Articles | Volume 18, issue 8
https://doi.org/10.5194/tc-18-3807-2024
© 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/tc-18-3807-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Aug 2024
Research article |
| 23 Aug 2024
| 23 Aug 2024
Interactive snow avalanche segmentation from webcam imagery: results, potential, and limitations
Elisabeth D. Hafner
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, 7260 Davos Dorf, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Center CERC, 7260 Davos Dorf, Switzerland
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, 8093 Zurich, Switzerland
Theodora Kontogianni
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, 8093 Zurich, Switzerland
ETH AI Center, ETH Zurich, 8092 Zurich, Switzerland
Rodrigo Caye Daudt
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, 8093 Zurich, Switzerland
Lucien Oberson
WSL Institute for Snow and Avalanche Research SLF, 7260 Davos Dorf, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Center CERC, 7260 Davos Dorf, Switzerland
Swiss National Railway, SBB, 3000 Bern, Switzerland
Jan Dirk Wegner
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, 8093 Zurich, Switzerland
Department of Mathematical Modeling and Machine Learning, University of Zurich, 8057 Zurich, Switzerland
Konrad Schindler
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, 8093 Zurich, Switzerland
Yves Bühler
WSL Institute for Snow and Avalanche Research SLF, 7260 Davos Dorf, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Center CERC, 7260 Davos Dorf, Switzerland
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We applied a horizontally mounted low-cost precipitation radar to measure velocities, frequency of occurrence, travel distances and turbulence characteristics of blowing snow off a mountain ridge. Our analysis provides a first insight into the potential of radar measurements for determining blowing snow characteristics, improves our understanding of mountain ridge blowing snow events and serves as a valuable data basis for validating coupled numerical weather and snowpack simulations.
Yves Bühler, Elisabeth D. Hafner, Benjamin Zweifel, Mathias Zesiger, and Holger Heisig
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Andrin Caviezel, Sophia E. Demmel, Adrian Ringenbach, Yves Bühler, Guang Lu, Marc Christen, Claire E. Dinneen, Lucie A. Eberhard, Daniel von Rickenbach, and Perry Bartelt
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Yves Bühler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, and Marc Christen
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 739–744, https://doi.org/10.5194/isprs-archives-XLII-2-739-2018, https://doi.org/10.5194/isprs-archives-XLII-2-739-2018, 2018
Karolina Korzeniowska, Yves Bühler, Mauro Marty, and Oliver Korup
Nat. Hazards Earth Syst. Sci., 17, 1823–1836, https://doi.org/10.5194/nhess-17-1823-2017, https://doi.org/10.5194/nhess-17-1823-2017, 2017
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In this study, we have focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on aerial imagery using an object-based image analysis (OBIA) approach. We compared the results with manually mapped avalanche polygons, and obtained a user’s accuracy of > 0.9 and a Cohen’s kappa of 0.79–0.85. Testing the method for a larger area of 226.3 km2, we estimated producer’s and user’s accuracies of 0.61 and 0.78, respectively.
Cesar Vera Valero, Nander Wever, Yves Bühler, Lukas Stoffel, Stefan Margreth, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 16, 2303–2323, https://doi.org/10.5194/nhess-16-2303-2016, https://doi.org/10.5194/nhess-16-2303-2016, 2016
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Simulating medium–small avalanches operationally on a mine service road allows avalanche hazard to be assessed on the mine transportation route. Using accurate data from the snow cover and the avalanche paths, the avalanche dynamic model developed can calculate the avalanche runout distances and snow volumes of the deposits. The model does not predict whether the avalanche is coming or not, but if it comes, the model will predict runout distances and mass of the deposits.
Yves Bühler, Marc S. Adams, Ruedi Bösch, and Andreas Stoffel
The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, https://doi.org/10.5194/tc-10-1075-2016, 2016
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We map the distribution of snow depth at two alpine test sites with unmanned aerial system (UAS) data by applying structure-from-motion photogrammetry. In comparison with manual snow depth measurements, we find high accuracies of 7 to 15 cm for the snow depth values. We can prove that photogrammetric measurements on snow-covered terrain are possible. Underlaying vegetation such as bushes and grass leads to an underestimation of snow depth in the range of 10 to 50 cm.
