Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island ...

The dataset represents aerial photographs taken from a UAV on Matveev Island, Russian Federation. There are a total of 197 images with a minimum resolution of 1632x1088 and a maximum of 5472x3648 (WxH). The main directory "walruses" contains three directories ("images", "mar...

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Bibliographic Details
Main Authors: Bogomolova, Iuliia, Efremov, Vlad, Leus, Andrew
Format: Dataset
Language:unknown
Published: Zenodo 2024
Subjects:
Computer vision
Walrus
Odobenus rosmarus
Deep Learning
walrus*
Online Access:https://dx.doi.org/10.5281/zenodo.10803921
https://zenodo.org/doi/10.5281/zenodo.10803921
id ftdatacite:10.5281/zenodo.10803921
record_format openpolar
spelling ftdatacite:10.5281/zenodo.10803921 2024-04-28T08:35:05+00:00 Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island ... Bogomolova, Iuliia Efremov, Vlad Leus, Andrew 2024 https://dx.doi.org/10.5281/zenodo.10803921 https://zenodo.org/doi/10.5281/zenodo.10803921 unknown Zenodo https://dx.doi.org/10.5281/zenodo.10803920 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Computer vision Walrus Odobenus rosmarus Deep Learning dataset Dataset 2024 ftdatacite https://doi.org/10.5281/zenodo.1080392110.5281/zenodo.10803920 2024-04-02T10:49:58Z The dataset represents aerial photographs taken from a UAV on Matveev Island, Russian Federation. There are a total of 197 images with a minimum resolution of 1632x1088 and a maximum of 5472x3648 (WxH). The main directory "walruses" contains three directories ("images", "markup", "masks"). The data is marked for instance segmentation in the form of json files. The minimum number of objects per image is 22, and the maximum is 948. ... Dataset Odobenus rosmarus walrus* DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Computer vision
Walrus
Odobenus rosmarus
Deep Learning
spellingShingle Computer vision
Walrus
Odobenus rosmarus
Deep Learning
Bogomolova, Iuliia
Efremov, Vlad
Leus, Andrew
Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island ...
topic_facet Computer vision
Walrus
Odobenus rosmarus
Deep Learning
description The dataset represents aerial photographs taken from a UAV on Matveev Island, Russian Federation. There are a total of 197 images with a minimum resolution of 1632x1088 and a maximum of 5472x3648 (WxH). The main directory "walruses" contains three directories ("images", "markup", "masks"). The data is marked for instance segmentation in the form of json files. The minimum number of objects per image is 22, and the maximum is 948. ...
format Dataset
author Bogomolova, Iuliia
Efremov, Vlad
Leus, Andrew
author_facet Bogomolova, Iuliia
Efremov, Vlad
Leus, Andrew
author_sort Bogomolova, Iuliia
title Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island ...
title_short Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island ...
title_full Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island ...
title_fullStr Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island ...
title_full_unstemmed Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island ...
title_sort aerial photographs of atlantic walruses subspecies odobenus rosmarus rosmarus on matveev island ...
publisher Zenodo
publishDate 2024
url https://dx.doi.org/10.5281/zenodo.10803921
https://zenodo.org/doi/10.5281/zenodo.10803921
genre Odobenus rosmarus
walrus*
genre_facet Odobenus rosmarus
walrus*
op_relation https://dx.doi.org/10.5281/zenodo.10803920
op_rights Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
op_doi https://doi.org/10.5281/zenodo.1080392110.5281/zenodo.10803920
_version_ 1797591536581476352

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