AI naturalists might hold the key to unlocking biodiversity data in social media imagery

August, Tom A. ORCID: https://orcid.org/0000-0003-1116-3385; Pescott, Oliver L. ORCID: https://orcid.org/0000-0002-0685-8046; Joly, Alexis; Bonnet, Pierre. 2020 AI naturalists might hold the key to unlocking biodiversity data in social media imagery. Patterns, 1 (7), 100116. 11, pp. https://doi.org/10.1016/j.patter.2020.100116

Before downloading, please read NORA policies.

Preview

Text
N528851JA.pdf - Published Version
Available under License Creative Commons Attribution 4.0.
Download (1MB) | Preview

Official URL: http://dx.doi.org/10.1016/j.patter.2020.100116

Abstract/Summary

The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword “flower” across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.patter.2020.100116
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-)
ISSN:	2666-3899
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	artificial intelligence, computer vision, deep learning, machine learning, social media, biodiversity, informatics, botany, plants, big data
NORA Subject Terms:	Ecology and Environment Data and Information
Date made live:	06 Nov 2020 12:42 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/528851

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.patter.2020.100116

UKCEH and CEH Sections/Science Areas:

Biodiversity (Science Area 2017-)

ISSN:

2666-3899

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

artificial intelligence, computer vision, deep learning, machine learning, social media, biodiversity, informatics, botany, plants, big data