Ice loss in Greenland and Antarctica has accelerated in recent decades. Melting polar ice sheets and mountain glaciers have considerable influence on sea level rise (SLR) and ocean currents, potential floods in coastal regions could put millions of people around the world at risk. The Intergovernmental Panel on Climate Change (IPCC) estimates that sea level could increase by 26–98cm by the end of this century. This large range in predicted SLR can be partially attributed to an incomplete understanding of bed topography and basal conditions in fast-flowing regions of ice sheets in Greenland and Antarctica. Therefore, a precise calculation of ice thickness is very important for sea level and flood monitoring. Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of available arctic and Antarctic data is unlabeled, and the labeling process is both time-consuming and expensive and requires a significant amount of domain experts’ time that could otherwise be spent on high-level scientific discovery.
NASA scientists have provided some initial label data for some small datasets. In this project, we are planning to use Sagmeaker tools to predict labels for the remaining data and engage the scientists so they can verify and improve the labeling process. After generating a good label data, we will develop several hybrid models to improve the accuracy of tracking the internal layers of ice sheet and calculating thickness. We will also use the weather models as an initial predication for deep learning and then improve the prediction model with active learning using data-driven approaches.
This research is supported by Amazon Machine Learning Research Award .