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Integrating machine...
Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity
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- Antonelli, Alexandre, 1978 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för biologi och miljövetenskap,Department of Biological and Environmental Sciences
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Dhanjal-Adams, K. L. (författare)
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- Silvestro, Daniele (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för biologi och miljövetenskap,Department of Biological and Environmental Sciences
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(creator_code:org_t)
- 2022-11-02
- 2022
- Engelska.
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Ingår i: Plants People Planet. - : Wiley. - 2572-2611. ; 5:3, s. 307-16
- Relaterad länk:
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Application of machine learning approaches is aiding biodiversity conservation and research at a time of rapid global change. Two emerging topics and their data requirements are presented. First, to identify areas of priority protection for preventing biodiversity loss, reinforcement learning is used by training models that take into account human disturbance and climate change under recurrent monitoring schemes. Second, neural networks are used to approximate classification of species into Red List categories of the International Union for Conservation of Nature, offering the possibility of real-time re-classification after events such as widespread fires and deforestation. We discuss how the identification of areas and species most at risk could be integrated into an ‘early warning system’ based on climatic monitoring, remotely sensed land-use changes and near-real time biological and threat data from citizen science initiatives. Such system would help guide actions to prevent biodiversity loss at the speed required for effective conservation.
Ämnesord
- NATURVETENSKAP -- Geovetenskap och miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences (hsv//eng)
Nyckelord
- artificial intelligence
- citizen science
- climate change
- conservation
- biology
- machine learning
- modelling
- neural network
- warning system
- climate-change impacts
- patterns
- plants
- Biodiversity & Conservation
- Plant Sciences
- Ecology
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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