Manually delineated glacier calving front locations of 27 marine-terminating glaciers from 2013 to 2021

Metadata

Person(s) who is (are) responsible for the content of the research data	Loebel, Erik - Technische Universität Dresden (ORCID: 0000-0001-9874-9295)
Person(s) who is (are) responsible for the content of the research data	Scheinert, Mirko - Technische Universität Dresden (ORCID: 0000-0002-0892-8941)
Person(s) who is (are) responsible for the content of the research data	Horwath, Martin - Technische Universität Dresden (ORCID: 0000-0001-5797-244X)
Person(s) who is (are) responsible for the content of the research data	Humbert, Angelika - Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung (ORCID: 0000-0002-0244-8760)
Person(s) who is (are) responsible for the content of the research data	Sohn, Julia - Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung (ORCID: 0000-0002-5044-1192)
Person(s) who is (are) responsible for the content of the research data	Heidler, Konrad - Technische Universität München (ORCID: 0000-0001-8226-0727)
Person(s) who is (are) responsible for the content of the research data	Liebezeit, Charlotte - Technische Universität Dresden
Person(s) who is (are) responsible for the content of the research data	Zhu, Xiao Xiang - Technische Universität München (ORCID: 0000-0001-5530-3613)
Abstract	This dataset provides 898 manually delineated glacier calving front positions of 23 Greenland glaciers, two glaciers at the Antarctic Peninsula, one glacier in Svalbard and one glacier in Patagonia from 2013 to 2021. For manual delineation, we used optical Landsat-8 imagery. The dataset is composed of two parts. Firstly we provide ocean masks and frontal positions as Polygon or LineString Shapefiles respectively. Secondly, we provide 1220 input raster subsets (9 layers each) with their corresponding, manually delineated, segmentation masks. Input raster subsets have dimensions of 512 pixels by 512 pixels and are available in both png and georeferenced tif format. These machine learning ready raster subsets are optimized for training and validating artificial neural networks.
Regions the data is referencing	Greenland
Regions the data is referencing	Antarctica
Regions the data is referencing	Svalbard
Regions the data is referencing	Patagonia
Additional keywords	remote sensing
Additional keywords	satellite imagery
Additional keywords	machine learning
Additional keywords	reference data
Year or period of data production	2022
Publication year	2024
Publisher	Technische Universität Dresden
References on related materials	IsPartOf: 123456789/5680 (Handle)
References on related materials	IsSupplementTo: 10.5194/tc-2023-52 (DOI)
Content of the research data	Dataset: 898 manually delineated glacier calving front positions of 23 Greenland glaciers, two glaciers at the Antarctic Peninsula, one glacier in Svalbard and one glacier in Patagonia from 2013 to 2021 (provided as Shapefiles as well as machine learning ready raster subsets).
Holder of usage rights	Technische Universität Dresden
Usage rights of the data	CC-BY-SA-4.0
Discipline(s)	Geography	de
Discipline(s)	Geological Science	de
Title of the dataset	Manually delineated glacier calving front locations of 27 marine-terminating glaciers from 2013 to 2021

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Change pattern identification of marine-terminating outlet glaciers [5]
Marine-terminating outlet glaciers experience a combination of seasonal and climate-driven change. Nearby glaciers exhibit very different retreat and advance behavior despite being situated in similar climatic conditions. This highlights the demand to essentially improve our understanding of the driving mechanisms and to provide a basis for parameterizations of oceanic forcing that are fed into mass-loss projections. Temporal changes of glacial flow velocities are presumably linked to the evolution of the subglacial hydrological system. Depending on the type of subglacial system, the temporal acceleration of the glacier is represented by different characteristics. While this is typically investigated only along a central flow line, the spatial distribution contains more information on the cause of the acceleration. In a similar way, the spatial pattern of acceleration due to changes at the calving front is likely driven by upstream propagation of changes in stresses. Hence, understanding the mechanisms in detail requires an analysis of different physical variables in high temporal and spatial resolution and combination with ice modelling. With the new generation of satellites the era of big data has started in glaciology, and new efficient methods to analyze change patterns are required.

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