LUVR: An interpretable Land Use and Visual Regression model embedding Street View images in air pollution modeling with mobile monitoring

Mobile monitoring campaigns using Land Use Regression (LUR) models effectively capture fine-scale spatial variations in urban air pollution. While traditional LUR models rely on land-use and demographic features, integrating micro-environmental information from Google Street View (GSV) images offers the potential to further enhance the model performance.

We developed LUVR, a framework that integrates vision-transformer-based (ViT) object detection and semantic segmentation features derived from GSV images into LUR models. Using 5.7 million mobile air pollution measurements and 0.37 million GSV images collected in Amsterdam, we modeled nitrogen dioxide (NO₂), black carbon (BC), and ultrafine particles (UFP) in 50m road segments. Three temporal image selection strategies—specific year, most nearby year, and season-weighted—were tested with stepwise linear regression and random forest models.

We found that adding GSV-derived features improved model performance, increasing R² by 0.01–0.05 and reducing errors by 0.7%–10.3%. The most-nearby-year strategy performed the best for NO₂, while BC and UFP benefited slightly more from the season-weighted strategy. This result suggests that for air pollution modeling, GSV-derived built environment features remain relatively stable across years. Using an open-vocabulary object detection module, we detected customized objects described in natural language in a zero-shot fashion, revealing previously unrecognized predictors such as chimneys, traffic lights, and shops. Combined with segmentation-derived features like walls, roads, and grass, visual features contributed 8%–18% to the overall model prediction.

This study demonstrates the potential of integrating visual features into LUR models to enhance hyperlocal air pollution monitoring and exposure assessment. Future research should optimize feature selection and expand applications to broader urban and environmental health studies.