Earth Surface Dynamics
Earth Surface Dynamics
ESurf
Articles | Volume 13, issue 4
Articles | Volume 13, issue 4
https://doi.org/10.5194/esurf-13-705-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-13-705-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 4
Research article
 | 
08 Aug 2025
Research article |  | 08 Aug 2025

AI-based tracking of fast-moving alpine landforms using high-frequency monoscopic time-lapse imagery

Hanne Hendrickx, Melanie Elias, Xabier Blanch, Reynald Delaloye, and Anette Eltner

Viewed

Total article views: 1,950 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,552 257 141 1,950 95 114
  • HTML: 1,552
  • PDF: 257
  • XML: 141
  • Total: 1,950
  • BibTeX: 95
  • EndNote: 114
Views and downloads (calculated since 22 Aug 2024)
Cumulative views and downloads (calculated since 22 Aug 2024)

Viewed (geographical distribution)

Total article views: 1,950 (including HTML, PDF, and XML) Thereof 1,898 with geography defined and 52 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
Short summary
This study presents a novel AI-based method for tracking and analysing the movement of rock glaciers and landslides, key landforms in high mountain regions. By utilising time-lapse images, our approach generates detailed velocity data, uncovering movement patterns often missed by traditional methods. This cost-effective tool enhances geohazard monitoring, providing insights into environmental drivers, improving process understanding, and contributing to better safety in alpine areas.
Read more
Share