Assessing the accuracy of openstreetmap data in south africa for the purpose of integrating it with authoritative data

The introduction and success of Volunteered Geographic Information (VGI) has gained the interest of National Mapping Agencies (NMAs) worldwide. VGI is geographic information that is freely generated by non-experts and shared using VGI initiatives available on the Internet. The NMA of South Africa i.e. the Chief Directorate: National GeoSpatial Information (CD: NGI) is looking to this volunteer information to maintain their topographical database; however, the main concern is the quality of the data. The purpose of this work is to assess whether it is feasible to use VGI to update the CD: NGI topographical database. The data from OpenStreetMap (OSM), which is one the most successful VGI initiatives, was compared to a reference data set provided by the CD: NGI. Corresponding features between the two data sets were compared in order to assess the various quality aspects. The investigation was split into quantitative and qualitative assessments. The aim of the quantitative assessments was to determine the internal quality of the OSM data. The internal quality elements included the positional accuracy, geometric accuracy, semantic accuracy and the completeness. The first part of the qualitative assessment was concerned with the currency of OSM data between 2006 and 2012. The second part of the assessment was focused on the uniformity of OSM data acquisition across South Africa. The quantitative results showed that both road and building features do not meet the CD: NGI positional accuracy standards. In some areas the positional accuracy of roads are close to the required accuracy. The buildings generally compare well in shape to the CD: NGI buildings. However, there were very few OSM polygon features to assess, thus the results are limited to a small sample. The semantic accuracy of roads was low. Volunteers do not generally classify roads correctly. Instead, many volunteers prefer to class roads generically. The last part of the quantitative results, the completeness, revealed that commercial areas reach high completeness percentages and sometimes exceed the total length of the CD: NGI roads. In residential areas, the percentages are lower and in low urban density areas, the lowest. Nonetheless, the OSM repository has seen significant growth since 2006. The qualitative results showed that because the OSM repository has continued to grow since 2006, the level of currency has increased. In South Africa, the most contributions were made between 2010 and 2012. The OSM data set is thus current after 2012. The amount and type of contributions are however not uniform across the country for various reasons. The number of point contributions was low. Thus, the relationship between the type of contribution and the settlement type could not be made with certainty. Because the OSM data does not meet the CD: NGI spatial accuracy requirements, the two data sets cannot be integrated at the database level. Instead, two options are proposed. The CD: NGI could use the OSM data for detecting changes to the landscape only. The other recommendation is to transform and verify the OSM data. Only those features with a high positional accuracy would then be ingested. The CD: NGI currently has a shortage of staff that is qualified to process ancillary data. Both of the options proposed thus require automated techniques because it is time consuming to perform these tasks manually.