Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 12
Articles | Volume 25, issue 12
https://doi.org/10.5194/nhess-25-4863-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-4863-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 12
Research article
 | 
09 Dec 2025
Research article |  | 09 Dec 2025

Deep learning-based object detection on LiDAR-derived hillshade images: insights into grain size distribution and longitudinal sorting of debris flows

Paul E. Schmid, Jacob Hirschberg, Raffaele Spielmann, and Jordan Aaron

Viewed

Total article views: 2,689 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,118 507 64 2,689 43 49
  • HTML: 2,118
  • PDF: 507
  • XML: 64
  • Total: 2,689
  • BibTeX: 43
  • EndNote: 49
Views and downloads (calculated since 28 May 2025)
Cumulative views and downloads (calculated since 28 May 2025)

Viewed (geographical distribution)

Total article views: 2,689 (including HTML, PDF, and XML) Thereof 2,576 with geography defined and 113 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Feb 2026
Download
Short summary
Debris flows are fast-moving water-sediment mixtures in steep channels, posing risks to infrastructure and lives. Traditional analysis is slow and labor-intensive. This study presents a method using laserscanners and deep learning to detect and track moving objects during active events. By converting three-dimensional data to two-dimensional images, it enables fast, accurate measurement of object speed and size. This improves debris-flow monitoring, enhancing hazard understanding and mitigation.
Read more
Share
Altmetrics
Final-revised paper
Preprint