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Published January 20, 2024 | Version v2
Dataset Open

Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica"

  • 1. Université libre de Bruxelles
  • 2. ETH Zürich
  • 3. EPFL
  • 4. Yale University
  • 5. KU Leuven
  • 6. TU Delft

Description

This archive provides datasets related to the following publication:

V. Tollenaar, H. Zekollari, F. Pattyn, M. Rußwurm, B. Kellenberger, S. Lhermitte, M. Izeboud, D. Tuia, Where the White Continent is blue: deep learning locates bare ice in Antarctica. Geophysical Research Letters, 50, e2023GL106285 (2024). DOI: 10.1029/2023GL106285

Contact: Veronica Tollenaar, Veronica.Tollenaar@ulb.be

Users should cite the original publication when using all or part of the data.

About the datasets: it includes the seven-band MODIS composite ("merged_bands_composite3031080910.tif"), the outlines of our set of training ("train_squares.zip"), validation ("validation_squares.zip"), and testing regions ("test_squares.zip"), the predictions on the presence of blue ice ("BIA_map.nc"), the corresponding uncertainty estimates ("BIA_map.nc"), and the smoothed blue ice area outlines ("smoothed_BIAs.zip"). References to the literature are provided in the corresponding manuscript.

- BIA_map.nc: netcdf in which the band "mean" represents the predictions on the presence of blue ice, and the band "std" the corresponding uncertainty estimates, in EPSG:3031
- handlabels_sqXXX.zip: archived .cpg, .bdf, .prj, .shp, and .shx files of the handlabeled blue ice areas (Figures S3 and S4) in the respective squares (indicated in the files "xxxx_squares.zip")
- merged_bands_composite3031080910.tif: 7-band tif file of the 7-band MODIS composite (Figure S1, Text S1)
- smoothed_BIAs.zip: archived .cpg, .bdf, .prj, .shp, and .shx files of the smoothed outlines of our BIA predictions (Text S5)
- xxxx_squares.zip: archived .cpg, .bdf, .prj, .shp, and .shx files of the outlines of training, validation, and testing regions (Figure S2)

Files

handlabels_sq246.zip

Files (15.1 GB)

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md5:e15c3314234cc88cb5af2630670d79f1
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