive software presented here can be used to automatically determine fission track densities for apatite fission track dating studies. Apatite fission track dating is mainly applied to tectonic research on exhumation rates in orogens. Time-consuming manual track counting can be replaced by deep neural networks capable of automatically finding the large majority of tracks.