Cargando…
Very High Resolution Species Distribution Modeling Based on Remote Sensing Imagery: How to Capture Fine-Grained and Large-Scale Vegetation Ecology With Convolutional Neural Networks?
Species Distribution Models (SDMs) are fundamental tools in ecology for predicting the geographic distribution of species based on environmental data. They are also very useful from an application point of view, whether for the implementation of conservation plans for threatened species or for monit...
Autores principales: | Deneu, Benjamin, Joly, Alexis, Bonnet, Pierre, Servajean, Maximilien, Munoz, François |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122285/ https://www.ncbi.nlm.nih.gov/pubmed/35599901 http://dx.doi.org/10.3389/fpls.2022.839279 |
Ejemplares similares
-
Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment
por: Deneu, Benjamin, et al.
Publicado: (2021) -
Deep Species Distribution Modeling From Sentinel-2 Image Time-Series: A Global Scale Analysis on the Orchid Family
por: Estopinan, Joaquim, et al.
Publicado: (2022) -
Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery
por: Kattenborn, Teja, et al.
Publicado: (2019) -
Assessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery
por: Lassiter, Allison, et al.
Publicado: (2020) -
Fine-grained classification based on multi-scale pyramid convolution networks
por: Wang, Gaihua, et al.
Publicado: (2021)