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 model performance, increasing R² by 0.01-0.05 and reducing mean absolute errors by 0.7-10.3%. Sensitivity analyses indicated that key street-view-derived visual features remained stable across years and seasons. Using images from nearby years expanded training instances, thereby enhancing alignment with mobile measurements at fine granularity. Our open-vocabulary object detection module identified influential but previously unrecognized object predictors, such as chimneys, traffic lights, and shops. Combined with segmentation-derived features (e.g., walls, roads, grass), street-view images contributed 8-18% feature importance to model predictions. These findings highlight the potential of visual data in enhancing hyperlocal air pollution mapping and exposure assessment.

Keywords: air pollution; deep learning; exposure assessment; land use regression (LUR); mobile sensing; street-view image; vision-language model (VLM); vision-transformer models (ViT).