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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Aug 2025
Research article |
| 08 Aug 2025
| 08 Aug 2025
AI-based tracking of fast-moving alpine landforms using high-frequency monoscopic time-lapse imagery
Hanne Hendrickx
CORRESPONDING AUTHOR
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, 01062 Dresden, Germany
Department of Geosciences, University of Fribourg, Fribourg, 1700, Switzerland
Melanie Elias
CORRESPONDING AUTHOR
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, 01062 Dresden, Germany
Xabier Blanch
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, 01062 Dresden, Germany
Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Reynald Delaloye
Department of Geosciences, University of Fribourg, Fribourg, 1700, Switzerland
Anette Eltner
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, 01062 Dresden, Germany
Related authors
Pedro Alberto Pereira Zamboni, Hanne Hendrickx, Dennis Sprute, Holger Flatt, Muhtasimul Islam Rushdi, Florian Brodrecht, and Anette Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W8-2024, 483–490, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-483-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-483-2024, 2024
Line Rouyet, Tobias Bolch, Francesco Brardinoni, Rafael Caduff, Diego Cusicanqui, Margaret Darrow, Reynald Delaloye, Thomas Echelard, Christophe Lambiel, Cécile Pellet, Lucas Ruiz, Lea Schmid, Flavius Sirbu, and Tazio Strozzi
Earth Syst. Sci. Data, 17, 4125–4157, https://doi.org/10.5194/essd-17-4125-2025, https://doi.org/10.5194/essd-17-4125-2025, 2025
Short summary
Short summary
Rock glaciers are landforms generated by the creep of frozen ground (permafrost) in cold-climate mountains. Mapping rock glaciers contributes to documenting the distribution and the dynamics of mountain permafrost. We compiled inventories documenting the location, the characteristics, and the extent of rock glaciers in 12 mountain regions around the world. In each region, a team of operators performed the work following common rules and agreed on final solutions when discrepancies were identified.
Lea Epple, Oliver Grothum, Anne Bienert, and Anette Eltner
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-380, https://doi.org/10.5194/essd-2025-380, 2025
Preprint under review for ESSD
Short summary
Short summary
The present study offers a unique, nested, high-resolution dataset that captures soil surface changes every 20 seconds during rainfall events, as well as over seasonal variations, across a range of spatial scales – plot, slope, and catchment. These data collected over a period of 3.5 years was facilitated by a camera-based approach. This open-access resource assists scientists in evaluating and refining existing models, improving process understanding, and training artificial intelligence.
Anette Eltner, David Favis-Mortlock, Oliver Grothum, Martin Neumann, Tomáš Laburda, and Petr Kavka
SOIL, 11, 413–434, https://doi.org/10.5194/soil-11-413-2025, https://doi.org/10.5194/soil-11-413-2025, 2025
Short summary
Short summary
This study develops a new method to improve the calibration and evaluation of models that predict soil erosion by water. By using advanced imaging techniques, we can capture detailed changes in the soil surface over time. This helps improve models that forecast erosion, especially as climate change creates new and unpredictable conditions. Our findings highlight the need for more precise tools to better model erosion of our land and environment in the future.
Oliver Grothum, Lea Epple, Anne Bienert, Xabier Blanch, and Anette Eltner
EGUsphere, https://doi.org/10.5194/egusphere-2025-2291, https://doi.org/10.5194/egusphere-2025-2291, 2025
Short summary
Short summary
Soil erosion threatens landscapes worldwide, and understanding how surfaces change over time is key to addressing this issue. We developed a new camera-based system that automatically captures and analyzes daily surface changes on a hillside over several years. Triggered by rain and a clock, the system showed how weather and farming impact the land. Our method offers a powerful way to monitor surface changes and can help improve predictions and solutions for soil erosion.
Xabier Blanch, Jens Grundmann, Ralf Hedel, and Anette Eltner
EGUsphere, https://doi.org/10.5194/egusphere-2025-724, https://doi.org/10.5194/egusphere-2025-724, 2025
Short summary
Short summary
This study presents a low-cost, automated system for monitoring river water levels using cameras and AI. By combining AI-based image analysis with photogrammetry, it accurately measures water levels in real-time, even in challenging conditions. Tested over 2.5 years at four sites, it achieved high accuracy (errors of 1.0–2.3 cm) and processed over 219,000 images. Its resilience makes it ideal for flood detection and water management in remote areas.
