Assessing OSM building completeness using population data

Abstract

OpenStreetMap (OSM) is currently an important source for building data, despite the existence of potential quality issues. Previous studies have assessed OSM data quality by comparing it with reference building data, which may not otherwise be readily available. This study assessed OSM building completeness using population data, and investigated the effectiveness of using population data for building reference data. We proposed various approaches, including type-based and regression-based approaches and their subtypes, and designed measures and methods to evaluate these approaches. Our evaluation examined four study areas in two countries, using global population data sets at three spatial resolutions (1-km, 100-m, and 30-m). Results showed that the type-based approach correctly classified approximately 80–99% of the assessed grid cells. The regression-based approach resulted in a high linear correlation (0.7 or greater) between the population counts and the referenced building count/building area size, with the strongest correlation present for the 1-km population dataset. We conclude that the use of population data as referenced building data is an effective method for the assessment of OSM building completeness. The paper concludes with the advantages and limitations of using both the type-based and the regression-based approaches.

Keywords:

• OpenStreetMap
• data quality
• quality assessment
• building data
• WorldPop
• HRSL

Acknowledgements

We would like to express special thanks to the editor (Dr. Jennifer Miller) and all the anonymous reviewers for their valuable comments that have helped improve this paper substantially.

Data and codes availability statement

The codes and data that support the findings of the present study are available on Figshare at https://doi.org/10.6084/m9.figshare.17158622.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The project was supported by National Natural Science Foundation of China [No. 41771428], Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [No. CUGESIW1801].

Notes on contributors

Yuheng Zhang

Yuheng Zhang is a master student in the School of Geography and Information Engineering, China University of Geosciences (Wuhan).

Qi Zhou

Qi Zhou is an Associate Professor in the School of Geography and Information Engineering, China University of Geosciences (Wuhan). His research interest includes GIScience, Volunteered Geographic Information (VGI), Spatial Data Quality and Geospatial Data Analysis.

Maria Antonia Brovelli

Maria Antonia Brovelli is a Professor of GIS and Copernicus Uptake at the Politecnico di Milano (PoliMI) and a member of the School of Doctoral Studies in Data Science at ''Roma La Sapienza'' University. Her research interest includes Open-Source GIS, Citizen Science and Big Geo Data.

Wanjing Li

Wanjing Li is a master student in the School of Geography and Information Engineering, China University of Geosciences (Wuhan).