We propose a one-week summer school that will take place in 2022 that will teach Norway based PhD and Postdoc participants’ actionable machine learning (ML) skills applied to glaciology. Mapping glaciers and understanding ice dynamics as well as glacier instabilities are of great importance to society due to the large contributions of glacial water discharge in the ocean to sea level rise, use as natural clean water sources, and as a natural hazard to communities worldwide. The effort from the glaciological community with the use of numerical modeling, remote sensing and fieldwork has led to an explosion in the number and size of data available to study glaciers (e.g. the Randolph Glacier Inventory, (RGI Consortium, 2017)). The automatisation of the analysis of big data is crucial to optimally exploit these data towards improving glacier science. To our knowledge, no workshops or summer schools have been designed primarily around training glaciology PhD and Postdocs on using ML techniques. This summer school is designed to provide interdisciplinary graduate students with skills in building and digesting large glacier data sets and using ML to analyse that data.
Project leader: John Aiken
Institution: UNIVERSITETET I OSLO