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.
In order to fully utilise the wealth of satellite data available, the last decade has seen reliance on new approaches for (i) accessing, (ii) processing, (iii) interpreting, and (iv) distributing results from large-scale datasets. This includes new technologies for data access including cloud-optimised datasets; cloud geoprocessing platforms such as Google Earth Engine, Microsoft Planetary Computer, and community JupyterHubs; the increasing use of large-scale data pipelines and machine/deep learning methods to understand and monitor entire ice sheets, ice shelves, or glaciated regions; and a widespread philosophy of open data and code sharing to enable rapid dissemination of new approaches.
This session seeks contributions from anyone working on remote sensing of ice sheets, ice shelves, and glaciers. In particular, we welcome submissions from those researching the cryosphere using cloud data and processing, large-scale data pipelines, machine and deep learning, open code/data, and other contemporary approaches.