13 citations as recorded by crossref.

1. Large eddy simulation of a utility-scale horizontal axis turbine with woody debris accumulation under live bed conditions M. Aksen et al. https://doi.org/10.1016/j.renene.2024.122110
2. Observer‐bias and sampling uncertainties in riverine wood flux and volume estimation from video monitoring technique H. Ghaffarian et al. https://doi.org/10.1002/esp.5500
3. Aerial-Drone-Based Tool for Assessing Flood Risk Areas Due to Woody Debris Along River Basins I. Barbero-García et al. https://doi.org/10.3390/drones9030191
4. Smart Video Analytics Solution to Identify Urban Floodborne Objects U. Iqbal et al. https://doi.org/10.1061/JCCEE5.CPENG-6526
5. Current progress in quantifying and monitoring instream large wood supply and transfer in rivers V. Ruiz‐Villanueva et al. https://doi.org/10.1002/esp.5765
6. Automatic detection of floating instream large wood in videos using deep learning J. Aarnink et al. https://doi.org/10.5194/esurf-13-167-2025
7. Unravelling the frequency and magnitude of large wood mobility and transport distance across 11 low‐order streams over multiple years B. Finch et al. https://doi.org/10.1002/esp.70248
8. The floodplain wood budget: Linking wood dynamics and ecogeomorphic processes K. Lininger et al. https://doi.org/10.1002/esp.70244
9. Floating wood volume determination using non-contact measurements and RANSAC method Ž. Pleterski et al. https://doi.org/10.1016/j.geomorph.2026.110221
10. Floodborne Objects Type Recognition Using Computer Vision to Mitigate Blockage Originated Floods U. Iqbal et al. https://doi.org/10.3390/w14172605
11. Analysis of floating objects based on non-intrusive measuring methods and machine learning M. Škerjanec et al. https://doi.org/10.1016/j.geomorph.2022.108254
12. Integrating deep-learning detection and hydrological monitoring for driftwood flux estimation in a steep tropical watershed during a typhoon-driven flood Q. Kong et al. https://doi.org/10.1016/j.jhydrol.2026.136183
13. Detection of Hazardous River Floating Debris using CCTV Images and Semantic Segmentation Model Y. Kim et al. https://doi.org/10.7846/JKOSMEE.2024.27.2.111