Articles | Volume 13, issue 4
https://doi.org/10.5194/esurf-13-705-2025
https://doi.org/10.5194/esurf-13-705-2025
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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2570', Anonymous Referee #1, 01 Oct 2024
  • RC2: 'Comment on egusphere-2024-2570', Anonymous Referee #2, 03 Oct 2024
  • AC1: 'Author Comment', Hanne Hendrickx, 02 Dec 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Hanne Hendrickx on behalf of the Authors (02 Dec 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (03 Dec 2024) by Giulia Sofia
RR by Anonymous Referee #2 (17 Jan 2025)
RR by Anonymous Referee #1 (26 Mar 2025)
ED: Reconsider after major revisions (26 Mar 2025) by Giulia Sofia
AR by Hanne Hendrickx on behalf of the Authors (24 Apr 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (05 May 2025) by Giulia Sofia
ED: Publish subject to technical corrections (11 May 2025) by Wolfgang Schwanghart (Editor)
AR by Hanne Hendrickx on behalf of the Authors (13 May 2025)  Manuscript 
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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.
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