International Journal of Geo-Information Article Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam Ron Mahabir 1,2,* , Ross Schuchard 1, Andrew Crooks 1,2,3 , Arie Croitoru 2,3 and Anthony Stefanidis 4 1 Department of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USA;
[email protected] (R.S.);
[email protected] (A.C.) 2 Center for Geoinformatics and Geospatial Intelligence, George Mason University, Fairfax, VA 22030, USA;
[email protected] 3 Department of Geography and Geoinformation Science, George Mason University Fairfax, VA 22030, USA 4 Department of Computer Science, William and Mary, Williamsburg, VA 23187, USA;
[email protected] * Correspondence:
[email protected]; Tel.:+1-703-993-6377 Received: 8 April 2020; Accepted: 24 May 2020; Published: 26 May 2020 Abstract: Over the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI data: Volunteered Street View Imagery (VSVI). Two VSVI sources, Mapillary and OpenStreetCam, were extracted and analyzed to study road coverage and contribution patterns for four US metropolitan areas. Results show that coverage patterns vary across sites, with most contributions occurring along local roads and in populated areas. We also found that a few users contributed most of the data. Moreover, the results suggest that most data are being collected during three distinct times of day (i.e., morning, lunch and late afternoon).