Cargando…
Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images
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 a...
Autores principales: | Reeb, Rachel A., Aziz, Naeem, Lapp, Samuel M., Kitzes, Justin, Heberling, J. Mason, Kuebbing, Sara E. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8801702/ https://www.ncbi.nlm.nih.gov/pubmed/35111176 http://dx.doi.org/10.3389/fpls.2021.787407 |
Ejemplares similares
-
Wildflower phenological escape differs by continent and spring temperature
por: Lee, Benjamin R., et al.
Publicado: (2022) -
In the Centre of Immensities
por: Lovell, Alfred Charles Bernard
Publicado: (1980) -
Cyanometabolites: molecules with immense antiviral potential
por: Singh, Uma, et al.
Publicado: (2023) -
A Quantitative Evaluation of the Performance of the Low-Cost AudioMoth Acoustic Recording Unit
por: Lapp, Sam, et al.
Publicado: (2023) -
Chilling and Forcing From Cut Twigs—How to Simplify Phenological Experiments for Citizen Science
por: Menzel, Annette, et al.
Publicado: (2020)