Robert Krüger, Xabier Blanch, Jens Grundmann, Ghazi Al-Rawas, and Anette Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W8-2024, 243–250, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-243-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-243-2024, 2024
Pedro Alberto Pereira Zamboni, Hanne Hendrickx, Dennis Sprute, Holger Flatt, Muhtasimul Islam Rushdi, Florian Brodrecht, and Anette Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W8-2024, 483–490, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-483-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-483-2024, 2024
Melanie Elias, Steffen Isfort, Anette Eltner, and Hans-Gerd Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 57–64, https://doi.org/10.5194/isprs-annals-X-2-2024-57-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-57-2024, 2024
Steffen Isfort, Melanie Elias, and Hans-Gerd Maas
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 113–120, https://doi.org/10.5194/isprs-annals-X-2-2024-113-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-113-2024, 2024
Robert Krüger, Pierre Karrasch, and Anette Eltner
Geosci. Instrum. Method. Data Syst., 13, 163–176, https://doi.org/10.5194/gi-13-163-2024, https://doi.org/10.5194/gi-13-163-2024, 2024
Short summary
Short summary
Low-cost sensors could fill gaps in existing observation networks. To ensure data quality, the quality of the factory calibration of a given sensor has to be evaluated if the sensor is used out of the box. Here, the factory calibration of a widely used low-cost rain gauge type has been tested both in the lab (66) and in the field (20). The results of the study suggest that the calibration of this particular type should at least be checked for every sensor before being used.
O. Grothum, A. Bienert, M. Bluemlein, and A. Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 163–170, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-163-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-163-2023, 2023
Xabier Blanch, Marta Guinau, Anette Eltner, and Antonio Abellan
Nat. Hazards Earth Syst. Sci., 23, 3285–3303, https://doi.org/10.5194/nhess-23-3285-2023, https://doi.org/10.5194/nhess-23-3285-2023, 2023
Short summary
Short summary
We present cost-effective photogrammetric systems for high-resolution rockfall monitoring. The paper outlines the components, assembly, and programming codes required. The systems utilize prime cameras to generate 3D models and offer comparable performance to lidar for change detection monitoring. Real-world applications highlight their potential in geohazard monitoring which enables accurate detection of pre-failure deformation and rockfalls with a high temporal resolution.
R. Blaskow and A. Eltner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 45–50, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-45-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-45-2023, 2023
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
Short summary
Short summary
This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Aldo Bertone, Chloé Barboux, Xavier Bodin, Tobias Bolch, Francesco Brardinoni, Rafael Caduff, Hanne H. Christiansen, Margaret M. Darrow, Reynald Delaloye, Bernd Etzelmüller, Ole Humlum, Christophe Lambiel, Karianne S. Lilleøren, Volkmar Mair, Gabriel Pellegrinon, Line Rouyet, Lucas Ruiz, and Tazio Strozzi
The Cryosphere, 16, 2769–2792, https://doi.org/10.5194/tc-16-2769-2022, https://doi.org/10.5194/tc-16-2769-2022, 2022
Short summary
Short summary
We present the guidelines developed by the IPA Action Group and within the ESA Permafrost CCI project to include InSAR-based kinematic information in rock glacier inventories. Nine operators applied these guidelines to 11 regions worldwide; more than 3600 rock glaciers are classified according to their kinematics. We test and demonstrate the feasibility of applying common rules to produce homogeneous kinematic inventories at global scale, useful for hydrological and climate change purposes.
Isabelle Gärtner-Roer, Nina Brunner, Reynald Delaloye, Wilfried Haeberli, Andreas Kääb, and Patrick Thee
The Cryosphere, 16, 2083–2101, https://doi.org/10.5194/tc-16-2083-2022, https://doi.org/10.5194/tc-16-2083-2022, 2022
Short summary
Short summary
We intensely investigated the Gruben site in the Swiss Alps, where glaciers and permafrost landforms closely interact, to better understand cold-climate environments. By the interpretation of air photos from 5 decades, we describe long-term developments of the existing landforms. In combination with high-resolution positioning measurements and ground surface temperatures, we were also able to link these to short-term changes and describe different landform responses to climate forcing.
