Analysing Building Shapes Quality of Collaborative Mapping

Abstract

The very fast developments of web and data collection technologies have enabled non-experts to collect and disseminate geospatial datasets through web applications. This new type of spatial data is usually known as collaborative mapping or volunteered geographic information VGI. There are various countries around the world could benefit from collaborative mapping data because it is cost free data, easy to access and it provides more customised data. However, there is a concern about its quality because the data collectors may lack the sufficient experience and training about geospatial data production. Most previous studies which have outlined and analysed VGI quality focused on positional and linear features. The current research has been conducted to investigate the quality of another feature type such as polygons (buildings) of collaborative mapping data. Two different VGI data sources have been tested: Google Maps and WikiMapia services. The VGI data was compared with reference data extracted from high resolution aerial image which was provided from General Directorate of Surveying. The suggested methodology based on applying several metrics and methods such as surface distance method, compactness, elongation, and ratio of areas computation. The polygon shape accuracy was analysed by comparing conventional statistical values such as mean, median, standard deviation, minimum, and maximum. The results indicated that there is no big difference between the shape similarities of collaborative mapping polygons. Hence, it can be used for several applications such as spatial data infrastructures (SDI) and urban planning.