C. Vera Valero, Y. Bühler, and P. Bartelt
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-3-2883-2015, https://doi.org/10.5194/nhessd-3-2883-2015, 2015
Manuscript not accepted for further review
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Wet snow avalanches can initiate from large fracture slabs or small point releases. Point
release wet snow avalanches can reach dangerous proportions when they initiate on steep and long avalanche paths and entrain warm moist snow. In this paper we investigate the dynamics of point release wet snow avalanches by applying a numerical model to simulate documented case studies on high altitude slopes in the Chilean Andes. The model simulated correctly flow height, velocity and avalanche run out.
Y. Bühler, M. Marty, L. Egli, J. Veitinger, T. Jonas, P. Thee, and C. Ginzler
The Cryosphere, 9, 229–243, https://doi.org/10.5194/tc-9-229-2015, https://doi.org/10.5194/tc-9-229-2015, 2015
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We are able to map snow depth over large areas ( > 100km2) using airborne digital photogrammetry. Digital photogrammetry is more economical than airborne Laser Scanning but slightly less accurate. Comparisons to independent snow depth measurements reveal an accuracy of about 30cm. Spatial continuous mapping of snow depth is a major step forward compared to point measurements usually applied today. Limitations are steep slopes (> 50°) and areas covered by trees and scrubs.
T. Grünewald, Y. Bühler, and M. Lehning
The Cryosphere, 8, 2381–2394, https://doi.org/10.5194/tc-8-2381-2014, https://doi.org/10.5194/tc-8-2381-2014, 2014
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Elevation dependencies of snow depth are analysed based on snow depth maps obtained from airborne remote sensing. Elevation gradients are characterised by a specific shape: an increase of snow depth with elevation is followed by a distinct peak at a certain level and a decrease in the highest elevations. We attribute this shape to an increase of precipitation with altitude, which is modified by topographical-induced redistribution processes of the snow on the ground (wind, gravitation).
A. Aydin, Y. Bühler, M. Christen, and I. Gürer
Nat. Hazards Earth Syst. Sci., 14, 1145–1154, https://doi.org/10.5194/nhess-14-1145-2014, https://doi.org/10.5194/nhess-14-1145-2014, 2014
Y. Bühler, S. Kumar, J. Veitinger, M. Christen, A. Stoffel, and Snehmani
Nat. Hazards Earth Syst. Sci., 13, 1321–1335, https://doi.org/10.5194/nhess-13-1321-2013, https://doi.org/10.5194/nhess-13-1321-2013, 2013
Related subject area
Discipline: Snow | Subject: Natural Hazards
Snow mechanical property variability at the slope scale – implication for snow mechanical modelling
Combining modelled snowpack stability with machine learning to predict avalanche activity
Can Saharan dust deposition impact snowpack stability in the French Alps?
A closed-form model for layered snow slabs
A random forest model to assess snow instability from simulated snow stratigraphy
Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting
Snow Avalanche Frequency Estimation (SAFE): 32 years of monitoring remote avalanche depositional zones in high mountains of Afghanistan
Brief communication: Weak control of snow avalanche deposit volumes by avalanche path morphology
Elevation-dependent trends in extreme snowfall in the French Alps from 1959 to 2019
Dynamic crack propagation in weak snowpack layers: insights from high-resolution, high-speed photography
Avalanche danger level characteristics from field observations of snow instability
Using avalanche problems to examine the effect of large-scale atmosphere–ocean oscillations on avalanche hazard in western Canada
On the importance of snowpack stability, the frequency distribution of snowpack stability, and avalanche size in assessing the avalanche danger level
The mechanical origin of snow avalanche dynamics and flow regime transitions
On the relation between avalanche occurrence and avalanche danger level
Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK
Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps
Cold-to-warm flow regime transition in snow avalanches
Francis Meloche, Francis Gauthier, and Alexandre Langlois
The Cryosphere, 18, 1359–1380, https://doi.org/10.5194/tc-18-1359-2024, https://doi.org/10.5194/tc-18-1359-2024, 2024
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Snow avalanches are a dangerous natural hazard. Backcountry recreationists and avalanche practitioners try to predict avalanche hazard based on the stability of snow cover. However, snow cover is variable in space, and snow stability observations can vary within several meters. We measure the snow stability several times on a small slope to create high-resolution maps of snow cover stability. These results help us to understand the snow variation for scientists and practitioners.
