Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM(2.5) Mapping

Fine spatiotemporal mapping of PM(2.5) concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM(2.5) influencing factors pose challenges for accurate mapping. To address these issues, we innovatively co...

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Detalles Bibliográficos
Autores principales: Shen, Huanfeng, Zhou, Man, Li, Tongwen, Zeng, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6861963/
https://www.ncbi.nlm.nih.gov/pubmed/31653059
http://dx.doi.org/10.3390/ijerph16214102

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6861963/
https://www.ncbi.nlm.nih.gov/pubmed/31653059
http://dx.doi.org/10.3390/ijerph16214102

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