Articles | Volume 13, issue 1
https://doi.org/10.5194/esurf-13-167-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
07 Feb 2025
Research article |
| 07 Feb 2025
| 07 Feb 2025
Automatic detection of floating instream large wood in videos using deep learning
Related authors
EGUsphere, 2026
This preprint is open for discussion and under review for Solid Earth (SE).
Hydrol. Earth Syst. Sci., 28, 5443–5458, 2024
Earth Surf. Dynam., 11, 487–509, 2023
Cited articles
Aarnink, J. and Beucler, T.: Codebase for Automatic Detection of Instream Large Wood in Videos Using Deep Learning, GitHub [code], https://github.com/janbertoo/Instream_Wood_Detection (last access: 1 March 2024), 2024. a
Aarnink, J., Vuaridel, M., and Ruiz-Villanueva, V.: Database for Automatic Detection of Instream Large Wood in Videos Using Deep Learning, Zenodo [data set], https://doi.org/10.5281/zenodo.10822254, 2024. a
Àlex Solé Gómez, Scandolo, L., and Eisemann, E.: A learning approach for river debris detection, Int. J. Appl. Earth Obs., 107, 102682, https://doi.org/10.1016/j.jag.2022.102682, 2022. a
Andreoli, A., Comiti, F., and Lenzi, M. A.: Characteristics, distribution and geomorphic role of large woody debris in a mountain stream of the Chilean Andes, Earth Surf. Proc. Land., 32, 1675–1692, https://doi.org/10.1002/esp.1593, 2007. a
Benda, L. E. and Sias, J. C.: A quantitative framework for evaluating the mass balance of in-stream organic debris, Foreset Ecol. Manag., 172, 1–16, https://doi.org/10.1016/S0378-1127(01)00576-X, 2003. a
