Advancing Air Pollution Exposure Models with Open-Vocabulary Object Detection and Semantic Segmentation of Street-View Images.

ABSTRACT: 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 (2019-2020) and 0.37 million street-view images (2008-2024), we mapped nitrogen dioxide (NO₂), black carbon (BC), and ultrafine particles (UFP) across 46,664 road segments in Amsterdam, The Netherlands. Incorporating street-view images improved mode

SUBMITTER: Yuan Z 

PROVIDER: S-EPMC12509304 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature