Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Ron Mahabir,Andrew Crooks,Anthony Stefanidis,Arie Croitoru,Ross Schuchard

doi:10.3390/ijgi9060341

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). The paper concludes with a discussion that while VSVI data is still relatively new, it has the potential to be a rich source of spatial and temporal information for monitoring cities.

Highlights

Cities are complex, dynamic systems that require various sources of geographical data for monitoring and assessing their health and wellbeing
This paper examines Volunteered Street View Imagery (VSVI) data collected from two different platforms: Mapillary [16] and OpenStreetCam (OSC) [17]
A comparison of Mapillary and OSC with Topologically Integrated Geographic Encoding and Referencing (TIGER) coverage ranges between 14% to 31% and 14% to 53%, respectively

Summary

Introduction

Dynamic systems that require various sources of geographical data for monitoring and assessing their health and wellbeing. Examples of studies that use such data include those using Instagram to explore the relationship between tourist hotspots and safe areas within cities (e.g., [19]), studying people’s perception of their environments (e.g., [20]) and the development of a route-based travel recommendation system using Flickr (e.g., [21]), and exploring the use of Twitter images to map the spatial extent of cities (e.g., [22]) While such platforms provide a rich and useful source of information on cities, they often contain a lot of noise as users are not restricted to any specific location or to capturing any specific feature within the city

Methods

Results

Conclusion