Brief description: 

One of the most innovative products that the QROWD project offers is that of the Virtual City Explorer (VCE). VCE is a crowdsourced solution for mobility infrastructure maps. Crowdsource users identify city features of interest, such as bike racks, and tag the information by adding photographs and answering some basic questions. This information is then modelled onto the virtual map which anyone can see.

So, why is this a valuable product? Well, VCE can do things other larger mapping companies cannot. For example, the case of Trento, Italy, demonstrates this impressively, given the nature of the city. Trento is a medieval walled settlement, featuring many roads, paths, and features that are inaccessible by most of the cars and transport scouts used to collect data for things like Google Maps.

In these types of cases, pedestrian access is the only way quality data can be generated about a town or city. It is also the only way that that these types of features can actually be checked against other data, making sure that that these features do indeed exist where a map says they should. So how does human-driven data get rendered into VCE?


A unique aspect of the VCE is the gamification of information. By this we mean that workers can earn points every time that they upload data or answer questions. On the VCE this is then logged, and user rankings can be provided. The more data someone contributes, the more points that they earn – and the more information the public can know about the city.

VCE is not only effective on cities such as Trento. Other types of settlement and buildings, too, can greatly benefit from crowdsourcing information. VCE aims to be tested on ski resorts, where variations in weather, season and levels of snow can change which features are currently available. We look forward to going into more detail about the case studies of VCE in the near future!

Main Features: 

Using Google Street view behind the scenes, crowd workers can login to the city virtual viewer and follow instructions to locate static items, for example bike racks. Individuals can be confined into certain areas of the city, and can see whether other bike racks have previously been logged on the map. Previously logged items are flagged as “taboo”, which allows the system to optimize cost and coverage as workers can only log non-taboo items: meaning that individuals are encouraged to continually find new items.

Areas of Application: 

Smart Cities, Urban Planning, Urban Mobility

Market Trends and Opportunities: 

Instead of paying municipality workers to walk and survey the city, this is a fantastic tool to locate and verify city infrastructure for a few dollars using crowdsourcing techniques. 

Customer Benefits: 

This is a very low-cost alternative for municipalities to verify their infrastructure. No need of workers in the city, but crowdsourcing. 

Technological novelty: 

Based on Google Street View and the latest advances and theory related to crowdsourcing.

Component / Service / App
Locating bike racks


University of Southampton
United Kingdom of Great Britain and Northern Ireland
BDVA member
BDV Reference categories: 
Data Analytics
Data Visualisation and Interaction
Urban Mobility
Readiness Level: 
smart cities
urban mobility