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Climate change is a major driver of biodiversity loss, changing thegeographic range and abundance of many species. However, there remainsignificant knowledge gaps about the distribution of species, due principallyto the amount of effort and expertise required for traditional fieldmonitoring. We propose an approach leveraging computer vision to improvespecies distribution modelling, combining the wide availability of remotesensing data with sparse on-ground citizen science data. We introduce a noveltask and dataset for mapping US bird species to their habitats by predictingspecies encounter rates from satellite images, along with baseline models whichdemonstrate the power of our approach. Our methods open up possibilities forscalably modelling ecosystems properties worldwide.

Bird Distribution Modelling using Remote Sensing and Citizen Science data