## 3 Current Developments
### Data Quality Analysis
#### 3.1.1 Road Network Evaluation  
Over the past five years, most OSM quality analyses evaluated the OSM road network in comparison to administrative or commercial datasets. In the first analysis, [PERSON] [19] compared the 2008 OSM data for Great Britain with the ordnance survey (OS) dataset, Meridian 2. To evaluate the positional accuracy, he used a buffer comparison method previously introduced by [PERSON] and [PERSON] [79] and [PERSON] [80]. The completeness of the dataset was evaluated by conducting a grid-based length comparison of the road networks. The result revealed that the OSM dataset can provide an adequate coverage for 29.3% of the area of England. One year later, in 2009, the analysis was repeated, and OSM improved its coverage to 65% [81]. The quality and coverage for OSM in England showed a heterogeneous pattern, with stronger road network concentrations in urban areas, but a lack of details, such as street names, whereas rural areas at times showed a complete lack of coverage. [PERSON] and [PERSON] [82] utilized a similar methodology to compare the commercial TomTom Multinet dataset with OSM for Germany. They concluded that the OSM data in Germany have a similar heterogeneity as previously found between OSM and OS data in the UK in terms of its completeness, highlighting that this particular VGI source can be an alternative to commercial providers in densely populated urban areas. Rural areas, however, tend to show less coverage in OSM and are not sufficient for the creation of more advanced applications, such as route planners. A different study for Germany compared OSM data with the commercial road network dataset distributed by Navteq [83]. The study implemented a feature matching method, which was previously introduced by [PERSON] _et al_. [84] and [PERSON] and [PERSON] [85]. Similar conclusions as the ones previously found by [PERSON] and [PERSON] [82] for the OSM dataset in Germany could be made. [PERSON] and [PERSON] [68] conducted a study for France by extending the previously introduced analysis by [PERSON] [19], which focused on the road network, to other features, such as points of interest (POIs), waterways and coastlines. The study showed a similar heterogeneity of the OSM dataset for France as previously revealed by other researchers for other countries. However, in this particular case, some of the discrepancies can be explained by imports of different datasets, a variety in data collection methods and the participation of contributors in designated projects focusing on selected features or a predefined area. [PERSON] _et al_. [86] introduced additional methods, similar to [PERSON] _et al_. [83], to match a VGI source to a reference dataset, improving the data quality evaluation. The results showed that their matching procedure for the two tested areas, utilizing OSM and OS transportation network information, is efficient and that the mismatch error was below 4%. All aforementioned studies had a strong focus on geometrical accuracy and completeness. In the following years after these initial studies, different research projects shifted this focus to other geodata quality measures. The evaluation of attribute information revealed that the removal of topological errors in OSM for Great Britain was not keeping up with newly introduced data errors in the database [87]. [PERSON] _et al_. [88] introduced a photogrammetric approach to assess and enhance the positional accuracy of the OSM street network data using stereo imagery and a vector adjustment model. In their method, they compared the road centerlines with referenced satellite imagery in the U.S. Based on several test areas, their proposed approach was able to improve the positional point accuracy and to recover the positional street displacement of OSM data. In a different study [89], a variety of methods were applied to evaluate positional and linear geometric accuracy and area shape similarity among datasets for integration purposes in different study areas for the UK and Iraq.