Modeling spatial and temporal urban environmental noise using street view imagery and machine learning

Abstract

This study proposes a framework for modeling environmental noise pollution by integrating land use regression (LUR) with machine learning models and street built environments. Using noise data collected from 128 locations over nine consecutive days in Mississauga, Ontario, Canada, the research demonstrates that incorporating finer-scale street built environment features derived from street view images significantly improves noise prediction accuracy. The model using XGBoost and street view-derived variables significantly outperforms traditional LUR-based models. The results indicate that street-level characteristics, particularly terrain, play a critical role in modeling noise levels, complementing traditional land use and NDVI-based greenness. Furthermore, the research highlights the importance of using non-linear models like XGBoost to capture complex relationships between noise and urban features. This approach offers valuable insights for advancing environmental noise modeling, which further supports future public health studies investigating the impact of noise exposure on population health.