Preprints
https://doi.org/10.5194/egusphere-2024-1752
https://doi.org/10.5194/egusphere-2024-1752
22 Jul 2024
 | 22 Jul 2024

Towards deep learning solutions for classification of automated snow height measurements (CleanSnow v1.0.0)

Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer

Abstract. Snow height measurements are still the backbone of any snow cover monitoring whether based on modeling or remote sensing. These ground-based measurements are often realized with the use of ultrasonic or laser technologies. In challenging environments, such as high alpine regions, the quality of sensor measurements deteriorates quickly, especially in the presence of extreme weather conditions or ephemeral snow conditions. Moreover, the sensors by their nature measure the height of an underlying object and are therefore prone to return other information, such as the height of vegetation, in snow-free periods. Quality assessment and real-time classification of automated snow height measurements is therefore desirable in order to provide high-quality data for research and operational applications. To this end, we propose CleanSnow, a machine learning approach to automated classification of snow height measurements into a snow cover class and a class corresponding to everything else, which takes into account both the temporal context and the dependencies between snow height and other sensor measurements. We created a new dataset of manually annotated snow height measurements, which allowed us to train our models in a supervised manner as well as quantitatively evaluate our results. Through a series of experiments and ablation studies to evaluate feature importance and compare several different models, we validated our design choices and demonstrate the importance of using temporal information together with information from auxiliary sensors. CleanSnow achieved a high accuracy and represents a new baseline for further research in the field. The presented approach to snow height classification finds its use in various tasks, ranging from snow modeling to climate science.

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Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2024-1752', Juan Antonio Añel, 14 Aug 2024
    • AC1: 'Reply on CEC1', Jan Svoboda, 15 Aug 2024
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 15 Aug 2024
    • CC1: 'Reply on CEC1', I. Iosifescu Enescu, 17 Aug 2024
      • CEC3: 'Reply on CC1', Juan Antonio Añel, 17 Aug 2024
        • CC2: 'Reply on CEC3', I. Iosifescu Enescu, 19 Aug 2024
          • CEC4: 'Reply on CC2', Juan Antonio Añel, 15 Oct 2024
            • AC4: 'Reply on CEC4', Jan Svoboda, 16 Oct 2024
  • RC1: 'Comment on egusphere-2024-1752', Anonymous Referee #1, 23 Sep 2024
    • AC2: 'Reply on RC1', Jan Svoboda, 15 Oct 2024
  • RC2: 'Comment on egusphere-2024-1752', Anonymous Referee #2, 03 Oct 2024
    • AC3: 'Reply on RC2', Jan Svoboda, 15 Oct 2024
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer

Data sets

Snow Height Classification Dataset Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer https://doi.org/10.16904/envidat.512

Model code and software

Towards deep learning solutions for classification of automated snow height measurements (CleanSnow v1.0.0) Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer https://doi.org/10.5281/zenodo.12698071

Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer

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Short summary
Accurately measuring snow height is key for modeling approaches in climate sciences, snow hydrology and avalanche forecasting. Erroneous snow height measurements often occur when the snow height is low or changes, for instance, during a snowfall in the summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep learning approaches. Our approach can be easily implemented into a data pipeline for snow modeling.