Quality Assessment of Semantic Tags in OpenStreetMap

A comprehensive understanding of urban areas includes deep knowledge about locations and functions of buildings within. In most developed countries cadastral data is available for this research, but from a global perspective the only free and comprehensive data source is OpenStreetMap (OSM). For this study we selected 42 cities across the globe covering a wide range of climate zones as well as cultures and assess the accuracy of building function labels in OSM indirectly by comparing them to Google Places. We state that points-of-interest (POIs) are reasonably covered in Google Places due to a large number of users and a business perspective driving its development. We study how many semantic building tags are in accordance with the proposed scheme by OSM. In this regard Los Angeles has the best coverage, followed by Amsterdam and Cologne. In Melbourne, Paris, and Sydney we find the most matching building functions of OSM and Google Places. In summary, we conclude that OSM is not ready to provide ground truth labels for each place on the globe, but can serve as a powerful and rich label source in selected study areas. Our study gives a first insight where obtaining training labels from OSM is a valid and reliable approach.