Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition

Chenyang Zhang,Fang Wang,Haiyan Tao,Qingli Shi,Li Zhuo

doi:10.3390/ijgi10030185

Abstract

Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function patterns of buildings, while building recognition rates remain unsatisfying. In this paper, we propose a new method to infer the mixed use of buildings based on a tensor decomposition algorithm, which integrates information from both high-resolution remote sensing images and social sensing data. We selected the Tianhe District of Guangzhou, China to validate our method. The results show that the recognition rate of buildings can reach 98.67%, with an average recognition accuracy of 84%. Our study proves that the tensor decomposition algorithm can extract different function patterns of buildings unsupervised, while remote sensing data can provide key information for inferring building functions. The tensor decomposition-based method can serve as an effective and efficient way to infer the mixed use of buildings, which can achieve better results with simpler steps.

Highlights

Mixed-use buildings refer to buildings that combine multiple functions vertically [1,2]
The distribution of mixed-use buildings has an important influence on many urban aspects such as traffic, population and the economy
This study proposed a new method to infer buildings’ mixed-use function, which was based on the integrative use of high-resolution remote sensing images and social sensing data, as well as the tensor decomposition algorithm

Summary

Introduction

Mixed-use buildings refer to buildings that combine multiple functions vertically [1,2] They represent the vertical dimension of urban land mixing and intensive use [3]. Knowing the distribution of mixed-use buildings can help planners better understand and optimize the status quo of the intensive use of lands, so as to further save travel time and consumption of space and urban energy. It can enhance the vitality of buildings and establish a good connection between mixed-use buildings and their surrounding communities and environment, thereby gaining higher economic benefits [6,7,8]. In most areas, the collection of the building function data still relies on time-consuming and laborious manual surveys [9]

Methods

Results

Discussion

Conclusion