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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume X-2-2024
https://doi.org/10.5194/isprs-annals-X-2-2024-113-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-X-2-2024-113-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
10 Jun 2024
| 10 Jun 2024
Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds
Steffen Isfort
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Germany
Melanie Elias
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Germany
Hans-Gerd Maas
Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, Germany
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Short summary
Short summary
This study introduces a novel AI-based method to track and analyse the movement of rock glaciers and landslides, key indicators of permafrost dynamics in high mountain regions. Using time-lapse images, our approach provides detailed velocity data, revealing patterns that traditional methods miss. This cost-effective tool enhances our ability to monitor geohazards, offering insights into climate change impacts on permafrost and improving safety in alpine areas.
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Short summary
Short summary
The simple use of time-lapse cameras as a visual observation tool may already be a great help for environmental investigations. However, beyond that, they have the potential to also deliver precise measurements with high temporal and spatial resolution when applying appropriate processing techniques. In this paper we introduce a method for the determination of glacier motion fields from time-lapse images, but it might also be adapted for other environmental motion analysis tasks.
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