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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

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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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