Journal article
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 could be valuable for a number of different monitoring scenarios, including image capture during aerial surveys. We present two alternative pathways for producing counts: (1) via the Zooniverse citizen science project Penguin Watch and (2) via a computer vision algorithm (Pengbot), and share a comparison of citizen science-, machine learning-, and expert- derived counts. We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, 4.5MB, Terms of use)
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- Publisher copy:
- 10.1038/s41597-020-0442-6
Authors
- Publisher:
- Nature Research
- Journal:
- Scientific Data More from this journal
- Volume:
- 7
- Issue:
- 1
- Article number:
- 102
- Publication date:
- 2020-03-26
- Acceptance date:
- 2020-02-03
- DOI:
- EISSN:
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2052-4463
- ISSN:
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2052-4463
- Pmid:
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32218449
- Language:
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English
- Keywords:
- Pubs id:
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1097590
- Local pid:
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pubs:1097590
- Deposit date:
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2020-03-31
Terms of use
- Copyright holder:
- Jones et al.
- Copyright date:
- 2020
- Rights statement:
- Copyright © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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