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