## 1 Introduction  
Volunteered geographic information (VGI) offers an intuitive method for the collection of geographic information (GI) through volunteering at a very low cost [1; 2; 3; 4; 5]. Probably one of the best-known example of VGI is the OSM project, contributed by users of varying mapping experiences [6]. This has become possible due to the availability of geo-location devices and more user-friendly software. The major driving forces behind the establishment and growth of OSM are restrictions on use, non-availability of data by various map providers, a high cost of procurement and the recency of data. Further, the OSM data are available under an Open Data Commons Open Database license (ODbL) for reuse, which allows for sharing, creation and adapting of the data unless reuse is attributed [7]. OSM has encouraged 3,581,079 enthusiastic contributors across the globe to create 3,802,465,092 nodes, 401,428,744 ways and 4,885,160 relations [8]. It uses a topological data structure (XML format) that contains a block of nodes, ways and relations to represent spatial features. Further, it contains tags (key-value pairs) for storing metadata about features. The OSM project produces a huge amount of labeled geographical data contributed by users. However, the data suffer from significant limitations that come naturally with VGI data. The first and foremost reason is uncertainty about the data availability i.e., when, which, how, from where and how much data will be contributed [9; 10; 11; 12]. This is normally defined as the contribution patterns and contributor's motive. Further, no knowledge about the contributors, their skills and motivation augments heterogeneous quality. Furthermore, the lack of unified standards and no top-down quality assurance model provides the risk that the contributed data often have a certain degree of error [13]. In addition, OSM provides an open classification tagging scheme (key-value pairs) that can lead to misclassification and reduction in data quality [14]. The issue of vandalism and data imputation further deepens the quality concern. Moreover, the lack of clarity in license terms and its definitions also deters organizations from incorporating VGI into their datasets. This is due to the lack of longitudinal research on legal aspects of utilizing VGI datasets [15]. These issues have raised a deep concern about the quality, reliability and fitness of data for GI application domains [16; 17; 18; 19; 20]. Many of these issues have been discussed in the vast literature. It is concluded that being affected by many limitations, VGI data are still reasonably credible [1]. Hence, for creating trust in OSM, the data need to be assessed. Various established quality standards ([[http://ncl.sbs.ohio-state.edu/ica/3_spatial.html](http://ncl.sbs.ohio-state.edu/ica/3_spatial.html)]([http://ncl.sbs.ohio-state.edu/ica/3_spatial.html](http://ncl.sbs.ohio-state.edu/ica/3_spatial.html)), accessed on 13 September 2016) exist for assessment of data, but ISO TC211 [21] is primarily responsible for drafting international standards for quality assessment procedures. The data quality indicators identified by TC211 for the assessment of spatial data are completeness, logical or topological consistency, positional accuracy, semantic accuracy, attribute accuracy, temporal accuracy and lineage, which have been used in various studies [18; 21; 22; 23; 24; 25]. For spatial data quality grading of OSM, all of these elements must be carefully assessed [4; 26]. The lack of a suitable quality evaluation framework renders problems for potential users to assess OSM data [6; 13]. The established quality evaluation procedures demand authoritative data to evaluate VGI. The latter case is not always possible because acquiring authoritative data is not an easy task. This may be due to non-availability of data, licensing terms and the high cost of acquiring the data [5]. It is suggested that under such a scenario, the existing quality measures become unsuitable for assessment [4; 14].