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

Autor: 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
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Remote Sensing, Vol 14, Iss 14, p 3429 (2022)
Druh dokumentu: article
ISSN: 14143429
2072-4292
DOI: 10.3390/rs14143429
Popis: 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 used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250 k images for each city). Our experimental setup is designed to quantify intra- and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Similar to LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities (London, New York, and Vancouver), which have similar pollution source profiles, was moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on cities with very different source profiles, such as Accra in Ghana and Hong Kong, were lower (R2 between zero and 0.21). This suggests a need for local calibration, using additional measurement data from cities that share similar source profiles.
Databáze: Directory of Open Access Journals
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