Integrating street-view images to quantify the urban soundscape: Case study of Fuzhou City's main urban areaa).

Abstract

Soundscapes are an important part of urban landscapes and play a key role in the health and well-being of citizens. However, predicting soundscapes over a large area with fine resolution remains a great challenge and traditional methods are time-consuming and require laborious large-scale noise detection work. Therefore, this study utilized machine learning algorithms and street-view images to estimate a large-area urban soundscape. First, a computer vision method was applied to extract landscape visual feature indicators from large-area streetscape images. Second, the 15 collected soundscape indicators were correlated with landscape visual indicators to construct a prediction model, which was applied to estimate large-area urban soundscapes. Empirical evidence from 98 000 street-view images in Fuzhou City indicated that street-view images can be used to predict street soundscapes, validating the effectiveness of machine learning algorithms in soundscape prediction.