Using convolutional neural networks to efficiently extract immense phenological data from community science images

Rachel Reeb, Naeem Aziz, Samuel Lapp, Justin Kitzes, J. Mason Heberling & Sara Kuebbing
Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently,...
1 citation reported since publication in 2021.
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