Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

In the past we have written extensively on Volunteered Geographic Information (VGI) such as OpenStreetMap or Twitter. However, we have not really explored Street View Imagery  (SVI), well not until now. Within the realm of VGI, SVI has emerged in recent years as a novel and rich source of data on cities from which geographic information can be derived.

Perhaps the most well-known example of SVI utilization is that of Google Street View (GSV). While SVI has been traditionally collected by governmental agencies and companies alike, we are now also witnessing the emergence of Volunteered Street View Imagery (VSVI), which relies on a crowdsourced effort to provide geotagged street-level imagery coverage of traversable pathways (e.g., a street or trail). Such imagery, similar to GSV, provides detailed information about the location of objects such as cars, road markings, traffic lights and signs, and allows for the automatic extraction of features at scale. Such imagery can also be mined using machine learning algorithms to automatically derive points of interest (POI) databases (e.g., locations of coffee shops and fire hydrants) without the intervention of the citizen.

To explore VSVI we have just published a new paper entitled: "Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam" in the ISPRS International Journal of Geo-Information. In this paper we examine VSVI data collected from two different platforms: Mapillary and OpenStreetCam (OSC) for four metropoiltan areas in the United States (i.e., Washington (District of Columbia), San Francisco (California), Phoenix (Arizona), and Detroit (Michigan)). Both of these online platforms accept sequences of images captured from mobile devices and uploaded via an app on the device (like those shown in the image to the right). Images are geolocated using the device’s global positioning system (GPS). More specifically the paper examines:

• the level of spatial coverage of each platform in order to assess the overall potential of such platforms to provide adequate coverage of geographic information.
• user contribution patterns in Mapillary and OSC in order to understand how users are contributing to these platforms.

Results from our systematic and quantitative analysis of these two emerging VGI sources indicate that most Mapillary and OSC contributions occurred along control-access highways and local roads, and that the overall coverage in these sources is variable in comparison to an authoritative source (i.e., TIGER). Furthermore, our results showed that while the number of contributors varied across sites, only a few contributors were responsible for producing most of the raw data. User contribution patterns were also different in Mapillary and OSC. Specifically, we found that while patterns in coverage were variable for the different OSC sites, coverage patterns in Mapillary tended to be similar among sites. This finding may be linked to several factors, including differences in mapping practice, or issues with participation inequality, a topic that has been highly researched for other VGI platforms such as OSM, but which is still lacking within VSVI. Lastly, user contributions in Mapillary tended to be higher around 8:00 am, 1:00 pm and 5:00 pm (local time). This finding suggests that VSVI contributions tend to coincide with the morning and afternoon commute, and the lunch hour of the contributors.

If you wish to find out more about this work below we provide the abstract to the paper, a visual flowchart of our workflow and some of our our results. The full reference and link to the paper is provided at the bottom of the post.

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.

Keywords: Crowdsourcing; Volunteered Geographic Information; Street View Imagery; Mapillary, OpenStreetCam

Overview of methodology

Spatial distribution of road networks.

Spatial comparison of roads in kilometers.

Full Reference: 

Mahabir, R., Schuchard, R., Crooks, A.T., Croitoru, A. and Stefanidis, A. (2020), Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam, ISPRS International Journal of Geo-Information. 9(6), 341; https://doi.org/10.3390/ijgi9060341 (pdf)