Predicting where endangered animals and plants are via human-AI collaboration

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A lack of precise information on where endangered plants and animals are prevents efforts to reverse the biodiversity crisis. Advances in AI applied to data on the distribution of species and their surrounding environments now provide powerful means to generate consistent and high-resolution estimates of the habitat of endangered animal and plant species. Species Habitat Models (SHMs) are AI tools that combine incomplete data on the distribution of species with high-resolution remotely sensed data on environmental variables like climate, land cover, and soils to quantify the key environmental requirements for a species throughout its range. These tools enable mapping predictions of probable habitat for a species across large geographical areas, regardless of whether those have been surveyed by biologists, based on local combinations of key environmental variables.

This AI for Good session will provide an overview of the field on Species Habitat Modeling and NatureServe’s transparent and repeatable modeling process, which leverages human-AI collaboration to produce refined estimates of habitat for endangered species in support of species conservation and improved recovery efforts throughout North America.

This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.

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* Register (or log in) to the AI4G Neural Network to add this session to your agenda or watch the replay
Start date
8 May 2023 at 17:00 CEST Geneva | 08:00-09:30 PDT, San Diego | 23:00-00:30 CST, Beijing
End date
8 May 2023 at 18:30 CEST Geneva | 08:00-09:30 PDT, San Diego | 23:00-00:30 CST, Beijing
Duration
90 minutes (including 30 minutes networking)
Programme stream
- Discovery - AI for Biodiversity
Topics
- Environment & Climate change
UN SDGs
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Predicting where endangered animals and plants are via human-AI collaboration