Quality Assessment of the French OpenStreetMap Dataset

By Jean-François Girres, Guillaume Touya. Trans. GIS, 2010

742 citations recorded by the source corpus.

The concept of Volunteered Geographic Information (VGI) has recently emerged from the new Web 2.0 technologies. The OpenStreetMap project is currently the most significant example of a system based on VGI. It aims at producing free vector geographic databases using contributions from Internet users. Spatial data quality becomes a key consideration in this context of freely downloadable geographic databases. This article studies the quality of French OpenStreetMap data. It extends the work of Haklay to France, provides a larger set of spatial data quality element assessments (i.e. geometric, attribute, semantic and temporal accuracy, logical consistency, completeness, lineage, and usage), and uses different methods of quality control. The outcome of the study raises questions such as the heterogeneity of processes, scales of production, and the compliance to standardized and accepted specifications. In order to improve data quality, a balance has to be struck between the contributors' freedom and their respect of specifications. The development of appropriate solutions to provide this balance is an important research issue in the domain of user‐generated content.

Subfields: Artificial Intelligence, Geography, Planning and Development, Signal Processing

View Paper: https://www.semanticscholar.org/paper/91efbf49be5938c9271170ba33078c7461622828

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Citations in the corpus

OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks (2016, result): “Its data quality is generally quite high (Girres and Touya 2010; Haklay 2010; Barron et al. 2014) and although data coverage varies worldwide, it is generally good when compared to corresponding estimates from the CIA World Factbook (Maron 2015).”
Quality assessment for building footprints data on OpenStreetMap (2014, methodology): “2012) and France (Girres and Touya 2010), and more detailed investigations about point (Neis et al.”
A review of volunteered geographic information quality assessment methods (2017, methodology): “We reviewed the works of Girres and Touya (2010) where they use statistics to observe the correlations of the number of contributors to the mean capture date, and to the mean version of the capture object in order to assess how many objects are updated.”
The Street Network Evolution of Crowdsourced Maps: OpenStreetMap in Germany 2007-2011 (2011, methodology): “In France, a similar approach was used to analyze OSM data and further studies were conducted at the same time [34].”
The world’s user-generated road map is more than 80% complete (2017, result): “While early assessments found significant gaps [18, 19, 30, 31], more recent studies of European countries have found that the network is virtually complete, and is comparable to or better than official or proprietary data sources [17, 22].”
Assuring the quality of volunteered geographic information (2012, neutral): “Girres and Touya (2010) provide a comparable analysis of French OpenStreetMap data, and Cipeluch et al. (2010) analyze Irish OpenStreetMap against Google Maps and Bing Maps.”
Learning Aerial Image Segmentation From Online Maps (2017, methodology): “For our study, we make use of online map data from OpenStreetMap [1]–[3] (OSM, http://www.”
Crowdsourcing for climate and atmospheric sciences: current status and future potential (2015, positive): “…number of contributors has been noted in the literature (e.g. Raymond, 2001; Flanagin and Metzger, 2008; Snow et al. , 2008; Welinder et al. , 2010; Girres and Touya, 2010; Haklay et al. , 2010; Heipke, 2010; Goodchild and Glennon, 2010; Goodchild and Li, 2012; Basiouka and Potsiou, 2012; Neis et…”
Analyzing the Contributor Activity of a Volunteered Geographic Information Project - The Case of OpenStreetMap (2012, background): “, better coverage in rural areas and less completeness in urban areas, could be determined [33–36].”
There's No Such Thing as the Perfect Map: Quantifying Bias in Spatial Crowd-sourcing Datasets (2015, result): “The former has been found to be very high [1, 10, 13, 14, 15, 16, 28] when compared to proprietary mapping datasets, in a variety of countries across Europe.”
Topological Map Extraction From Overhead Images (2018, methodology): “This new dataset contains building footprints and road networks from OSM [17, 16, 14] and aerial images from Google Maps.”
How Many Volunteers Does it Take to Map an Area Well? The Validity of Linus’ Law to Volunteered Geographic Information (2010, methodology): “We would like to see other studies that use different methods to test our findings, such as the study by Girres and Touya (2010) which found that ‘the number of OSM objects in an area clearly grows in relation to the number of contributors in the area, but in a non-linear way: most areas have less…”