Abstract. In the context of rapid urbanization, traffic noise pollution has emerged as a critical environmental issue. This study proposes an innovative three-dimensional dynamic noise mapping method addressing existing technical challenges in noise modeling and visualization. The key innovations include: Utilizing the Grounding DINO large-scale vision-language model to automatically extract traffic flow information from video surveillance data, significantly improving data acquisition efficiency and accuracy. Developing a Web-based three-dimensional visualization system using the Cesium platform, supporting interactive dynamic noise distribution display and innovatively introducing an audio feedback mechanism. The research method combines deep learning with spatiotemporal correlation analysis to effectively capture noise source parameters. The noise model adopts the CNOSSOS-EU standard, considering multiple factors including geometric divergence attenuation, atmospheric absorption, ground effects, and building reflection and diffraction. Using Jingxiu and Lianchi Districts in Baoding City, Hebei Province as a case study, the research validates the method’s effectiveness. The three-dimensional visualization results demonstrate the approach’s superior ability to reflect the physical characteristics of real-world acoustic environments, providing crucial technical support for urban planning and noise control decision-making. Key innovations include improved noise distribution accuracy, dynamic visualization capabilities, and the introduction of interactive audio feedback, offering a novel technical approach to urban noise assessment.
Zhang et al. (Sat,) studied this question.