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

Viewed

Total article views: 3,367 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,381 925 61 3,367 196 73 95
  • HTML: 2,381
  • PDF: 925
  • XML: 61
  • Total: 3,367
  • Supplement: 196
  • BibTeX: 73
  • EndNote: 95
Views and downloads (calculated since 21 Oct 2020)
Cumulative views and downloads (calculated since 21 Oct 2020)

Viewed (geographical distribution)

Total article views: 3,367 (including HTML, PDF, and XML) Thereof 2,977 with geography defined and 390 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Aug 2025
Download
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.
Share