Bird Distribution Modelling using Remote Sensing and Citizen Science data (Papers Track) Overall Best Paper

Mélisande Teng (Mila, Université de Montréal); Amna Elmustafa (African Institute for Mathematical Science); Benjamin Akera (McGill University); Hugo Larochelle (UdeS); David Rolnick (McGill University, Mila)

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Ecosystems & Biodiversity Computer Vision & Remote Sensing


Climate change is a major driver of biodiversity loss, changing the geographic range and abundance of many species. However, there remain significant knowl- edge gaps about the distribution of species, due principally to the amount of effort and expertise required for traditional field monitoring. We propose an approach leveraging computer vision to improve species distribution modelling, combining the wide availability of remote sensing data with sparse on-ground citizen science data from .We introduce a novel task and dataset for mapping US bird species to their habitats by predicting species encounter rates from satellite images, along with baseline models which demonstrate the power of our approach. Our methods open up possibilities for scalably modelling ecosystems properties worldwide.