Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity.

Resource Type: Journal article
Creator: Antonelli, Alexandre ( )

Dhanjal‐Adams, Kiran L. ( )

Silvestro, Daniele ( )
Date published: 2022-11-02
Institution: Royal Botanic Gardens, Kew
Organisational unit: Science
Funder: Name: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Awards: PCEFP3_187012

Name: Vetenskapsrådet

Awards: 2019‐05191
Journal title: Plants, People, Planet
Article number: ppp3.10337
Publisher: Published by John Wiley & Sons Ltd on behalf of New Phytologist Foundation.
Place of publication: Hoboken, NJ, US
ISSN: 2572-2611
eISSN: 2572-2611
Date accepted: 2022-09-15
Official URL: https://doi.org/10.1002/ppp3.10337
Licence: CC BY-NC 4.0 Attribution-NonCommercial
Rights statement: In Copyright
DOI: 10.1002/ppp3.10337
Alternate identifier: identifier: ppp3.10337

type: article_number
Keyword: Conservation biology
Citizen science
Climate change
Neural network
Warning system
Modelling
Artificial intelligence
Machine learning
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