Léo Viallon-Galinier, Pascal Hagenmuller, and Nicolas Eckert
The Cryosphere, 17, 2245–2260, https://doi.org/10.5194/tc-17-2245-2023, https://doi.org/10.5194/tc-17-2245-2023, 2023
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Avalanches are a significant issue in mountain areas where they threaten recreationists and human infrastructure. Assessments of avalanche hazards and the related risks are therefore an important challenge for local authorities. Meteorological and snow cover simulations are thus important to support operational forecasting. In this study we combine it with mechanical analysis of snow profiles and find that observed avalanche data improve avalanche activity prediction through statistical methods.
Oscar Dick, Léo Viallon-Galinier, François Tuzet, Pascal Hagenmuller, Mathieu Fructus, Benjamin Reuter, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 17, 1755–1773, https://doi.org/10.5194/tc-17-1755-2023, https://doi.org/10.5194/tc-17-1755-2023, 2023
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Saharan dust deposition can drastically change the snow color, turning mountain landscapes into sepia scenes. Dust increases the absorption of solar energy by the snow cover and thus modifies the snow evolution and potentially the avalanche risk. Here we show that dust can lead to increased or decreased snowpack stability depending on the snow and meteorological conditions after the deposition event. We also show that wet-snow avalanches happen earlier in the season due to the presence of dust.
Philipp Weißgraeber and Philipp L. Rosendahl
The Cryosphere, 17, 1475–1496, https://doi.org/10.5194/tc-17-1475-2023, https://doi.org/10.5194/tc-17-1475-2023, 2023
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The work presents a mathematical model that calculates the behavior of layered snow covers in response to loadings. The information is necessary to predict the formation of snow slab avalanches. While sophisticated computer simulations may achieve the same goal, they can require weeks to run. By using mathematical simplifications commonly used by structural engineers, the present model can provide hazard assessments in milliseconds, even for snowpacks with many layers of different types of snow.
Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer
The Cryosphere, 16, 4593–4615, https://doi.org/10.5194/tc-16-4593-2022, https://doi.org/10.5194/tc-16-4593-2022, 2022
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Information on snow instability is crucial for avalanche forecasting. We introduce a novel machine-learning-based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detection of the weakest layer and assessment of its degree of instability with one single index.
Simon Horton and Pascal Haegeli
The Cryosphere, 16, 3393–3411, https://doi.org/10.5194/tc-16-3393-2022, https://doi.org/10.5194/tc-16-3393-2022, 2022
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Snowpack models can help avalanche forecasters but are difficult to verify. We present a method for evaluating the accuracy of simulated snow profiles using readily available observations of snow depth. This method could be easily applied to understand the representativeness of available observations, the agreement between modelled and observed snow depths, and the implications for interpreting avalanche conditions.
Arnaud Caiserman, Roy C. Sidle, and Deo Raj Gurung
The Cryosphere, 16, 3295–3312, https://doi.org/10.5194/tc-16-3295-2022, https://doi.org/10.5194/tc-16-3295-2022, 2022
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Snow avalanches cause considerable material and human damage in all mountain regions of the world. We present the first model to automatically inventory avalanche deposits at the scale of a catchment area – here the Amu Panj in Afghanistan – every year since 1990. This model called Snow Avalanche Frequency Estimation (SAFE) is available online on the Google Engine. SAFE has been designed to be simple and universal to use. Nearly 810 000 avalanches were detected over the 32 years studied.
Hippolyte Kern, Nicolas Eckert, Vincent Jomelli, Delphine Grancher, Michael Deschatres, and Gilles Arnaud-Fassetta
The Cryosphere, 15, 4845–4852, https://doi.org/10.5194/tc-15-4845-2021, https://doi.org/10.5194/tc-15-4845-2021, 2021
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Snow avalanches are a major component of the mountain cryosphere that often put people, settlements, and infrastructures at risk. This study investigated avalanche path morphological factors controlling snow deposit volumes, a critical aspect of snow avalanche dynamics that remains poorly known. Different statistical techniques show a slight but significant link between deposit volumes and avalanche path morphology.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
The Cryosphere, 15, 4335–4356, https://doi.org/10.5194/tc-15-4335-2021, https://doi.org/10.5194/tc-15-4335-2021, 2021
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Extreme snowfall can cause major natural hazards (avalanches, winter storms) that can generate casualties and economic damage. In the French Alps, we show that between 1959 and 2019 extreme snowfall mainly decreased below 2000 m of elevation and increased above 2000 m. At 2500 m, we find a contrasting pattern: extreme snowfall decreased in the north, while it increased in the south. This pattern might be related to increasing trends in extreme snowfall observed near the Mediterranean Sea.