Robert Ljubičić, Dariia Strelnikova, Matthew T. Perks, Anette Eltner, Salvador Peña-Haro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Ulf Scherling, Pietro Vuono, and Salvatore Manfreda
Hydrol. Earth Syst. Sci., 25, 5105–5132, https://doi.org/10.5194/hess-25-5105-2021, https://doi.org/10.5194/hess-25-5105-2021, 2021
Short summary
Short summary
The rise of new technologies such as drones (unmanned aerial systems – UASs) has allowed widespread use of image velocimetry techniques in place of more traditional, usually slower, methods during hydrometric campaigns. In order to minimize the velocity estimation errors, one must stabilise the acquired videos. In this research, we compare the performance of different UAS video stabilisation tools and provide guidelines for their use in videos with different flight and ground conditions.
Lea Epple, Andreas Kaiser, Marcus Schindewolf, and Anette Eltner
SOIL Discuss., https://doi.org/10.5194/soil-2021-85, https://doi.org/10.5194/soil-2021-85, 2021
Revised manuscript not accepted
Short summary
Short summary
Intensified extreme weather events due to climate change can result in changes of soil erosion. These unclear developments make an improvement of soil erosion modelling all the more important. Assuming that soil erosion models cannot keep up with the current data, this work gives an overview of 44 models, their strengths and weaknesses and discusses their potential for further development with respect to new and improved soil and soil erosion assessment techniques.
A. Eltner, D. Mader, N. Szopos, B. Nagy, J. Grundmann, and L. Bertalan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 717–722, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-717-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-717-2021, 2021
Sebastián Vivero, Reynald Delaloye, and Christophe Lambiel
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2021-8, https://doi.org/10.5194/esurf-2021-8, 2021
Preprint withdrawn
Short summary
Short summary
We use repeated drone flights to measure the velocities of a rock glacier located in the western Swiss Alps. The results are validated by comparing with simultaneous GPS measurements. Between 2016 and 2019, the rock glacier doubled its overall frontal velocity, from 5 m to more than 10 m per year. These high velocities and the development of a scarp feature indicate a rock glacier destabilisation phase. Finally, this work highlights the use of drones for rock glacier monitoring.
Cited articles
Altmann, M., Piermattei, L., Haas, F., Heckmann, T., Fleischer, F., Rom, J., Betz-Nutz, S., Knoflach, B., Müller, S., Ramskogler, K., Pfeiffer, M., Hofmeister, F., Ressl, C., and Becht, M.: Long-Term Changes of Morphodynamics on Little Ice Age Lateral Moraines and the Resulting Sediment Transfer into Mountain Streams in the Upper Kauner Valley, Austria, Water, 12, 3375, https://doi.org/10.3390/W12123375, 2020.
Barsch, D.: Rockglaciers, Springer Berlin Heidelberg, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-80093-1, 1996.
Blanch, X., Guinau, M., Eltner, A., and Abellan, A.: Fixed photogrammetric systems for natural hazard monitoring with high spatio-temporal resolution, Nat. Hazards Earth Syst. Sci., 23, 3285–3303, https://doi.org/10.5194/nhess-23-3285-2023, 2023.
Cicoira, A., Marcer, M., Gärtner-Roer, I., Bodin, X., Arenson, L. U., and Vieli, A.: A general theory of rock glacier creep based on in-situ and remote sensing observations, Permafrost Periglac., 32, 139–153, https://doi.org/10.1002/ppp.2090, 2021.
Cicoira, A., Weber, S., Biri, A., Buchli, B., Delaloye, R., Da Forno, R., Gärtner-Roer, I., Gruber, S., Gsell, T., Hasler, A., Lim, R., Limpach, P., Mayoraz, R., Meyer, M., Noetzli, J., Phillips, M., Pointner, E., Raetzo, H., Scapozza, C., Strozzi, T., Thiele, L., Vieli, A., Vonder Mühll, D., Wirz, V., and Beutel, J.: In situ observations of the Swiss periglacial environment using GNSS instruments, Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, 2022.
Computer Vision and Geometry Lab (CVG, ETH Zürich): LightGlue, Github [code], https://github.com/cvg/LightGlue (last access: 4 August 2025), 2023.
Delaloye, R., Lambiel, C., and Gärtner-Roer, I.: Overview of rock glacier kinematics research in the Swiss Alps, Geogr. Helv., 65, 135–145, https://doi.org/10.5194/gh-65-135-2010, 2010.
