Convener: James Lea | Co-conveners: Celia A. Baumhoer, Stephen BroughECSECS, Soroush REZVANBEHBAHANIECSECS, Leigh Stearns
| Attendance Wed, 06 May, 10:45–12:30 (CEST)

The explosion of data and computing power that is now available to glaciologists presents significant opportunities for advancing our understanding of glacial environments. However, significant barriers exist to achieving this, with the scales and rates at which data are being generated rendering many traditional approaches to analysis impractical.
Researchers across nearly all fields of glaciology are therefore increasingly requiring the development of automated and/or machine learning based approaches to effectively monitor and investigate these environments, in addition to new ways of visualising results. This session will therefore bring together glaciologists who use big data, machine learning and/or artificial intelligence to help share knowledge of different approaches that are currently being taken by the community and where possible demonstrate their potential transferability in this emergent field. Contributions are invited from those involved in developing and/or applying methods that seek to address these data generation, analytical and visualisation challenges with the aim of gaining greater understanding of past, present and future glacier and ice sheet change.

Public information:
During the chat session we will provide the opportunity for our presenters to briefly introduce their work and answer questions. If there is time, we will also facilitate a discussion for authors and attendees to discuss the following topics:

1. What are the challenges experienced by those applying big data/machine learning/AI techniques, and how can those involved in other areas of glaciology help?

2. What are the challenges of experienced by those using big data/machine learning/AI data products, and how can those creating them make them more accessible?

Other topics for discussion are very much encouraged both from those working with big/machine learning/AI data and those who may be interested in the potential of such approaches in glaciology.