Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China

Wei Chen,Bailang Yu,Qiusheng Wu,Gang Chen,Xin Huang,Yuyu Zhou

doi:10.3390/rs12172805

Abstract

The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.83 and 0.71, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level.

Highlights

Buildings are a vital element in urban studies
Building type is of great use for analyzing urban socioeconomic features dominated by human activities
We employed natural language processing (NLP)-based approaches to extract semantic information hidden in POI names

Summary

Introduction

Buildings are a vital element in urban studies. As the fundamental structural element in the urban physical space [1], buildings are the basic spatial unit to monitor urban structure development in the horizontal and vertical dimensions. The product of urban 3D building structure, i.e., building footprint, height, and volume, is a proxy for analyzing structural specifics within cities and revealing their driving factors [2]. As the main venues of urban activities in the urban socioeconomic space, buildings are the basic measurement unit to study the impact of human activities in the process of quantitative urban modeling. The product of building type can be a proxy for analyzing urban socioeconomic features dominated by human activities (e.g., living, working, and recreation), which are represented by the building types. In a bottom-up urban building energy use model developed by

Objectives

Results

Discussion

Conclusion