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  • Technische Universität Dresden
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  • Change pattern identification of outlet glaciers in Greenland
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  • Technische Universität Dresden
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  • Bereich Bau und Umwelt
  • Fakultät Umweltwissenschaften
  • Fachrichtung Geowissenschaften
  • Institut für Planetare Geodäsie
  • Professur für Geodätische Erdsystemforschung
  • Artificial Intelligence for Cold Regions (AI-CORE)
  • Change pattern identification of outlet glaciers in Greenland
  • Datenpaketanzeige
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Manually delineated glacier calving fronts of 23 Greenland and 2 Antarctic outlet glaciers from 2013 to 2021 and source code for automated extraction by deep learningOpen Access Icon

Thumbnail
calving front positions in polygon and linestring shapefile format (7.024Mb)
contents.txt (2.557Mb)
machine learning ready input raster subsets in georeferenced tif format (3.407Gb)
machine learning ready input raster subsets in png format (2.107Gb)
source code for automated extraction by deep learning (15.46Mb)
data-license.txt (CC-BY-NC-4.0) (13.40Kb)
Datum
2022
Autor
Loebel, Erik
Scheinert, Mirko
Horwath, Martin
Heidler, Konrad
Christmann, Julia
Phan, Long Duc
Humbert, Angelika
Xiao Xiang, Zhu
Metadaten
Zur Langanzeige
Zusammenfassung
Supplementary material to the manuscript: Loebel, E., Scheinert, M., Horwath, M., Heidler, K., Christmann, J., Phan, L., Humbert, A., Zhu, X. (2022): Extracting glacier calving fronts by deep learning: the benefit of multi-spectral, topographic and textural input features, IEEE Transactions on Geoscience and Remote Sensing. The dataset is composed of three parts. Firstly, this dataset provides 728 manually delineated glacier calving front positions of 23 Greenland and 2 Antarctic outlet glaciers from 2013 to 2021. For that, we provide ocean masks and frontal positions as Polygon or LineString Shapefiles respectively. For manual delineation, we used optical Landsat-8 imagery. Secondly, we provide 995 input raster subsets (17 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. Thirdly, we lay out our source code we use for neural network training and accuracy assessment. The code uses python3 and shell scripts.
URI
https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5721
http://dx.doi.org/10.25532/OPARA-183
Sammlungen
  • Change pattern identification of outlet glaciers in Greenland [2]Open Access Icon

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