Designing a User-centric Framework for Information Quality Ranking of Large-scale Street View Images

Street view imagery (SVI), largely captured via outfitted fleets or mounted dashcams in consumer vehicles is a rapidly growing source of geospatial data used in urban sensing and development. These datasets are often collected opportunistically, are massive in size, and vary in quality which limits the scope and extent of their use in urban planning. Thus far there has not been much work to identify the obstacles experienced and tools needed by the users of such datasets. This severely limits the opportunities of using emerging street view images in supporting novel research questions that can improve the quality of urban life. This work includes a formative interview study with 5 expert users of large-scale street view datasets from academia, urban planning, and related professions which identifies novel use cases, challenges, and opportunities to increase the utility of these datasets. Based on the user findings, we present a framework to evaluate the quality of information for street images across three attributes (spatial, temporal, and content) that stakeholders can utilize for estimating the value of a dataset, and to improve it over time for their respective use case. We then present a case study using novel street view images where we evaluate our framework and present practical use cases for users. We discuss the implications for designing future systems to support the collection and use of street view data to assist in sensing and planning the urban environment.