Crowd-mapping is a relatively new field of research, which involves the collection of geographic data by a crowd of workers. The collection of said data is of great importance for organizations like municipalities, where it is used for applications such as maintaining streets and greenery. The benefit of crowd-mapping over traditional mapping methods, where workers physically observe the area, is that it has the potential to be far more cost-effective and time-efficient. As this should not come at the cost of losing accuracy, research needs to be done on how to effectively map objects in a city. Although previous work has focused on mapping urban objects using street-level imagery, they are all specifically aimed at a single type of object. Furthermore, they do not offer a general method for geo-location estimation and do not estimate the height of the objects. All of the systems designed in previous work only support task execution using a web platform. As crowd-mapping is nothing without the crowd, it is important to keep the workers engaged. No research had been done on how the task execution platform and type of task could affect the worker engagement and satisfaction. In this thesis we will design a system for crowd-mapping urban objects using street-level imagery. We will propose novel methods for geo-location and object height estimation. Experimentation showed that the proposed geo-location method was able to deliver an accuracy with up to 83% of the estimations being within 2.5 meters of the ground truth with a mean distance of 1.85 meters. The height estimation showed up to 85% of the estimations being within 30 centimeters from the ground truth with a mean difference of 15 centimeters. Furthermore, the system supports task execution on three platforms; web, mobile and mobile virtual reality. We demonstrated the feasibility of executing mapping-, data-enrichment- and verification tasks on each of these platforms. Experimentation with the different platforms showed that the type of task and execution platform affects user engagement, cognitive load, satisfaction and execution time.

In online crowd mapping, crowd workers recruited through crowdsourcing marketplaces collect geographic data. Compared to traditional mapping methods, where workers physically explore the area, the benefit of using online crowd mapping is the potential to be cost-effective and time-efficient. Previous studies have focused on mapping urban objects using street-level imagery. However, they are specifically aimed at a single type of object, and only through web platforms. To the best of our knowledge, there is still a lack of understanding on how workers perform the mapping tasks through different platforms. Aiming to fill this knowledge gap, we investigate the worker performance across web, mobile, and virtual reality platforms by designing a multi-platform system for mapping urban objects using street-level imagery with novel methods for geo-location estimation. We design a preliminary study to show the feasibility of executing online mapping tasks on three platforms. The result demonstrates that the type of task and execution platform can affect the worker performance in terms of worker accuracy, execution time, user engagement, and cognitive load.@en