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.