Bastian Bergfeld, Alec van Herwijnen, Benjamin Reuter, Grégoire Bobillier, Jürg Dual, and Jürg Schweizer
The Cryosphere, 15, 3539–3553, https://doi.org/10.5194/tc-15-3539-2021, https://doi.org/10.5194/tc-15-3539-2021, 2021
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The modern picture of the snow slab avalanche release process involves a
dynamic crack propagation phasein which a whole slope becomes detached. The present work contains the first field methodology which provides the temporal and spatial resolution necessary to study this phase. We demonstrate the versatile capabilities and accuracy of our method by revealing intricate dynamics and present how to determine relevant characteristics of crack propagation such as crack speed.
Jürg Schweizer, Christoph Mitterer, Benjamin Reuter, and Frank Techel
The Cryosphere, 15, 3293–3315, https://doi.org/10.5194/tc-15-3293-2021, https://doi.org/10.5194/tc-15-3293-2021, 2021
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Snow avalanches threaten people and infrastructure in snow-covered mountain regions. To mitigate the effects of avalanches, warnings are issued by public forecasting services. Presently, the five danger levels are described in qualitative terms. We aim to characterize the avalanche danger levels based on expert field observations of snow instability. Our findings contribute to an evidence-based description of danger levels and to improve consistency and accuracy of avalanche forecasts.
Pascal Haegeli, Bret Shandro, and Patrick Mair
The Cryosphere, 15, 1567–1586, https://doi.org/10.5194/tc-15-1567-2021, https://doi.org/10.5194/tc-15-1567-2021, 2021
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Numerous large-scale atmosphere–ocean oscillations including the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, the Pacific North American Teleconnection Pattern, and the Arctic Oscillation are known to substantially affect winter weather patterns in western Canada. Using avalanche problem information from public avalanche bulletins, this study presents a new approach for examining the effect of these atmospheric oscillations on the nature of avalanche hazard in western Canada.
Frank Techel, Karsten Müller, and Jürg Schweizer
The Cryosphere, 14, 3503–3521, https://doi.org/10.5194/tc-14-3503-2020, https://doi.org/10.5194/tc-14-3503-2020, 2020
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Exploring a large data set of snow stability tests and avalanche observations, we quantitatively describe the three key elements that characterize avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. The findings will aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.
Xingyue Li, Betty Sovilla, Chenfanfu Jiang, and Johan Gaume
The Cryosphere, 14, 3381–3398, https://doi.org/10.5194/tc-14-3381-2020, https://doi.org/10.5194/tc-14-3381-2020, 2020
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This numerical study investigates how different types of snow avalanches behave, how key factors affect their dynamics and flow regime transitions, and what are the underpinning rules. According to the unified trends obtained from the simulations, we are able to quantify the complex interplay between bed friction, slope geometry and snow mechanical properties (cohesion and friction) on the maximum velocity, runout distance and deposit height of the avalanches.
Jürg Schweizer, Christoph Mitterer, Frank Techel, Andreas Stoffel, and Benjamin Reuter
The Cryosphere, 14, 737–750, https://doi.org/10.5194/tc-14-737-2020, https://doi.org/10.5194/tc-14-737-2020, 2020
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Snow avalanches represent a major natural hazard in seasonally snow-covered mountain regions around the world. To avoid periods and locations of high hazard, avalanche warnings are issued by public authorities. In these bulletins, the hazard is characterized by a danger level. Since the danger levels are not well defined, we analyzed a large data set of avalanches to improve the description. Our findings show discrepancies in present usage of the danger scale and show ways to improve the scale.
