Tian Li, Konrad Heidler, Lichao Mou, Ádám Ignéczi, Xiao Xiang Zhu, and Jonathan L. Bamber
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-396,https://doi.org/10.5194/essd-2023-396, 2023
Preprint under review for ESSD
Our study uses deep learning to create a new high-resolution dataset of calving front locations for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124,919 terminal 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 assessments.
Erik Loebel, Mirko Scheinert, Martin Horwath, Angelika Humbert, Julia Sohn, Konrad Heidler, Charlotte Liebezeit, and Xiao Xiang Zhu
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-52,https://doi.org/10.5194/tc-2023-52, 2023
Preprint under review for TC
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.
Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.