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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XI-2-2026
https://doi.org/10.5194/isprs-annals-XI-2-2026-933-2026
© Author(s) 2026. 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-XI-2-2026-933-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Jul 2026
| 03 Jul 2026
Preface: Technical Commission II (Photogrammetry)
Alper Yilmaz
Dept. of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, Ohio, USA
Jan Dirk Wegner
Department of Mathematical Modeling and Machine Learning (DM3L), University of Zurich, Zurich, Switzerland
Rongjun Qin
Dept. of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, Ohio, USA
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Short summary
Short summary
For many safety-related applications such as road management, well-documented avalanches are important. To enlarge the information, webcams may be used. We propose supporting the mapping of avalanches from webcams with a machine learning model that interactively works together with the human. Relying on that model, there is a 90% saving of time compared to the "traditional" mapping. This gives a better base for safety-critical decisions and planning in avalanche-prone mountain regions.
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