Perceptible landscape patterns reveal invisible socioeconomic profiles of cities  

基于景观格局感知揭示城市社会经济发展水平

作　　者：Wenning Li Ranhao Sun Hongbin He Ming Yan Liding Chen 李文宁;孙然好;贺宏斌;陈利顶

机构地区：[1]State Key Laboratory of Urban and Regional Ecology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Beijing 100085,China [2]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Science Bulletin》2024年第20期3291-3302,共12页科学通报（英文版）

基　　金：supported by the National Key R&D Program of China(2022YFF1303101)。

摘　　要：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 in2018–2019 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.

关 键 词：Street view images Landscape pattern Three-dimensional landscape indicators Deep learning 

分 类 号：F299.2[经济管理—国民经济]