  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.
