Cargando…
A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Spatial-Resolution Satellite Imagery
High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, like satelli...
Autores principales: | Sorek-Hamer, Meytar, von Pohle, Michael, Sahasrabhojanee, Adwait, Asanjan, Ata Akbari, Deardorff, Emily, Suel, Esra, Lingenfelter, Violet, Das, Kamalika, Oza, Nikunj, Ezzati, Majid, Brauer, Michael |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615102/ https://www.ncbi.nlm.nih.gov/pubmed/37724306 http://dx.doi.org/10.3390/atmos13050696 |
Ejemplares similares
-
What you see is what you breathe? Estimating air pollution spatial variation using street level imagery
por: Suel, Esra, et al.
Publicado: (2022) -
Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas
por: Suel, Esra, et al.
Publicado: (2021) -
Measuring social, environmental and health inequalities using deep learning and street imagery
por: Suel, Esra, et al.
Publicado: (2019) -
Gradient boosting machine learning to improve satellite-derived column water vapor measurement error
por: Just, Allan C., et al.
Publicado: (2020) -
Enabling country-scale land cover mapping with meter-resolution satellite imagery
por: Tong, Xin-Yi, et al.
Publicado: (2023)