Perceptible landscape patterns reveal invisible socioeconomic profiles of cities

Abstract

Urban landscape is directly perceived by residents and is a significant symbol of urbanization development. A comprehensive assessment of urban landscapes is crucial for guiding the development of inclusive, resilient, and sustainable cities and human settlements. Previous studies have primarily analyzed two-dimensional landscape indicators derived from satellite remote sensing, potentially overlooking the valuable insights provided by the three-dimensional configuration of landscapes. This limitation arises from the high cost of acquiring large-area three-dimensional data and the lack of effective assessment indicators. Here, we propose four urban landscapes indicators in three dimensions (UL3D): greenness, grayness, openness, and crowding. We construct the UL3D using 4.03 million street view images from 303 major cities in China, employing a deep learning approach. We combine urban background and two-dimensional urban landscape indicators with UL3D to predict the socioeconomic profiles of cities. The results show that UL3D indicators differs from two-dimensional landscape indicators, with a low average correlation coefficient of 0.31 between them. Urban landscapes had a changing point in 2018–2019 due to new urbanization initiatives, with grayness and crowding rates slowing, while openness increased. The incorporation of UL3D indicators significantly enhances the explanatory power of the regression model for predicting socioeconomic profiles. Specifically, GDP per capita, urban population rate, built-up area per capita, and hospital count correspond to improvements of 25.0%, 19.8%, 35.5%, and 19.2%, respectively. These findings indicate that UL3D indicators have the potential to reflect the socioeconomic profiles of cities.