User Contribution Patterns and Completeness Evaluation of Mapillary, a Crowdsourced Street Level Photo Service

Abstract Mapillary is a Web 2.0 application which allows users to contribute crowdsourced street level photographs from all over the world. In the first part of the analysis this article reviews Mapillary data growth for continents and countries as well as the contribution behavior of individual mappers, such as the number of days of active mapping. In the second part of the analysis the study assesses Mapillary data completeness relative to a reference road network dataset at the country level. In addition, a more detailed completeness analysis is conducted for selected urban and rural areas in the US and part of northern Europe for which the completeness of Mapillary data will also be compared with that of Google Street View. Results show that Street View provides generally a better coverage on almost all road categories with some exceptions for pedestrian and cycle paths in selected cities. However, Mapillary data can be conveniently collected from any mobile device that is equipped with a photo camera. This gives Mapillary the potential to reach better coverage along off‐road segments than Google Street View.