Abstract
As open source volunteered geographic information continues to gain popularity, the user community and data contributions are expected to grow, e.g., CloudMade, Apple, and Ushahidi now provide OpenStreetMap© (OSM) as a base layer for some of their mapping applications. This, coupled with the lack of cartographic standards and the expectation to one day be able to use this vector data for more geopositionally sensitive applications, like GPS navigation, leaves potential users and researchers to question the accuracy of the database. This research takes a photogrammetric approach to determining the positional accuracy of OSM road features using stereo imagery and a vector adjustment model. The method applies rigorous analytical measurement principles to compute accurate real world geolocations of OSM road vectors. The proposed approach was tested on several urban gridded city streets from the OSM database with the results showing that the post adjusted shape points improved positionally by 86%. Furthermore, the vector adjustment was able to recover 95% of the actual positional displacement present in the database. To demonstrate a practical application, a head-to-head positional accuracy assessment between OSM, the USGS National Map (TNM), and United States Census Bureau’s Topologically Integrated Geographic Encoding Referencing (TIGER) 2007 roads was conducted.
Highlights
Different metrics describing positional accuracy are presented depending on the geometry of the feature being tested, e.g., the accuracy of points are usually expressed as Root Mean Square Error (RMSE) error estimates, while linear features could be compared using the buffer method
The proposed approach and vector adjustment model was developed to assess the positional accuracy of geographical vector data, such as road centerlines
Most of the current methods of determining positional accuracy are based on comparing test vectors to a reference/truth dataset that is known to be of higher quality
Summary
OpenStreetMap© (OSM) is an open source geographical mapping project that provides the public with a free digital map of the world. OSM contributors can edit (update) the map in several ways including using Global Positioning System (GPS) waypoints and tracks to identify features in the field or measuring satellite imagery to identify roads and other geographical features of interest [1]. This type of open contributing by volunteer contributors is known as crowdsourcing and refers to many volunteers providing information into the database, where each individual volunteer contributes a small portion that pertains to their local knowledgebase. While other studies compared a test vector dataset with GPS locations [24,25] and positions determined from georeferenced orthoimagery [26]
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