What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery

What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery

High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to the high input data needs of existing estimation approaches. We introduced a computer vision method to estimate annual means for air pollution levels from street-level images. We...

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Bibliographic Details
Main Authors: Esra Suel, Meytar Sorek-Hamer, Izabela Moise, Michael von Pohle, Adwait Sahasrabhojanee, Ata Akbari Asanjan, Raphael E. Arku, Abosede S. Alli, Benjamin Barratt, Sierra N. Clark, Ariane Middel, Emily Deardorff, Violet Lingenfelter, Nikunj C. Oza, Nishant Yadav, Majid Ezzati, Michael Brauer
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Remote Sensing
Subjects:
computer vision
deep learning
street images
air pollution
data science
transferability
Online Access:https://www.mdpi.com/2072-4292/14/14/3429

Internet

https://www.mdpi.com/2072-4292/14/14/3429

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