Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images.

ABSTRACT: 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, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools

SUBMITTER: Reeb RA 

PROVIDER: S-EPMC8801702 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature