Earth Surface Dynamics
Earth Surface Dynamics
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Articles | Volume 14, issue 3
Articles | Volume 14, issue 3
https://doi.org/10.5194/esurf-14-391-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-14-391-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 3
Research article
 | 
12 May 2026
Research article |  | 12 May 2026

OrthoSAM: multi-scale extension of the Segment Anything Model for river pebble delineation from large orthophotos

Vito Chan, Aljoscha Rheinwalt, and Bodo Bookhagen

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Buscombe, D.: SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry, Earth Surf. Proc. Land., 45, 638–651, https://doi.org/10.1002/esp.4760, 2020. a
Buscombe, D., Rubin, D. M., and Warrick, J. A.: A universal approximation of grain size from images of noncohesive sediment, J. Geophys. Res.-Earth Surf., 115, https://doi.org/10.1029/2009JF001477, 2010. a
Butler, J. B., Lane, S. N., and Chandler, J. H.: Automated extraction of grain-size data from gravel surfaces using digital image processing, J. Hydraul. Res., https://doi.org/10.1080/00221686.2001.9628276, 2001. a
Carbonneau, P. E., Lane, S. N., and Bergeron, N. E.: Catchment-scale mapping of surface grain size in gravel bed rivers using airborne digital imagery, Water Resour. Res., 40, https://doi.org/10.1029/2003WR002759, 2004. a
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OrthoSAM is a new method that uses Segment Anything Model (SAM) to automatically identify and outline individual pebbles in high-resolution aerial images. OrthoSAM divides large photos into smaller sections that SAM can process effectively, and it improves the way to tell SAM where to look for objects. It uses a multi-resolution approach to handle different sizes, and it can be used to determine the distribution. Tests with computer-generated images and field data show that it is very precise.
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