Comparison of Approaches for Urban Functional Zones Classification Based on Multi-Source Geospatial Data: A Case Study in Yuzhong District, Chongqing, China

Kai Cao,Hui Guo,Ye Zhang

doi:10.3390/su11030660

Abstract

Accurate and timely classification and monitoring of urban functional zones prove to be significant in rapidly developing cities, to better understand the real and varying urban functions of cities to support urban planning and management. Many efforts have been undertaken to identify urban functional zones using various classification approaches and multi-source geospatial datasets. The complexity of this category of classification poses tremendous challenges to these studies especially in terms of classification accuracy, but on the opposite, the rapid development of machine learning technologies provides us with new opportunities. In this study, a set of commonly used urban functional zones classification approaches, including Multinomial Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine (SVM), and Random Forest, are examined and compared with the newly developed eXtreme Gradient Boosting (XGBoost) model, using the case study of Yuzhong District, Chongqing, China. The investigation is based on multi-variate geospatial data, including night-time imagery, geotagged Weibo data, points of interest (POI) from Gaode, and Baidu Heat Map. This study is the first endeavor of implementing the XGBoost model in the field of urban functional zones classification. The results suggest that the XGBoost classification model performed the best and was able to achieve an accuracy of 88.05%, which is significantly higher than the other commonly used approaches. In addition, the integration of night-time imagery, geotagged Weibo data, POI from Gaode, and Baidu Heat Map has also demonstrated their values for the classification of urban functional zones in this case study.

Highlights

In recent years, most cities focus on classifying land use/land cover (LULC) based on remote sensing (RS) satellite images, which is costly and lacks timely update
In this study, the XGBoost model was employed, tested, and compared with other commonly used classification models to classify a variety of urban functional zones in the case study of Yuzhong District, Chongqing, China
The results could explicitly demonstrate that the XGBoost model could effectively be applied in urban functional zones classification through the combination of physical and socioeconomic features extracted from high-resolution satellite images and multi-source geospatial data, respectively

Summary

Introduction

Most cities focus on classifying land use/land cover (LULC) based on remote sensing (RS) satellite images, which is costly and lacks timely update. As an effective way to understand the urban space and the interaction between human activities and the environment, is seldom conducted by the government due to limited budgets and manpower [1,2]. The demand for up-to-date urban function information is becoming increasingly crucial, because it is the basis to capture human behavior patterns of a city, and to effectively inform urban management with respect to traffic control, energy recycling, and emergency management [5,6,7]. The above-mentioned studies largely take advantage of the spectral features of a city, and satellite images can only describe the natural characteristics of ground elements, and largely ignore and cannot capture the real human activities

Methods

Results

Conclusion