Publication: Advancing Air Pollution Exposure Models with Open-Vocabulary Object Detection and Semantic Segmentation of Street-View Images
Advancing Air Pollution Exposure Models with Open-Vocabulary Object Detection and Semantic Segmentation of Street-View Images
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Yuan, Z., Kerckhoffs, J., Lin, P. D., Suel, E., Li, H., Yi, L., Jimenez, M. P., James, P., De Hoogh, K., Hoek, G., & Vermeulen, R. (2025). Advancing Air Pollution Exposure Models with Open-Vocabulary Object Detection and Semantic Segmentation of Street-View Images. Environmental Science & Technology, 59, 21237–21247. https://doi.org/10.1021/acs.est.5c09687
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Mobile monitoring campaigns combined with land use regression (LUR) models effectively capture fine-scale spatial variations in urban air pollution. However, traditional predictor variables often fail to capture the nuances of the built environment and undocumented emission sources. To address this, we developed a framework integrating customizable object-level and segmentation-level visual features from street-view images into stepwise regression and random-forest-based LUR models. Using 5.7 million mobile air pollution measurements
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1520-5851
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Yuan, Z., Kerckhoffs, J., Lin, P. D., Suel, E., Li, H., Yi, L., Jimenez, M. P., James, P., De Hoogh, K., Hoek, G., & Vermeulen, R. (2025). Advancing Air Pollution Exposure Models with Open-Vocabulary Object Detection and Semantic Segmentation of Street-View Images. Environmental Science & Technology, 59, 21237–21247. https://doi.org/10.1021/acs.est.5c09687
