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, Dumitru Octavian, Datcu Mihai
Format: Other/Unknown Material
Language:English
Published: Zenodo 2021
Subjects:
Latent Dirichlet Allocation
Topics
Sentinel-1
Greenland
Sea ice
Online Access:https://doi.org/10.5281/zenodo.5075861
id ftzenodo:oai:zenodo.org:5075861
record_format openpolar
spelling ftzenodo:oai:zenodo.org:5075861 2024-09-15T18:09:42+00:00 Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland Karmakar Chandrabali Dumitru Octavian Datcu Mihai 2021-07-06 https://doi.org/10.5281/zenodo.5075861 eng eng Zenodo https://doi.org/10.1109/JSTARS.2020.3039012 https://zenodo.org/communities/polarops https://zenodo.org/communities/eu https://doi.org/10.5281/zenodo.5075860 https://doi.org/10.5281/zenodo.5075861 oai:zenodo.org:5075861 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Latent Dirichlet Allocation Topics Sentinel-1 info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5281/zenodo.507586110.1109/JSTARS.2020.303901210.5281/zenodo.5075860 2024-07-26T22:07:19Z 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. Other/Unknown Material Greenland Sea ice Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
topic Latent Dirichlet Allocation
Topics
Sentinel-1
spellingShingle Latent Dirichlet Allocation
Topics
Sentinel-1
Karmakar Chandrabali
Dumitru Octavian
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.
format Other/Unknown Material
author Karmakar Chandrabali
Dumitru Octavian
Datcu Mihai
author_facet Karmakar Chandrabali
Dumitru Octavian
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://doi.org/10.5281/zenodo.5075861
genre Greenland
Sea ice
genre_facet Greenland
Sea ice
op_relation https://doi.org/10.1109/JSTARS.2020.3039012
https://zenodo.org/communities/polarops
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5075860
https://doi.org/10.5281/zenodo.5075861
oai:zenodo.org:5075861
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.507586110.1109/JSTARS.2020.303901210.5281/zenodo.5075860
_version_ 1810447275850203136

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