AI Naturalists Might Hold the Key to Unlocking Biodiversity Data in Social Media Imagery

Tom A August; Oliver L Pescott; Alexis Joly; Pierre Bonnet

doi:10.1016/j.patter.2020.100116

AI Naturalists Might Hold the Key to Unlocking Biodiversity Data in Social Media Imagery

Patterns (N Y). 2020 Oct 9;1(7):100116. doi: 10.1016/j.patter.2020.100116.

Authors

Tom A August¹, Oliver L Pescott¹, Alexis Joly², Pierre Bonnet^{3

4}

Affiliations

¹ UK Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK.
² INRIA Sophia-Antipolis - ZENITH Team, LIRMM - UMR 5506 - CC 477, 161 Rue Ada, 34095 Montpellier Cedex 5, France.
³ AMAP, Univ Montpellier, CIRAD, CNRS, INRA, IRD, Montpellier, France.
⁴ CIRAD, UMR AMAP, Montpellier, France.

Abstract

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.

Keywords: artificial intelligence; big data; biodiversity; botany; computer vision; deep learning; informatics; machine learning; plants; social media.