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Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics

Abstract:

Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in count data (number of penguins per image) and ‘nearest neighbour distance’ measurements. The latter provide useful summaries of colony spatial structure (which can indicate phenological stage) and can be used to detect movement – metrics which coul...

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Publication status:
Published
Peer review status:
Peer reviewed

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Role:
Author
ORCID:
0000-0003-1970-0034
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-8945-8573
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Publisher:
Nature Research Publisher's website
Journal:
Scientific Data Journal website
Volume:
7
Issue:
1
Article number:
102
Publication date:
2020-03-26
Acceptance date:
2020-02-03
DOI:
EISSN:
2052-4463
ISSN:
2052-4463
Pmid:
32218449
Pubs id:
1097590
Local pid:
pubs:1097590
Language:
English
Keywords:
Format:
Electronic

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