Delaloye, R., Morard, S., Barboux, C., Abbet, D., Gruber, V., Riedo, M., and Gachet, S.: Rapidly moving rock glaciers in Mattertal, Jahrestagung der Schweizerischen Geomorphologischen Gesellschaft, 21–31, 2013.
Elias, M.: GIRAFFE, Github [code], https://github.com/mel-ias/GIRAFFE (last access: 4 August 2025), 2024.
Elias, M. and Hendrickx, H.: AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery. In Earth Surface Dynamics, Zenodo [data set], https://doi.org/10.5281/zenodo.14260180, 2024.
Elias, M. and Maas, H.-G.: Measuring Water Levels by Handheld Smartphones: A contribution to exploit crowdsourcing in the spatio-temporal densification of water gauging networks, Int. Hydrogr. Rev., 27, 9–22, https://doi.org/10.58440/ihr-27-a01, 2022.
Elias, M., Kehl, C., and Schneider, D.: Photogrammetric water level determination using smartphone technology, Photogramm. Rec., 34, 198–223, https://doi.org/10.1111/phor.12280, 2019.
Elias, M., Eltner, A., Liebold, F., and Maas, H.-G.: Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras, Sensors, 20, 643, https://doi.org/10.3390/s20030643, 2020.
Elias, M., Weitkamp, A., and Eltner, A.: Multi-modal image matching to colorize a SLAM based point cloud with arbitrary data from a thermal camera, ISPRS Open Journal of Photogrammetry and Remote Sensing, 9, 100041, https://doi.org/10.1016/j.ophoto.2023.100041, 2023.
Elias, M., Isfort, S., Eltner, A., and Maas, H.-G.: UAS Photogrammetry for Precise Digital Elevation Models of Complex Topography: A Strategy Guide, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 57–64, https://doi.org/10.5194/isprs-annals-X-2-2024-57-2024, 2024.
Eltner, A., Kaiser, A., Abellan, A., and Schindewolf, M.: Time lapse structure-from-motion photogrammetry for continuous geomorphic monitoring, Earth Surf. Proc. Land., 42, 2240–2253, https://doi.org/10.1002/esp.4178, 2017.
Eltner, A., Sardemann, H., and Grundmann, J.: Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery, Hydrol. Earth Syst. Sci., 24, 1429–1445, https://doi.org/10.5194/hess-24-1429-2020, 2020.
Eltner, A., Hoffmeiste, D., Kaiser, A., Karrasch, P., Klingbeil, L., Stöcker, C., and Rovere, A.: UAVs for the Environmental Sciences, 494 pp., https://doi.org/10.53186/1028514, 2022.
Fortun, D., Bouthemy, P., and Kervrann, C.: Optical flow modeling and computation: A survey, Comput. Vis. Image Und., 134, 1–21, https://doi.org/10.1016/j.cviu.2015.02.008, 2015.
Frauenfelder, R., Haeberli, W., and Hoelzle, M.: Rockglacier occurrence and related terrain parameters in a study area of the Eastern Swiss Alps, in: Permafrost: Proceedings of the 8th International Conference on Permafrost, edited by: Phillips, M., Springman, S. M., and Arenson, L. U., Swets & Zeitlinger, 253–258, https://www.arlis.org/docs/vol1/ICOP/55700698/Pdf/Chapter_046.pdf (last access: 22 July 2025), 2003.
Harley, A. W.: pips2, Github [code], https://github.com/aharley/pips2 (last access: 4 August 2025), 2023.
Harley, A. W., Fang, Z., and Fragkiadaki, K.: Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories, arXiv [preprint], https://doi.org/10.48550/arxiv.2204.04153, 2022.
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016.
Heid, T. and Kääb, A.: Evaluation of existing image matching methods for deriving glacier surface displacements globally from optical satellite imagery, Remote Sens. Environ., 118, 339–355, https://doi.org/10.1016/j.rse.2011.11.024, 2012.
Hendrickx, H.: pips_env, Github [code] and [data set], https://github.com/hannehendrickx/pips_env (last access: 4 August 2025), 2024.
Hermle, D., Gaeta, M., Krautblatter, M., Mazzanti, P., and Keuschnig, M.: Performance Testing of Optical Flow Time Series Analyses Based on a Fast, High-Alpine Landslide, Remote Sens., 14, 455, https://doi.org/10.3390/rs14030455, 2022.
