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

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

doi:https://doi.org/10.5194/esurf-13-705-2025

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

Data sets

pips_env H. Hendrickx https://github.com/hannehendrickx/pips_env

Model code and software

pips2 A. W. Harley https://github.com/aharley/pips2

LightGlue Computer Vision and Geometry Lab (CVG, ETH Zürich) https://github.com/cvg/LightGlue

GIRAFFE M. Elias https://github.com/mel-ias/GIRAFFE

pips_env H. Hendrickx https://github.com/hannehendrickx/pips_env

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.