Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica"
This archive provides datasets related to the following manuscript: 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, [Paper # 2023GL106285] Contact: Veronica Tollena...
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ftzenodo:oai:zenodo.org:8333865 2024-09-15T17:48:25+00:00 Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica" Tollenaar, Veronica Zekollari, Harry Pattyn, Frank Rußwurm, Marc Kellenberger, Benjamin Lhermitte, Stef Izeboud, Maaike Tuia, Devis 2023-09-11 https://doi.org/10.5281/zenodo.8333865 unknown Zenodo https://doi.org/10.5281/zenodo.8333864 https://doi.org/10.5281/zenodo.8333865 oai:zenodo.org:8333865 info:eu-repo/semantics/restrictedAccess blue ice areas Antarctica info:eu-repo/semantics/other 2023 ftzenodo https://doi.org/10.5281/zenodo.833386510.5281/zenodo.8333864 2024-07-26T15:57:30Z This archive provides datasets related to the following manuscript: 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, [Paper # 2023GL106285] Contact: Veronica Tollenaar, Veronica.Tollenaar@ulb.be Data is provided for review and should not be shared. 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 ("final_map.nc"), the corresponding uncertainty estimates ("final_map.nc"), and the smoothed blue ice area outlines ("smoothed_BIAs.zip"). References to the literature are provided in the corresponding manuscript. - final_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 - 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) Other/Unknown Material Antarc* Antarctica Zenodo |
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unknown |
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blue ice areas Antarctica |
spellingShingle |
blue ice areas Antarctica Tollenaar, Veronica Zekollari, Harry Pattyn, Frank Rußwurm, Marc Kellenberger, Benjamin Lhermitte, Stef Izeboud, Maaike Tuia, Devis Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica" |
topic_facet |
blue ice areas Antarctica |
description |
This archive provides datasets related to the following manuscript: 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, [Paper # 2023GL106285] Contact: Veronica Tollenaar, Veronica.Tollenaar@ulb.be Data is provided for review and should not be shared. 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 ("final_map.nc"), the corresponding uncertainty estimates ("final_map.nc"), and the smoothed blue ice area outlines ("smoothed_BIAs.zip"). References to the literature are provided in the corresponding manuscript. - final_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 - 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) |
format |
Other/Unknown Material |
author |
Tollenaar, Veronica Zekollari, Harry Pattyn, Frank Rußwurm, Marc Kellenberger, Benjamin Lhermitte, Stef Izeboud, Maaike Tuia, Devis |
author_facet |
Tollenaar, Veronica Zekollari, Harry Pattyn, Frank Rußwurm, Marc Kellenberger, Benjamin Lhermitte, Stef Izeboud, Maaike Tuia, Devis |
author_sort |
Tollenaar, Veronica |
title |
Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica" |
title_short |
Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica" |
title_full |
Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica" |
title_fullStr |
Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica" |
title_full_unstemmed |
Datasets for "Where the White Continent is blue: deep learning locates bare ice in Antarctica" |
title_sort |
datasets for "where the white continent is blue: deep learning locates bare ice in antarctica" |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://doi.org/10.5281/zenodo.8333865 |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://doi.org/10.5281/zenodo.8333864 https://doi.org/10.5281/zenodo.8333865 oai:zenodo.org:8333865 |
op_rights |
info:eu-repo/semantics/restrictedAccess |
op_doi |
https://doi.org/10.5281/zenodo.833386510.5281/zenodo.8333864 |
_version_ |
1810289623444750336 |