How, P., Hulton, N. R. J., Buie, L., and Benn, D. I.: PyTrx: A Python-Based Monoscopic Terrestrial Photogrammetry Toolset for Glaciology, Front. Earth Sci., 8, 21, https://doi.org/10.3389/feart.2020.00021, 2020.
Huang, Z., Shi, X., Zhang, C., Wang, Q., Cheung, K. C., Qin, H., Dai, J., and Li, H.: FlowFormer: A Transformer Architecture for Optical Flow, in: Computer Vision – ECCV 2022, Cham, 668–685, https://doi.org/10.1007/978-3-031-19790-1_40, 2022.
Hur, J. and Roth, S.: Optical Flow Estimation in the Deep Learning Age, in: Modelling Human Motion: From Human Perception to Robot Design, edited by: Noceti, N., Sciutti, A., and Rea, F., Springer International Publishing, Cham, 119–140, https://doi.org/10.1007/978-3-030-46732-6_7, 2020.
Ioli, F., Dematteis, N., Giordan, D., Nex, F., and Pinto, L.: Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring, PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 92, 657–678, https://doi.org/10.1007/s41064-023-00272-w, 2024.
James, M. R. and Robson, S.: Sequential digital elevation models of active lava flows from ground-based stereo time-lapse imagery, ISPRS J. Photogramm., 97, 160–170, https://doi.org/10.1016/j.isprsjprs.2014.08.011, 2014.
Kääb, A. and Reichmuth, T.: Advance mechanisms of rock glaciers, Permafrost Periglac., 16, 187–193, https://doi.org/10.1002/ppp.507, 2005.
Kenner, R., Bühler, Y., Delaloye, R., Ginzler, C., and Phillips, M.: Monitoring of high alpine mass movements combining laser scanning with digital airborne photogrammetry, Geomorphology, 206, 492–504, https://doi.org/10.1016/j.geomorph.2013.10.020, 2014.
Kenner, R., Phillips, M., Limpach, P., Beutel, J., and Hiller, M.: Monitoring mass movements using georeferenced time-lapse photography: Ritigraben rock glacier, western Swiss Alps, Cold Reg. Sci. Technol., 145, 127–134, https://doi.org/10.1016/j.coldregions.2017.10.018, 2018.
Kummert, M. and Delaloye, R.: Mapping and quantifying sediment transfer between the front of rapidly moving rock glaciers and torrential gullies, Geomorphology, 309, 60–76, https://doi.org/10.1016/j.geomorph.2018.02.021, 2018.
Kummert, M., Delaloye, R., and Braillard, L.: Erosion and sediment transfer processes at the front of rapidly moving rock glaciers: Systematic observations with automatic cameras in the western Swiss Alps, Permafrost Periglac., 29, 21–33, https://doi.org/10.1002/ppp.1960, 2018.
Lindenberger, P., Sarlin, P.-E., and Pollefeys, M.: LightGlue: Local Feature Matching at Light Speed, arXiv [preprint], https://doi.org/10.48550/arXiv.2306.13643, 2023.
Lowe, D. G.: Distinctive Image Features from Scale-Invariant Keypoints, Int. J. Comput. Vision, 60, 91–110, https://doi.org/10.1023/B:VISI.0000029664.99615.94, 2004.
Marcer, M., Bodin, X., Brenning, A., Schoeneich, P., Charvet, R., and Gottardi, F.: Permafrost Favorability Index: Spatial Modeling in the French Alps Using a Rock Glacier Inventory, Front. Earth Sci., 5, 105, https://doi.org/10.3389/feart.2017.00105, 2017.
Marcer, M., Cicoira, A., Cusicanqui, D., Bodin, X., Echelard, T., Obregon, R., and Schoeneich, P.: Rock glaciers throughout the French Alps accelerated and destabilised since 1990 as air temperatures increased, Commun. Earth Environ., 2, 1–11, https://doi.org/10.1038/s43247-021-00150-6, 2021.
Matsuoka, N.: Combining Time-Lapse Photography and Multisensor Monitoring to Understand Frost Creep Dynamics in the Japanese Alps, Permafrost Periglac., 25, 94–106, https://doi.org/10.1002/ppp.1806, 2014.
McColl, S. T. and Draebing, D.: Rock Slope Instability in the Proglacial Zone: State of the Art, Springer, Cham, 119–141, https://doi.org/10.1007/978-3-319-94184-4_8, 2019.
