Manually delineated glacier calving front locations of 27 marine-terminating glaciers from 2013 to 2021
Documentation of the data | 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). | |
Additional geographical or spatial references | Greenland | |
Additional geographical or spatial references | Svalbard | |
Additional geographical or spatial references | Antarctica | |
Additional geographical or spatial references | Patagonia | |
References to related material | 10.5194/tc-2023-52 | |
Description of the data | Change pattern identification of marine-terminating outlet glaciers 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. | |
Type of the data | Dataset | |
Total size of the dataset | 4134511002 | |
Author | Loebel, Erik | |
Author | Scheinert, Mirko | |
Author | Horwath, Martin | |
Author | Humbert, Angelika | |
Author | Sohn, Julia | |
Author | Heidler, Konrad | |
Author | Liebezeit, Charlotte | |
Author | Zhu, Xiao Xiang | |
Upload date | 2024-01-18T15:12:00Z | |
Publication date | 2024-01-18T15:12:00Z | |
Publication date | 2026-06-12T12:44:00Z | |
Data of data creation | 2022 | |
Publication date | 2024-01-18 | |
Abstract of the dataset | 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. | |
Public reference to this page | https://opara.zih.tu-dresden.de/handle/123456789/2708 | |
Public reference to this page | https://doi.org/10.25532/OPARA-282 | |
Publisher | Technische Universität Dresden | |
Licence | Attribution-ShareAlike 4.0 International | |
URI of the licence text | http://creativecommons.org/licenses/by-sa/4.0/ | |
Specification of the discipline(s) | 3::34::317 | |
Specification of the discipline(s) | 3::34 | |
Title of the dataset | Manually delineated glacier calving front locations of 27 marine-terminating glaciers from 2013 to 2021 | |
Project abstract | In “Artificial Intelligence for Cold Regions” (AI-CORE) we will develop a collaborative approach for applying Artificial Intelligence (AI) methods in earth observation and thereby breaking new ground for researching the cryosphere. Rapidly changing ice sheets and thawing permafrost are big societal challenges, hence quantifying these changes and understanding the mechanisms are of major importance. Given the vast extent of polar regions and the availability of exponentially increasing satellite remote sensing data, intelligent data analysis is urgently required to exploit the full information in satellite time series. So far, extensive competences in data science, AI implementation, and processing infrastructures are decentralized and distributed among the individual Helmholtz centers. In the era of big data, cloud computing and extensive earth observation programs, a core challenge is to establish and consolidate a joint platform for AI applications by combining existing competences and infrastructures with new developments serving especially AI applications. Four geo-scientific use cases from cryosphere research will be used to demonstrate the new collaborative AI approach. These use cases are challenging due to diverse, extensive, and inhomogeneous input data and their high relevance is given in the context of climate change. To address these case studies, several AI methods will be developed, tested, evaluated, and implemented in the data processing infrastructure of the project members by combining all distributed capabilities into a joint platform. A “best practice” approach will be identified to solve each of the individual research questions. Once established, this knowledge and the AI-CORE platform can be used even beyond the exemplary use cases to address current and upcoming challenges in data processing, management, data science, and big data. The experience of this collaborative approach will be of very high value for the next research program PoF IV. Furthermore, the networking and knowledge exchange among AI-CORE members will facilitate synergies between methodsbased research and direct AI applications beyond the immediate use cases. | |
Project title | Artificial Intelligence for Cold Regions (AI-CORE) |
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