Bettina Richter, Jürg Schweizer, Mathias W. Rotach, and Alec van Herwijnen
The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, https://doi.org/10.5194/tc-13-3353-2019, 2019
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Information on snow stability is important for avalanche forecasting. To improve the stability estimation in the snow cover model SNOWPACK, we suggested an improved parameterization for the critical crack length. We compared 3 years of field data to SNOWPACK simulations. The match between observed and modeled critical crack lengths greatly improved, and critical weak layers appear more prominently in the modeled vertical profile of critical crack length.
Yves Bühler, Elisabeth D. Hafner, Benjamin Zweifel, Mathias Zesiger, and Holger Heisig
The Cryosphere, 13, 3225–3238, https://doi.org/10.5194/tc-13-3225-2019, https://doi.org/10.5194/tc-13-3225-2019, 2019
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We manually map 18 737 avalanche outlines based on SPOT6 optical satellite imagery acquired in January 2018. This is the most complete and accurate avalanche documentation of a large avalanche period covering a big part of the Swiss Alps. This unique dataset can be applied for the validation of other remote-sensing-based avalanche-mapping procedures and for updating avalanche databases to improve hazard maps.
Anselm Köhler, Jan-Thomas Fischer, Riccardo Scandroglio, Mathias Bavay, Jim McElwaine, and Betty Sovilla
The Cryosphere, 12, 3759–3774, https://doi.org/10.5194/tc-12-3759-2018, https://doi.org/10.5194/tc-12-3759-2018, 2018
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Snow avalanches show complicated flow behaviour, characterized by several flow regimes which coexist in one avalanche. In this work, we analyse flow regime transitions where a powder snow avalanche transforms into a plug flow avalanche by incorporating warm snow due to entrainment. Prediction of such a transition is very important for hazard mitigation, as the efficiency of protection dams are strongly dependent on the flow regime, and our results should be incorporated into avalanche models.
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
Bozzini, C., Conedera, M., and Krebs, P.: A new tool for facilitating the retrieval and recording of the place name cultural heritage, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W2, 115–118, https://doi.org/10.5194/isprsarchives-XL-5-W2-115-2013, 2013. a, b, c
Bründl, M. and Margreth, S.: Integrative Risk Management, in: W. Haeberli & C. Whiteman (Eds.), Snow and Ice-Related Hazards, Risks and Disasters, 2015, 263–301, https://doi.org/10.1016/B978-0-12-394849-6.00009-3, 2015. a
Bühler, Y., Hafner, E. D., Zweifel, B., Zesiger, M., and Heisig, H.: Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13, 3225–3238, https://doi.org/10.5194/tc-13-3225-2019, 2019. a, b, c
Bühler, Y., Bebi, P., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., Marty, C., Schmucki, G., Caviezel, A., Kühne, R., Wohlwend, S., and Bartelt, P.: Automated avalanche hazard indication mapping on a statewide scale, Nat. Hazards Earth Syst. Sci., 22, 1825–1843, https://doi.org/10.5194/nhess-22-1825-2022, 2022. a
Chen, L., Strauch, M., and Merhof, D.: Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints, arXiv [preprint], https://doi.org/10.48550/arXiv.2004.09821, 2020. a
Christen, M., Kowalski, J., and Bartelt, P.: RAMMS: Numerical simulation of dense snow avalanches in three-dimensional terrain, Cold Reg. Sci. Technol., 63, 1–14, https://doi.org/10.1016/j.coldregions.2010.04.005, 2010. a
Cronbach, L. J.: Test “reliability”: Its meaning and determination, Psychometrika, 12, 1–16, https://doi.org/10.1007/bf02289289, 1947. a
De Brabandere, B., Neven, D., and Van Gool, L.: Semantic Instance Segmentation for Autonomous Driving, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), Honolulu, Hawaii, USA,, 21–26 July 2017, 478–480, https://doi.org/10.1109/CVPRW.2017.66, 2017. a
Eckerstorfer, M., Bühler, Y., Frauenfelder, R., and Malnes, E.: Remote sensing of snow avalanches: Recent advances, potential, and limitations, Cold Reg. Sci. Technol., 121, 126–140, https://doi.org/10.1016/j.coldregions.2015.11.001, 2016. a
