Geochronology
Geochronology
GChron
Articles | Volume 3, issue 1
Articles | Volume 3, issue 1
https://doi.org/10.5194/gchron-3-383-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gchron-3-383-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 3, issue 1
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

Supplement

https://doi.org/10.5194/gchron-3-383-2021-supplement

Data sets

Model code v2.1 (published article) and dataset Simon Nachtergaele and Johan De Grave https://doi.org/10.5281/zenodo.4906116

Model code and software

Model code v2.1 (published article) and dataset Simon Nachtergaele and Johan De Grave https://doi.org/10.5281/zenodo.4906116

Model code online application Simon Nachtergaele and Johan De Grave https://doi.org/10.5281/zenodo.4906177

Video supplement

Geochronological dating using AI-Track-tive: a brief intro to the offline application Simon Nachtergaele https://youtu.be/kW7TmHmI674

AI-Track-tive v2: new software for geological fission track dating: a tutorial Simon Nachtergaele https://youtu.be/CRr7B4TweHU

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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.
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