Can citizen science and social media images support the detection of new invasion sites? A deep learning test case with Cortaderia selloana

Cardoso, Ana Sofia; Malta-Pinto, Eva; Tabik, Siham; August, Tom ORCID: https://orcid.org/0000-0003-1116-3385; Roy, Helen E. ORCID: https://orcid.org/0000-0001-6050-679X; Correia, Ricardo; Vicente, Joana R.; Vaz, Ana Sofia. 2024 Can citizen science and social media images support the detection of new invasion sites? A deep learning test case with Cortaderia selloana. Ecological Informatics, 81, 102602. 13, pp. 10.1016/j.ecoinf.2024.102602

Before downloading, please read NORA policies.

Preview

Text
N537297JA.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.
Download (6MB) | Preview

Official URL: http://dx.doi.org/10.1016/j.ecoinf.2024.102602

Abstract/Summary

Deep learning has advanced the content analysis of digital data, unlocking opportunities for detecting, mapping, and monitoring invasive species. Here, we tested the ability of open source classification and object detection models (i.e., convolutional neural networks: CNNs) to identify and map the invasive plant Cortaderia selloana (pampas grass) in mainland Portugal. CNNs were trained over citizen science images and then applied to social media content (from Flickr, Twitter, Instagram, and Facebook), allowing to classify or detect the species in over 77% of situations. Images where the species was identified were mapped, using their georeferenced coordinates and time stamp, showing previously unreported occurrences of C. selloana, and a tendency for the species expansion from 2019 to 2021. Our study shows great potential from deep learning, citizen science and social media data for the detection, mapping, and monitoring of invasive plants, and, by extension, for supporting follow-up management options.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.ecoinf.2024.102602
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-)
ISSN:	1574-9541
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	artificial intelligence, convolutional neural networks, computer vision, pampas grass
NORA Subject Terms:	Ecology and Environment Computer Science Data and Information
Related URLs:	Other Other Dataset
Date made live:	17 Apr 2024 12:25 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537297

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.ecoinf.2024.102602

UKCEH and CEH Sections/Science Areas:

Biodiversity (Science Area 2017-)

ISSN:

1574-9541

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

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

Additional Keywords:

artificial intelligence, convolutional neural networks, computer vision, pampas grass