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• W4292260462 abstract "Anthropogenic habitat destruction and climate change are altering the composition of plant communities worldwide 1,2 . However, traditional species distribution models cannot detect rapid, local plant species changes due to their low spatial and temporal resolution 3,4 , and remote sensing models can only identify changes in coarse vegetation categories 5,6 . Here we combine open-access remote sensing imagery, citizen science observations, and deep learning to create a multi-species prediction model at high spatial and temporal resolution. We train a novel deep convolutional neural network using ∼half a million observations within California to simultaneously predict the presence of over 2,000 plant species at meter-level resolution. This model— deepbiosphere —accurately performs many key biodiversity monitoring tasks, from fine-mapping geographic distributions of individual species and communities, to detecting rapid plant community changes in space and time. Deepbiosphere shifts the paradigm for species distribution modeling, providing a roadmap for inexpensive, automatic, and scalable detection of anthropogenic impacts on species worldwide." @default.
• W4292260462 created "2022-08-19" @default.
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• W4292260462 date "2022-08-16" @default.
• W4292260462 modified "2023-10-01" @default.
• W4292260462 title "An image is worth a thousand species: combining neural networks, citizen science, and remote sensing to map biodiversity" @default.
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• W4292260462 doi "https://doi.org/10.1101/2022.08.16.504150" @default.
• W4292260462 hasPublicationYear "2022" @default.
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