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Articles | Volume 17, issue 9
Articles | Volume 17, issue 9
https://doi.org/10.5194/se-17-1087-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-17-1087-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 9
Method article
 | 
29 Sep 2026
Method article |  | 29 Sep 2026

Towards robust fracture mapping: benchmarking automatic fracture mapping in 2D outcrop imagery

Ayoub Fatihi, Jefter Caldeira, Tom Beucler, Samuel T. Thiele, and Anindita Samsu

Data sets

FraXet: Deep learning ready UAV dataset for fracture mapping Ayoub Fatihi et al. https://doi.org/10.5281/zenodo.17069947

Fracture Mapping Models Trained on FraXet Ayoub Fatihi et al. https://doi.org/10.5281/zenodo.17866853

Model code and software

Code Fracture Segmentation on FraXet Ayoub Fatihi and Sam Thiele https://doi.org/10.5281/zenodo.17953223

Interactive computing environment

Online FraXteX Interactive Demo Ayoub Fatihi et al. https://huggingface.co/spaces/ayoubft/fractex2D_tuto

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Short summary
Mapping rock fractures in high resolution aerial images is essential for understanding Earth processes and managing resources, but manual tracing is slow and inconsistent. We created FraXet, a large harmonized dataset of nearly nine thousand images, and compared standard image filters with modern deep learning models. The deep learning methods were far more accurate and produced smoother, more reliable maps, while also showing where results are uncertain.
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