Eckerstorfer, M., Vickers, H., Malnes, E., and Grahn, J.: Near-Real Time Automatic Snow Avalanche Activity Monitoring System Using Sentinel-1 SAR Data in Norway, Remote Sensing, 11, 2863, https://doi.org/10.3390/rs11232863, 2019. a, b
ESA: DEFROST, https://business.esa.int/projects/defrost (last access 8 February 2024), 2020. a
Fox, J., Siebenbrunner, A., Reitinger, S., Peer, D., and Rodríguez-Sánchez, A.: Deep Learning for Real-Time Avalanche Detection in Webcam Images, International Snow Science Workshop ISSW, Bend, 8–13 October 2023, https://arc.lib.montana.edu/snow-science/objects/ISSW2023_P3.47.pdf (last access: 15 August 2024), 2023. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
Golparvar, B. and Wang, R.-Q.: AI-supported Framework of Semi-Automatic Monoplotting for Monocular Oblique Visual Data Analysis, arXiv [preprint], https://doi.org/10.48550/arXiv.2111.14021, 2021. a, b
Gulshan, V., Rother, C., Criminisi, A., Blake, A., and Zisserman, A.: Geodesic star convexity for interactive image segmentation, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 13–18 June 2010. 3129–3136, https://doi.org/10.1109/CVPR.2010.5540073, 2010. a
Gupta, A., Dollar, P., and Girshick, R.: LVIS: A Dataset for Large Vocabulary Instance Segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv [preprint], https://doi.org/10.48550/arXiv.1908.03195, 2019. a
Hafner, E. D.: Data interactive avalanche segmentation, EnviDat [data set], https://doi.org/10.16904/ENVIDAT.526, 2024. a
Hafner, E. D. and Oberson, L.: aval-e/InteractiveAvalancheSegmentation: Code to ineractivly segment avalanches from oblique photographs, Zenodo [code], https://doi.org/10.5281/zenodo.12698270, 2024. a
Hafner, E. D., Techel, F., Leinss, S., and Bühler, Y.: Mapping avalanches with satellites – evaluation of performance and completeness, The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, 2021. a, b, c, d
Hafner, E. D., Barton, P., Daudt, R. C., Wegner, J. D., Schindler, K., and Bühler, Y.: Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations, The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, 2022. a, b, c, d
Hafner, E., Oberson, L., Kontogianni, T., Daudt, R. C., Wegner, J. D., Schindler, K., and Bühler, Y.: Using interactive object segmentation to derive avalanche outlines from webcam imagery, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10867, https://doi.org/10.5194/egusphere-egu23-10867, 2023a. a
Hafner, E. D., Techel, F., Daudt, R. C., Wegner, J. D., Schindler, K., and Bühler, Y.: Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice, Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023, 2023b. a, b, c, d, e, f, g, h, i
He, K., Gkioxari, G., Dollár, P., and Girshick, R.: Mask R-CNN, arXiv [preprint], https://doi.org/10.48550/arXiv.1703.06870, 2018. a
Kapper, K. L., Goelles, T., Muckenhuber, S., Trügler, A., Abermann, J., Schlager, B., Gaisberger, C., Eckerstorfer, M., Grahn, J., Malnes, E., Prokop, A., and Schöner, W.: Automated snow avalanche monitoring for Austria: State of the art and roadmap for future work, Frontiers in Remote Sensing, 4, https://doi.org/10.3389/frsen.2023.1156519, 2023. a
Karas, A., Karbou, F., Giffard-Roisin, S., Durand, P., and Eckert, N.: Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for Snow Avalanche Debris Monitoring, IEEE T. Geosci. Remote, 60, 1–17, https://doi.org/10.1109/TGRS.2021.3131853, 2022. a
Keskinen, Z., Hendrikx, J., Eckerstorfer, M., and Birkeland, K.: Satellite detection of snow avalanches using Sentinel-1 in a transitional snow climate, Cold Reg. Sci. Technol., 199, 103558, https://doi.org/10.1016/j.coldregions.2022.103558, 2022. a
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., and Girshick, R.: Segment Anything, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.02643, 2023. a
Kontogianni, T., Gygli, M., Uijlings, J., and Ferrari, V.: Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections, ECCV 2020, online, 23–28 August 2020, https://doi.org/10.48550/arXiv.1911.12709, 2020. a, b