Pellet, C., Bodin, X., Cusicanqui, D., Delaloye, R., Kaab, A., Kaufmann, V., Noetzli, J., Thibert, E., Vivero, S., and Kellerer-Pirklbauer, A.: State of the climate in 2022: Rock glaciers velocity, B. Am. Meteor. Soc., 104, 41–42, https://doi.org/10.1175/2023BAMSStateoftheClimate.1, 2023.
Rock glacier inventories and kinematics (RGIK): Guidelines for inventorying rock glaciers: baseline and practical concepts (Version 1.0), https://doi.org/10.51363/unifr.srr.2023.002, 2023.
Sarlin, P. E., Detone, D., Malisiewicz, T., and Rabinovich, A.: SuperGlue: Learning Feature Matching with Graph Neural Networks, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4937–4946, https://doi.org/10.1109/CVPR42600.2020.00499, 2020.
Schwalbe, E. and Maas, H.-G.: The determination of high-resolution spatio-temporal glacier motion fields from time-lapse sequences, Earth Surf. Dynam., 5, 861–879, https://doi.org/10.5194/esurf-5-861-2017, 2017.
Shi, X., Huang, Z., Bian, W., Li, D., Zhang, M., Cheung, K. C., See, S., Qin, H., Dai, J., and Li, H.: VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation, arXiv [preprint], https://doi.org/10.48550/arXiv.2303.08340, 20 August 2023.
Stumpf, A., Augereau, E., Delacourt, C., and Bonnier, J.: Photogrammetric discharge monitoring of small tropical mountain rivers: A case study at Rivière des Pluies, Réunion Island, Water Resour. Res., 52, 4550–4570, https://doi.org/10.1002/2015WR018292, 2016.
Sun, J., Shen, Z., Wang, Y., Bao, H., and Zhou, X.: LoFTR: Detector-Free Local Feature Matching with Transformers, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4, 8918–8927, https://doi.org/10.1109/CVPR46437.2021.00881, 2021.
Teed, Z. and Deng, J.: RAFT: Recurrent All-Pairs Field Transforms for Optical Flow, in: Computer Vision – ECCV 2020, Cham, 402–419, https://doi.org/10.1007/978-3-030-58536-5_24, 2020.
Travelletti, J., Delacourt, C., Allemand, P., Malet, J.-P., Schmittbuhl, J., Toussaint, R., and Bastard, M.: Correlation of multi-temporal ground-based optical images for landslide monitoring: Application, potential and limitations, ISPRS J. Photogramm., 70, 39–55, https://doi.org/10.1016/j.isprsjprs.2012.03.007, 2012.
Geomorphology Research Group (UniFr): GRABENGUFER LANDSLIDE (VS), https://www.unifr.ch/geo/geomorphology/en/resources/study-sites/grabengufer-landslide.html (last access: 18 July 2025), 2025a.
Geomorphology Research Group (UniFr): GRABENGUFER ROCKGLACIER (VS), https://www.unifr.ch/geo/geomorphology/en/resources/study-sites/grabengufer-rg.html (last access: 18 July 2025), 2025b.
Ulm, M., Elias, M., Eltner, A. Lotsari, E., and Anders, K.: Automated change detection in photogrammetric 4D point clouds – transferability and extension of 4D objects-by-change for monitoring riverbank dynamics using low-cost cameras, Applied Geomatics, 17, 367–378, https://doi.org/10.1007/s12518-025-00623-9, 2025.
Wegner, K., Stark, M., Haas, F., and Becht, M.: Suitability of terrestrial archival imagery for SfM-MVS based surface reconstruction of steep rock walls for the detection of rockfalls, J. Geomorphol., https://doi.org/10.1127/jgeomorphology/2023/0775, 2023.
Xu, H., Zhang, J., Cai, J., Rezatofighi, H., and Tao, D.: GMFlow: Learning Optical Flow via Global Matching, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, June 2022, 8111–8120, https://doi.org/10.1109/CVPR52688.2022.00795, 2022.
Zheng, Y., Harley, A. W., Shen, B., Wetzstein, G., and Guibas, L. J.: PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking, arXiv [preprint], https://doi.org/10.48550/arXiv.2307.15055, 2023.
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.
This study presents a novel AI-based method for tracking and analysing the movement of rock...
