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Instructor notes

Notes from the theoretical courses taught during the Machine Learning in Glaciology workshop, at the Finse research station (Norway).

Lecture 1: Physics-based machine learning for glacier modelling

Authors: Jordi Bolibar, Facundo Sapienza

The presentation introduces students to the general concepts of a machine learning pipeline. How to properly design a dataset, how to correctly train models and how validate, test and understand the capabilities and limitation of the model(s).

The following contents are covered:

  • Modelling the glacier system

    • Glacier evolution models
    • Local vs Global glacier modelling

  • Physics-based machine learning

    • Machine learning pipelines
    • Regression for physical processes
      • Respecting physics
        • Feature selection
        • Data driven machine learning
        • Physical losses or Physics-Informed Neural Networks
        • Neural/Universal Differential Equations
      • Trustworthy models
        • Testing and validation
        • Physical interpretation
      • Being mindful about model limitations

  • Project description

Lecture 2: Deep Learning for remote sensing and glacier mapping

Authors: Benjamin Robson, Konstantin Maslov and Thomas Schellenberger

The three presentations will cover:

  • Remote Sensing in Glaciology Remote Sensing in Glaciology – the traditional basics

    • Intro Optical and SAR remote sensing and their applications in Glaciology
    • Glacier extend mapping
    • Glacier zone mapping
    • Challenges

  • Random Forest and Deep learning image classification for Glacier Mapping

    • Intro to ML image classification
    • Random Forest
    • Deep learning

  • Cryospheric Mapping with Remote Sensing - an overview of the problems, data and methods (with a focus on OBIA and debris-covered glaciers

    • Object based image analysis
    • Mapping debris-covered glaciers

  • Project description The students will have the opportunity to work to mapping glaciers in High Mountain Asia using Sentinel-1 and Sentinel-2 data and pre-trained random forest and DL models as well as OBIA. They are encouraged to tune and train additional random forest models with a number of different input features and to compare the performance of the three approaches statistically.

Lecture 3: Introduction to JupyterHub and Git

Authors: Facundo Sapienza and Ellianna Abrahams

Lecture 4: Understanding Statistical Methods for the Sciences

Author: Ellianna Abrahams