## 2 Data Quality Parameters and Related Work in OSM
### Semantic Accuracy  
The semantic accuracy is the quality of spatial features and is described in accordance with the selected model [38]. This information is defined by features, their attributes, values and relationships between features. The quality assessment process of a dataset allows measuring the 'distance' between the data and the perceived reality. Here, distance implies both geometric distance and semantic distance. In OSM, the semantic accuracy of map entities is described through tags [1]. Further, the quality indicators for semantic accuracy can be categorized as data-centric, user-centric and context-centric [4]. The data-centric assessment is based on the extrinsic authoritative comparison and intrinsic assessment methods. Further, types of features and attributes of features are subjected to measure the semantic similarity [57; 71; 77]. [PERSON] and [PERSON] [78] have performed semantic similarity for the possible integration ordnance survey (OS) and OSM project. [PERSON] and [PERSON] [69] \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Researcher** & **Reference Datasets** & **Description** \\ \hline [PERSON] [39], [PERSON] [40] & OSM (Healthroom, U.K.) & The study analyzed road features without names in the attribute tables, and the test length of these roads was calculated and presented as a percentage. \\ \hline [PERSON] and [PERSON] [27] & OSM (Alststadt, Heidelberg, Germany) & The research employed term frequency-inverse distance frequency measure (tf-idid) to evaluate the importance of tags related to the feature type. \\ \hline [PERSON] et al. [67] & CSM (Canada) & The study used concave chunks for defining contributor’s editing sessions for producing an image of contribution. \\ \hline [PERSON] and [PERSON] [66] & OSM (Lubbenau, Germany) & In this study, spatial operations were applied on the metadata of the spatial dataset, e.g., star join, to extract “data completeness” of the area. \\ \hline [PERSON] et al. [68] & OSM (London, U.K.) & The study used methodology to assess regional data completeness by analyzing changes in community activity over time periods. \\ \hline [PERSON] and [PERSON] [70] & OSM (Tehram, Iran) & The authors assessed OSM based on metrics such as minimum bounding geometry area and directional distribution. (standard deviational ellipse) and applied fuzzy logic to identify the completeness of OSM data in gridded cells \\ \hline [PERSON] and [PERSON] [69] & OSM (Selected regions of Germany and U.K.) & The study developed a conceptual framework for analyzing the completeness and other quality attributes of data based on intrinsic indicators. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies on intrinsic completeness assessment of OpenStreetMap (OSM) data. have presented a conceptual model to assess the data quality elements using intrinsic indicators. Further, [PERSON] et al. (2017) have used a machine learning approach to assess the semantic quality of the London OSM dataset. The accuracy has been obtained by finding a relative number of correctly-predicted instances with the total number of actual instances. [PERSON] et al. (2017) have introduced the semantic similarity measure based on the changes in the history of OSM elements. The user contribution-centric approach for assessing the semantic accuracy relies on the trusted contributors. [PERSON] and [PERSON] (2010) and [PERSON] and [PERSON] (2010) have identified and analyzed the motivations of contributors and their feature editing. [PERSON] et al. (2010) have elaborated the methods used by researchers to evaluate VGI data quality based on contributors' motivations, experience, etc. There have been many studies that have examined the motivation of trusted individuals ([PERSON] et al., 2010; [PERSON] et al., 2011; [PERSON] et al., 2011; [PERSON] et al., 2011), but such studies are considered generic for evaluation. Further, [PERSON] et al.