FUSING MULTI-MODAL DATA FOR SUPERVISED CHANGE DETECTION

Our study uses deep learning to produce a new high-resolution calving front dataset for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124 919 terminus traces. This dataset offers insights into understanding calving mechanisms and can help improve glacier frontal ablation estimates as a component of the integrated mass balance assessment.

Comprehensive data sets of calving front change are essential to study and model outlet glaciers. Current records are limited in temporal resolution as they rely on manual delineation. We apply deep learning to automatically delineate calving fronts of 23 Greenland glaciers. Resulting time series resolve long-term, seasonal and sub-seasonal patterns. We discuss the implications of our results and provide the cryosphere community with a data product and an implementation of our processing system.