What question did this study set out to answer?

To analyze urban perception in Zhengzhou City using machine learning techniques integrated with visual elements.

January 14, 2026Buildings0 citationsOpen Access

Research on Urban Perception of Zhengzhou City Based on Interpretable Machine Learning

MZMengjing ZhangShandong Jiaotong University CPChen PanJiangsu Provincial Meteorological Bureau XHXiaohua HuangNingbo University of Technology

Key Points

To analyze urban perception in Zhengzhou City using machine learning techniques integrated with visual elements.
Constructed an analytical framework integrating street scene images and interpretable machine learning.
Extracted street visual elements using deep learning technology.
Applied the LightGBM model and SHAP method for analysis.
Identified significant east–west differences in urban perception with central business district as a focal point.
Positive perceptions like safety and vitality are concentrated in central areas, while negative perceptions are prevalent in urban fringe.
Key visual elements affecting perception include greenery visibility and the openness of the sky.

Abstract

Urban perception research has long focused on global metropolises, but has overlooked many cities with complex functions and spatial structures, resulting in insufficient universality of existing theories when facing diverse urban contexts. This study constructed an analytical framework that integrates street scene images and interpretable machine learning. Taking Zhengzhou City as the research object, it extracted street visual elements based on deep learning technology and systematically analyzed the formation mechanism of multi-dimensional urban perception by combining the LightGBM model and SHAP method. The main findings of the research are as follows: (1) The urban perception of Zhengzhou City shows a significant east–west difference with Zhongzhou Avenue as the boundary. Positive perceptions such as safety and vitality are concentrated in the central business district and historical districts, while negative perceptions are more common in the urban fringe areas with chaotic built environments and single functions. (2) The visibility of greenery, the openness of the sky and the continuity of the building interface are identified as key visual elements affecting perception, and their directions and intensifies of action show significant differences due to different perception dimensions. (3) The influence of visual elements on perception has a complex mechanism of action. For instance, the promoting effect of greenery visibility on beauty perception tends to level off after reaching a certain threshold. The research results of this study can provide quantitative basis and strategic reference for the improvement in urban space quality and humanized street design.

Ask AI

Helpful

Bookmark

View Full Paper