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Deep Learning meets Behavioural Ecology in the wild: methodological applications using the sociable weaver

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Deep Learning meets Behavioural Ecology in the wild: methodological applications using the sociable weaver Studies of wild animals, from conservation to behaviour, are usually based on individually marked animals. This requires capturing, marking and sampling animals, which imposes limitations as these methods can be challenging, time consuming and impact individual welfare. Additionally, following and observing or video recording animals to obtain data is further constraining. Recent developments in artificial intelligence, in particular deep learning, have the potential do radically and rapidly change the way in which animals are studied in the wild. These new methods can push current boundaries by allowing not only less invasive methods of identification, but also obtaining large volumes of data and, importantly, collection of new types of data, allowing new questions to be addressed.

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recordType

award

status

SIGNED

region

EU

value

202400

unit

EUR