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

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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.
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