Articles | Volume 18, issue 8
https://doi.org/10.5194/tc-18-3807-2024
https://doi.org/10.5194/tc-18-3807-2024
Research article
 | 
23 Aug 2024
Research article |  | 23 Aug 2024

Interactive snow avalanche segmentation from webcam imagery: results, potential, and limitations

Elisabeth D. Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler

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Cited articles

Baumer, J., Metzger, N., Hafner, E. D., Daudt, R. C., Wegner, J. D., and Schindler, K.: Automatic Image Compositing and Snow Segmentation for Alpine Snow Cover Monitoring, in: 2023 10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22–23 June 2023, 77–84, https://doi.org/10.1109/SDS57534.2023.00018, 2023. a
Benenson, R., Popov, S., and Ferrari, V.: Large-Scale Interactive Object Segmentation With Human Annotators, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, USA, 15–20 June 2019, 11692–11701, https://doi.org/10.1109/CVPR.2019.01197, 2019. a, b
Bianchi, F. M., Grahn, J., Eckerstorfer, M., Malnes, E., and Vickers, H.: Snow Avalanche Segmentation in SAR Images With Fully Convolutional Neural Networks, IEEE J. Sel. Top. Appl., 14, 75–82, https://doi.org/10.1109/JSTARS.2020.3036914, 2021. a, b
Boykov, Y. and Jolly, M.-P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, in: Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, Vancouver, B.C., Canada, 7–14 July 2001, 105–112 https://doi.org/10.1109/ICCV.2001.937505, 2001. a, b
Bozzini, C., Conedera, M., and Krebs, P.: A New Monoplotting Tool to Extract Georeferenced Vector Data and Orthorectified Raster Data from Oblique Non-Metric Photographs, International Journal of Heritage in the Digital Era, 1, 499–518, https://doi.org/10.1260/2047-4970.1.3.499, 2012. a, b, c, d
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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.