Exploring Spatial Influence of Remotely Sensed PM(2.5) Concentration Using a Developed Deep Convolutional Neural Network Model

Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for exploiting the spatial influence feature in remotel...

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Detalles Bibliográficos
Autores principales: Li, Junming, Jin, Meijun, Li, Honglin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388139/
https://www.ncbi.nlm.nih.gov/pubmed/30720752
http://dx.doi.org/10.3390/ijerph16030454

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388139/
https://www.ncbi.nlm.nih.gov/pubmed/30720752
http://dx.doi.org/10.3390/ijerph16030454

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