Retrieving sea, lake and river ice information is important for modelling weather and climate changes, and understanding ecological dynamics and the planet's response to anthropogenic forcing. Understanding ice conditions is also important for local indigenous communities, and for wider economic, logistical, and operational activities, such as navigation, tourism and freight transport.

The recent decade has seen an exponential increase in the collection and availability of remote sensing data pertaining to sea, lake and river ice conditions. Extracting meaningful information and developing data products at scale derived from remote sensing necessitates digital technologies for the storage, preparation, collation and analysis of these big datasets, such as AI or other high-performance computing techniques.

This session welcomes contributions utilising techniques such as segmentation, detection, forecasting, resolution enhancement, data infilling and data fusion that provide novel insights into sea, lake or river ice. Submissions are particularly encouraged that push the boundaries of the spatiotemporal resolution of the ingested data or improve on existing data products, as well as those that enable new advancements in AI or other techniques providing emerging insights into ice conditions.

Furthermore, this session aims to integrate sea, lake or river ice experts with AI and model specialists to exchange new knowledge in algorithm development regarding ice information retrieval.