An ice-water classification dataset for learning from label proportions 2015 [Canada]

An ice-water classification dataset for machine learning researchers to test algorithms related to learning from label proportions. The dataset includes patches extracted from 10 RADARSAT-2 dual-polarized scenes, Egg Code polygon (bag) IDs, labels, and the estimated proportion of each bag provided b...

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Bibliographic Details
Main Authors: Li, Fan, Taylor, Graham
Format: Other/Unknown Material
Language:unknown
Published: Borealis 2015
Subjects:
Engineering
classification
label proportion
sea ice
weakly-supervised learning
Canada
Sea ice
Online Access:https://hdl.handle.net/10864/WJ7VY
id ftborealisdata:hdl:10864/WJ7VY
record_format openpolar
spelling ftborealisdata:hdl:10864/WJ7VY 2023-05-15T18:17:39+02:00 An ice-water classification dataset for learning from label proportions 2015 [Canada] Li, Fan Taylor, Graham Li, Fan 2015 https://hdl.handle.net/10864/WJ7VY unknown Borealis https://hdl.handle.net/10864/WJ7VY The RADARSAT-2 images used in this research were obtained from the Canada Ice Service Engineering classification label proportion sea ice weakly-supervised learning Patches extracted from RADARSAT-2 images and related ground truth 2015 ftborealisdata 2022-10-10T05:53:41Z An ice-water classification dataset for machine learning researchers to test algorithms related to learning from label proportions. The dataset includes patches extracted from 10 RADARSAT-2 dual-polarized scenes, Egg Code polygon (bag) IDs, labels, and the estimated proportion of each bag provided by ice experts. Other/Unknown Material Sea ice Borealis Canada
institution Open Polar
collection Borealis
op_collection_id ftborealisdata
language unknown
topic Engineering
classification
label proportion
sea ice
weakly-supervised learning
spellingShingle Engineering
classification
label proportion
sea ice
weakly-supervised learning
Li, Fan
Taylor, Graham
An ice-water classification dataset for learning from label proportions 2015 [Canada]
topic_facet Engineering
classification
label proportion
sea ice
weakly-supervised learning
description An ice-water classification dataset for machine learning researchers to test algorithms related to learning from label proportions. The dataset includes patches extracted from 10 RADARSAT-2 dual-polarized scenes, Egg Code polygon (bag) IDs, labels, and the estimated proportion of each bag provided by ice experts.
author2 Li, Fan
format Other/Unknown Material
author Li, Fan
Taylor, Graham
author_facet Li, Fan
Taylor, Graham
author_sort Li, Fan
title An ice-water classification dataset for learning from label proportions 2015 [Canada]
title_short An ice-water classification dataset for learning from label proportions 2015 [Canada]
title_full An ice-water classification dataset for learning from label proportions 2015 [Canada]
title_fullStr An ice-water classification dataset for learning from label proportions 2015 [Canada]
title_full_unstemmed An ice-water classification dataset for learning from label proportions 2015 [Canada]
title_sort ice-water classification dataset for learning from label proportions 2015 [canada]
publisher Borealis
publishDate 2015
url https://hdl.handle.net/10864/WJ7VY
geographic Canada
geographic_facet Canada
genre Sea ice
genre_facet Sea ice
op_source The RADARSAT-2 images used in this research were obtained from the Canada Ice Service
op_relation https://hdl.handle.net/10864/WJ7VY
_version_ 1766192327329579008

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