Remote sensing in the ‘Big Data’ era is characterised by the availability of petabytes of satellite data, facilitating observations of the cryosphere at increasingly high temporal and spatial scales. These datasets are invaluable for understanding past and contemporary changes to the cryosphere, which is particularly crucial as climate change continues and extreme events become increasingly frequent.

To fully utilise the wealth of satellite data available, the last decade has seen reliance on new approaches for accessing, processing, interpreting, and distributing results from large-scale datasets. These approaches include:
• new technologies for data access including cloud-optimised datasets;
• cloud geoprocessing platforms and on-demand data products;
• data fusion methods that combine coincident satellite observations and assimilate them into numerical and physical models;
• machine- and deep-learning approaches;
• large-scale, automated data pipelines to understand and monitor entire ice sheets, ice shelves, or glaciated and snow-covered regions;
• and a widespread philosophy of open data and software sharing to enable rapid dissemination of new approaches.

This session seeks contributions from anyone working on remote sensing of any element of the cryosphere. In particular, we welcome submissions from those researching the cryosphere using large-scale remote sensing data pipelines, machine and deep learning, cloud processing, open-source code/data, and other contemporary approaches.