Exploring the relationship between density and completeness of urban building data in OpenStreetMap for quality estimation

ABSTRACT

OpenStreetMap (OSM) is a free spatial data source based on crowd sourced data. Although the OSM data have a range of applications, such as generating 3D models, and routing and navigation, quality issues are still significant concerns when using the data. Several studies have undertaken quality assessments by comparing OSM data with reference data. However, reference data are not always available due to high costs or licensing restrictions, and very few studies have quantitatively estimated the quality of OSM data under conditions where the corresponding reference data are not available. This study proposed the use of a building density (or building coverage ratio) indicator as a proxy, and designed a series of experiments involving different study areas to quantitatively explore the relationship between building density and building completeness for OSM data in urban areas. The residuals (estimated building completeness and reference building completeness) were also analyzed. Two main results were found from the experiments. (1) There was an approximate linear relationship between building density and building completeness in the OSM data. More precisely, the building completeness of OSM data was approximately 3.4–4 times the building density of OSM data. (2) Approximately 70–80% of the absolute residuals were smaller than 10%, and 80–90% of them were smaller than 20%. This shows that, in most cases, estimated building completeness was close to the corresponding reference building completeness. Therefore, we concluded that the building density indicator is a potential proxy for the quantitative completeness estimation of OSM building data in urban areas. The limitations of using this indicator were also addressed.

KEYWORDS:

• OpenStreetMap
• completeness
• building density
• quality estimation

Acknowledgments

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. G1323541711), and it was also funded by Beijing Key Laboratory of Urban Spatial Information Engineering (No. 2017213). The author would like to express special thanks to all the anonymous reviewers and the editor for their valuable comments.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41771428];Beijing Key Laboratory of Urban Spatial Information Engineering [2017213];Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [G1323541711];