Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland

Data description File type : -.npy (python numpy file) File content: Each file is a numpy array of size (number of 256x256 patches, 4096) indexed by id of the patch (each scene contains 6,400 patches, each patch has 4,096 micropatches of size 4x4, assigned one topic [1] per micropatch, resulting in...

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Bibliographic Details
Main Authors: Karmakar Chandrabali, Octavian, Dumitru, Datcu Mihai
Format: Dataset
Language:English
Published: Zenodo 2021
Subjects:
Online Access:https://dx.doi.org/10.5281/zenodo.5075860
https://zenodo.org/record/5075860
id ftdatacite:10.5281/zenodo.5075860
record_format openpolar
spelling ftdatacite:10.5281/zenodo.5075860 2023-05-15T16:29:03+02:00 Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland Karmakar Chandrabali Octavian, Dumitru Datcu Mihai 2021 https://dx.doi.org/10.5281/zenodo.5075860 https://zenodo.org/record/5075860 en eng Zenodo https://zenodo.org/communities/polarops https://dx.doi.org/10.1109/jstars.2020.3039012 https://dx.doi.org/10.5281/zenodo.5075861 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 Latent Dirichlet Allocation, Topics, Sentinel-1 dataset Dataset 2021 ftdatacite https://doi.org/10.5281/zenodo.5075860 https://doi.org/10.1109/jstars.2020.3039012 https://doi.org/10.5281/zenodo.5075861 2021-11-05T12:55:41Z Data description File type : -.npy (python numpy file) File content: Each file is a numpy array of size (number of 256x256 patches, 4096) indexed by id of the patch (each scene contains 6,400 patches, each patch has 4,096 micropatches of size 4x4, assigned one topic [1] per micropatch, resulting in 4,096 topics per patch). Each file has 4 months of observation. Array size is 25600 x 4096. We provide 6 files containing 24 months of observation (see the excel file for the Sentinel-1 ids) [2]. Software to open with: Python Example code: import numpy Data= numpy.load(“filename_with_path”) Reference: 1. 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. 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). 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 The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
topic Latent Dirichlet Allocation, Topics, Sentinel-1
spellingShingle Latent Dirichlet Allocation, Topics, Sentinel-1
Karmakar Chandrabali
Octavian, Dumitru
Datcu Mihai
Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland
topic_facet Latent Dirichlet Allocation, Topics, Sentinel-1
description Data description File type : -.npy (python numpy file) File content: Each file is a numpy array of size (number of 256x256 patches, 4096) indexed by id of the patch (each scene contains 6,400 patches, each patch has 4,096 micropatches of size 4x4, assigned one topic [1] per micropatch, resulting in 4,096 topics per patch). Each file has 4 months of observation. Array size is 25600 x 4096. We provide 6 files containing 24 months of observation (see the excel file for the Sentinel-1 ids) [2]. Software to open with: Python Example code: import numpy Data= numpy.load(“filename_with_path”) Reference: 1. 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. 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).
format Dataset
author Karmakar Chandrabali
Octavian, Dumitru
Datcu Mihai
author_facet Karmakar Chandrabali
Octavian, Dumitru
Datcu Mihai
author_sort Karmakar Chandrabali
title Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland
title_short Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland
title_full Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland
title_fullStr Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland
title_full_unstemmed Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland
title_sort sea-ice data content representation based on latent dirichlet allocation for belgica bank in greenland
publisher Zenodo
publishDate 2021
url https://dx.doi.org/10.5281/zenodo.5075860
https://zenodo.org/record/5075860
long_lat ENVELOPE(-15.000,-15.000,78.467,78.467)
ENVELOPE(73.317,73.317,-52.983,-52.983)
geographic Belgica Bank
Greenland
The Sentinel
geographic_facet Belgica Bank
Greenland
The Sentinel
genre Greenland
Sea ice
genre_facet Greenland
Sea ice
op_relation https://zenodo.org/communities/polarops
https://dx.doi.org/10.1109/jstars.2020.3039012
https://dx.doi.org/10.5281/zenodo.5075861
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.5075860
https://doi.org/10.1109/jstars.2020.3039012
https://doi.org/10.5281/zenodo.5075861
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