Using deep learning to address ecological challenges: the case of cultural ecosystem services, wildlife trafficking and invasive plants

The global spread of the internet has spurred the emergence of new conservation approaches focused on the comprehension of biodiversity patterns as well as on the interlinkages between people and their environment. People increasingly create imagery and textual content on the web, either through the search for topics of interest, the expression of opinions about newsworthy events, or the uploading of photographs and videos in social media platforms. As such, internet users passively offer huge amounts of data which can be used to get real-time insights into a multitude of issues pertaining to ecology and conservation. Inevitably, dealing with the massive amount of social media data produced by an increasingly number of internet users, becomes a major technical (and ethical) challenge to extract meaningful information for conservation. The recent technological advances from artificial intelligence algorithms, and specifically from deep learning, have empowered social media analytics to become prominent in conversation research. This talk will present recent advancements associated to three case studies using deep learning algorithms over social media imagery data to: (1) identify opportunities for cultural ecosystem services, (2) detect invasive alien species and its distribution, and (3) identify protected species prone to wildlife trafficking.

[Host: Ana Sofia Vaz, Predicting and Managing Ecological Change - ECOCHANGE, Invasion Science - InvasionS ]