Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 169-174, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-169-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
10 Dec 2015
ASSESSMENT OF COMPLETENESS AND POSITIONAL ACCURACY OF LINEAR FEATURES IN VOLUNTEERED GEOGRAPHIC INFORMATION (VGI)
M. Eshghi and A. A. Alesheikh Department of GIS, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, Tehran, Iran
Keywords: Volunteered Geographical Information (VGI), Data Quality, Quality Assessment, Positional Accuracy, completeness Abstract. Recent advances in spatial data collection technologies and online services dramatically increase the contribution of ordinary people to produce, share, and use geographic information. Collecting spatial data as well as disseminating them on the internet by citizens has led to a huge source of spatial data termed as Volunteered Geographic Information (VGI) by Mike Goodchild. Although, VGI has produced previously unavailable data assets, and enriched existing ones. But its quality can be highly variable and challengeable. This presents several challenges to potential end users who are concerned about the validation and the quality assurance of the data which are collected. Almost, all the existing researches are based on how to find accurate VGI data from existing VGI data which consist of a) comparing the VGI data with the accurate official data, or b) in cases that there is no access to correct data; therefore, looking for an alternative way to determine the quality of VGI data is essential, and so forth. In this paper it has been attempt to develop a useful method to reach this goal. In this process, the positional accuracy of linear feature of Iran, Tehran OSM data have been analyzed.
Conference paper (PDF, 1008 KB)


Citation: Eshghi, M. and Alesheikh, A. A.: ASSESSMENT OF COMPLETENESS AND POSITIONAL ACCURACY OF LINEAR FEATURES IN VOLUNTEERED GEOGRAPHIC INFORMATION (VGI), Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 169-174, https://doi.org/10.5194/isprsarchives-XL-1-W5-169-2015, 2015.

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