Levandowsky, M. and Winter, D.: Distance between sets, Nature, 234, 34–35, 1971. a
Li, Z., Chen, Q., and Koltun, V.: Interactive Image Segmentation with Latent Diversity, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, 18–23 June 2018, 577–585, https://doi.org/10.1109/CVPR.2018.00067, 2018. a
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., and Dollár, P.: Microsoft COCO: Common Objects in Context, arXiv [preprint], https://doi.org/10.48550/arXiv.1405.0312, 2015. a
Lin, Z., Duan, Z.-P., Zhang, Z., Guo, C.-L., and Cheng, M.-M.: FocusCut: Diving into a Focus View in Interactive Segmentation, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, Louisiana, USA, 19–24 June 2022, 2627–2636, https://doi.org/10.1109/CVPR52688.2022.00266, 2022. a
Liu, Y., Li, H., Hu, C., Luo, S., Luo, Y., and Chen, C. W.: Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images, IEEE T. Neur. Net. Lear., https://doi.org/10.1109/TNNLS.2023.3336563, 2022. a
Mahadevan, S., Voigtlaender, P., and Leibe, B.: Iteratively Trained Interactive Segmentation, in: 2018 Conference on Computer Vision and Pattern Recognition (CVPR), arXiv [preprint], https://doi.org/10.48550/arXiv.1805.04398, 2018. a
Padilla, R., Netto, S. L., and da Silva, E. A. B.: A Survey on Performance Metrics for Object-Detection Algorithms, 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niteroi, Brazil, 1–3 July 2020, 237–242, https://doi.org/10.1109/IWSSIP48289.2020.9145130, 2020. a
Portenier, C., Hüsler, F., Härer, S., and Wunderle, S.: Towards a webcam-based snow cover monitoring network: methodology and evaluation, The Cryosphere, 14, 1409–1423, https://doi.org/10.5194/tc-14-1409-2020, 2020. a
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 15 August 2024), 2021. a
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection, arXiv [preprint], https://doi.org/10.48550/arXiv.1506.02640, 2016. a
Rother, C., Kolmogorov, V., and Blake, A.: “GrabCut”: Interactive Foreground Extraction Using Iterated Graph Cuts, ACM Transaction on Graphics (TOM), 23, 309–314, https://doi.org/10.1145/1015706.1015720, 2004. a
Rudolf-Miklau, F., Sauermoser, S., and Mears, A. (Eds.): The technical avalanche protection handbook, Ernst & Sohn, Berlin, ISBN 978-3-433-03034-9, 2015. a
Sampl, P. and Zwinger, T.: Avalanche simulation with SAMOS, Ann. Glaciol., 38, 393–398, https://doi.org/10.3189/172756404781814780, 2004. a
Sofiiuk, K., Petrov, I., Barinova, O., and Konushin, A.: f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation, arXiv [preprint], https://doi.org/10.48550/arXiv.2001.10331, 2020. a
Supervisely: Supervisely Computer Vision platform, https://supervisely.com, last access 20 July 2023. a
University of Innsbruck, Lo.La Peak Solutions GmbH, Avalanche Warning Service Tyrol, and Avalanche Warning Service Bavaria: UIBK Avalanche Dataset, University of Innsbruck [data set], https://doi.org/10.48323/H07F4-QZD17, 2023. a, b
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., Liu, W., and Xiao, B.: Deep High-Resolution Representation Learning for Visual Recognition, arXiv [preprint], https://doi.org/10.48550/arXiv.1908.07919, 2020. a, b
Wesselink, D. S., Malnes, E., Eckerstorfer, M., and Lindenbergh, R. C.: Automatic detection of snow avalanche debris in central Svalbard using C-band SAR data, Polar Res., 36, 1333236, https://doi.org/10.1080/17518369.2017.1333236, 2017. a
Xu, N., Price, B., Cohen, S., Yang, J., and Huang, T.: Deep Interactive Object Selection, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, 373–381, https://doi.org/10.1109/CVPR.2016.47, 2016. a, b, c
Xu, Z. and Zhao, S.: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network, Scientific Data, 11, 266, https://doi.org/10.1038/s41597-023-02844-2, 2024. a, b
Yuan, Y., Chen, X., and Wang, J.: Object-Contextual Representations for Semantic Segmentation, in: Computer Vision – ECCV 2020, Springer International Publishing, 173–190, https://doi.org/10.1007/978-3-030-58539-6_11, 2020. a
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.
For many safety-related applications such as road management, well-documented avalanches are...
