Semantic Sea-Ice Classification for Belgica Bank in Greenland
Each Sentinel-1 image is tiled into patches of 256x256 pixels. The size of the images is different and we reduced to the smallest one. In total for each image are 6,400 patches [1-2, 4]. See the excel file for the 24 Sentinel-1 ids. The semantic classes are: Black border Old ice First-Year ice Glaci...
Main Authors: | , , |
---|---|
Format: | Dataset |
Language: | unknown |
Published: |
Zenodo
2021
|
Subjects: | |
Online Access: | https://dx.doi.org/10.5281/zenodo.5075448 https://zenodo.org/record/5075448 |
id |
ftdatacite:10.5281/zenodo.5075448 |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.5281/zenodo.5075448 2023-05-15T16:29:27+02:00 Semantic Sea-Ice Classification for Belgica Bank in Greenland Octavian, Dumitru Karmakar Chandrabali Datcu Mihai 2021 https://dx.doi.org/10.5281/zenodo.5075448 https://zenodo.org/record/5075448 unknown Zenodo https://igarss2021.com/view_paper.php?PaperNum=3147 https://zenodo.org/communities/polarops https://igarss2021.com/view_paper.php?PaperNum=3147 https://dx.doi.org/10.1109/jstars.2020.3039012 https://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-83-2019 https://dx.doi.org/10.5281/zenodo.5075447 https://zenodo.org/communities/polarops Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Semantic labelling, sea-ice classes, active learning dataset Dataset 2021 ftdatacite https://doi.org/10.5281/zenodo.5075448 https://doi.org/10.1109/jstars.2020.3039012 https://doi.org/10.5194/isprs-archives-xlii-2-w16-83-2019 https://doi.org/10.5281/zenodo.5075447 2021-11-05T12:55:41Z Each Sentinel-1 image is tiled into patches of 256x256 pixels. The size of the images is different and we reduced to the smallest one. In total for each image are 6,400 patches [1-2, 4]. See the excel file for the 24 Sentinel-1 ids. The semantic classes are: Black border Old ice First-Year ice Glaciers Icebergs Mountains Young ice Water group The last class combines the Floating ice, Water body, Water ice current and melted snow defined in [3] because they have very similar physical properties. References: 1. C.O. Dumitru, G. Schwarz, C. Karmakar, and M. Datcu, “Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images”, IGARSS, Belgium, July 2021, pp. 1-4. 2. C. Karmakar, C.O. Dumitru, and M. Datcu, “Explainable AI for SAR Image Time Series: Knowledge Extraction for Polar Areas”, MDPI Remote Sensing Journal, 2021, pp. 1-21 (under review). 3. C.O. Dumitru, V. Andrei, G. Schwarz, and M. Datcu, “Machine Learning for Sea Ice Monitoring from Satellites”, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W16, pp. 83-89, 2019. 4. C. Karmakar, C.O. Dumitru, G. Schwarz, and M. Datcu, “ Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation ”, IEEE JSTARS, vol. 14, pp. 676-689, 2021. Dataset Greenland Sea ice DataCite Metadata Store (German National Library of Science and Technology) Belgica Bank ENVELOPE(-15.000,-15.000,78.467,78.467) Greenland |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Semantic labelling, sea-ice classes, active learning |
spellingShingle |
Semantic labelling, sea-ice classes, active learning Octavian, Dumitru Karmakar Chandrabali Datcu Mihai Semantic Sea-Ice Classification for Belgica Bank in Greenland |
topic_facet |
Semantic labelling, sea-ice classes, active learning |
description |
Each Sentinel-1 image is tiled into patches of 256x256 pixels. The size of the images is different and we reduced to the smallest one. In total for each image are 6,400 patches [1-2, 4]. See the excel file for the 24 Sentinel-1 ids. The semantic classes are: Black border Old ice First-Year ice Glaciers Icebergs Mountains Young ice Water group The last class combines the Floating ice, Water body, Water ice current and melted snow defined in [3] because they have very similar physical properties. References: 1. C.O. Dumitru, G. Schwarz, C. Karmakar, and M. Datcu, “Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images”, IGARSS, Belgium, July 2021, pp. 1-4. 2. C. Karmakar, C.O. Dumitru, and M. Datcu, “Explainable AI for SAR Image Time Series: Knowledge Extraction for Polar Areas”, MDPI Remote Sensing Journal, 2021, pp. 1-21 (under review). 3. C.O. Dumitru, V. Andrei, G. Schwarz, and M. Datcu, “Machine Learning for Sea Ice Monitoring from Satellites”, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W16, pp. 83-89, 2019. 4. C. Karmakar, C.O. Dumitru, G. Schwarz, and M. Datcu, “ Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation ”, IEEE JSTARS, vol. 14, pp. 676-689, 2021. |
format |
Dataset |
author |
Octavian, Dumitru Karmakar Chandrabali Datcu Mihai |
author_facet |
Octavian, Dumitru Karmakar Chandrabali Datcu Mihai |
author_sort |
Octavian, Dumitru |
title |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_short |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_full |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_fullStr |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_full_unstemmed |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_sort |
semantic sea-ice classification for belgica bank in greenland |
publisher |
Zenodo |
publishDate |
2021 |
url |
https://dx.doi.org/10.5281/zenodo.5075448 https://zenodo.org/record/5075448 |
long_lat |
ENVELOPE(-15.000,-15.000,78.467,78.467) |
geographic |
Belgica Bank Greenland |
geographic_facet |
Belgica Bank Greenland |
genre |
Greenland Sea ice |
genre_facet |
Greenland Sea ice |
op_relation |
https://igarss2021.com/view_paper.php?PaperNum=3147 https://zenodo.org/communities/polarops https://igarss2021.com/view_paper.php?PaperNum=3147 https://dx.doi.org/10.1109/jstars.2020.3039012 https://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-83-2019 https://dx.doi.org/10.5281/zenodo.5075447 https://zenodo.org/communities/polarops |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.5075448 https://doi.org/10.1109/jstars.2020.3039012 https://doi.org/10.5194/isprs-archives-xlii-2-w16-83-2019 https://doi.org/10.5281/zenodo.5075447 |
_version_ |
1766019150958821376 |