Preprints
https://doi.org/10.5194/egusphere-2022-938
https://doi.org/10.5194/egusphere-2022-938
 
06 Dec 2022
06 Dec 2022

Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms

Julia Kaltenborn1,2,3,4, Amy R. Macfarlane1, Viviane Clay2,5, and Martin Schneebeli1 Julia Kaltenborn et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Flüelastrasse 11, 7260 Davos Dorf, Switzerland
  • 2Institute of Cognitive Science, University Osnabrück, Wachsbleiche 27, 49090 Osnabrück, Germany
  • 3Mila - Quebec AI Institute, 6666 Rue Saint-Urbain, QC H2S 3H1, Montréal, Canada
  • 4School of Computer Science, McGill University, 3480 Rue University, QC H3A 2A7, Montréal, Canada
  • 5Numenta, 889 Winslow Street, CA 94063, Redwood City, United States

Abstract. Snow-layer segmentation and classification is an essential diagnostic task for a wide variety of cryospheric applications. The SnowMicroPen (SMP) measures the snowpack's penetration force at submillimetre resolution against the snow depth. The resulting depth-force profile can be parameterized for density and specific surface area. However, no information on traditional snow types is currently extracted automatically. The labeling of snow types is a time-intensive task that requires practice and becomes infeasible for large datasets. Previous work showed that automated segmentation and classification is in theory possible, but can either not be applied to data straight from the field or needs additional time-costly information, such as from classified snow pits. To address this gap, we evaluate how well machine learning models can automatically segment and classify SMP profiles. We trained fourteen different models, among them semi-supervised models and artificial neural networks (ANNs), on the MOSAiC SMP dataset, a large collection of snow profiles on Arctic sea ice. We found that SMP profiles can be successfully segmented and classified into snow classes, based solely on the SMP's signal. The model comparison provided in this study enables practitioners to choose a model that is suitable for their task and dataset. The findings presented will facilitate and accelerate snow type identification through SMP profiles. Overall, snowdragon creates a link between traditional snow classification and high-resolution force-depth profiles. With such a tool, traditional snow profile observations can be compared to SMP profiles.

Julia Kaltenborn et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Referee Comment on egusphere-2022-938', Anonymous Referee #1, 24 Dec 2022
  • RC2: 'Comment on egusphere-2022-938', Pascal Hagenmuller, 26 Jan 2023

Julia Kaltenborn et al.

Data sets

Snowpit SnowMicroPen (SMP) force profiles collected during the MOSAiC expedition Macfarlane, Amy R.; Schneebeli, Martin; Dadic, Ruzica; Wagner, David N.; Arndt, Stefanie; Clemens-Sewall, David; Hämmerle, Stefan; Hannula, Henna-Reetta; Jaggi, Matthias; Kolabutin, Nikolai; Krampe, Daniela; Lehning, Michael; Matero, Ilkka; Nicolaus, Marcel; Oggier, Marc; Pirazzini, Roberta; Polashenski, Chris; Raphael, Ian; Regnery, Julia; Shimanchuck, Egor; Smith, Madison M.; Tavri, Aikaterini https://doi.pangaea.de/10.1594/PANGAEA.935554

Model code and software

snowdragon Kaltenborn, Julia https://doi.org/10.5281/zenodo.7335813

Pre-trained Models for SMP Classification and Segmentation Kaltenborn, Julia; Macfarlane, Amy R.; Clay, Viviane; Schneebeli, Martin https://zenodo.org/record/7063521

Julia Kaltenborn et al.

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Short summary
Snow-layer segmentation and classification is an essential diagnostic task for cryospheric applications. A SnowMicroPen (SMP) can be used to that end, however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models can automate this task. Of the 14 different models trained on the MOSAiC SMP dataset, the long short-term memory performed the best. The findings presented here facilitate and accelerate SMP data analysis.