Work

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

Year: 2025

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide 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 (NO 2 ), black carbon (BC), and ultrafine particles (UFP) across 46,664 road segments in Amsterdam, The Netherlands. Incorporating street-view images improved model performance, increasing R 2 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.

Source: Environmental Science & Technology

DOI: 10.1021/acs.est.5c09687