  Citizen engagement and technology usage are two emerging trends driven by
smart city initiatives. Governments around the world are adopting technology
for faster resolution of civic issues. Typically, citizens report issues, such
as broken roads, garbage dumps, etc. through web portals and mobile apps, in
order for the government authorities to take appropriate actions. Several
mediums -- text, image, audio, video -- are used to report these issues.
Through a user study with 13 citizens and 3 authorities, we found that image is
the most preferred medium to report civic issues. However, analyzing civic
issue related images is challenging for the authorities as it requires manual
effort. Moreover, previous works have been limited to identifying a specific
set of issues from images. In this work, given an image, we propose to generate
a Civic Issue Graph consisting of a set of objects and the semantic relations
between them, which are representative of the underlying civic issue. We also
release two multi-modal (text and images) datasets, that can help in further
analysis of civic issues from images. We present a novel approach for
adversarial training of existing scene graph models that enables the use of
scene graphs for new applications in the absence of any labelled training data.
We conduct several experiments to analyze the efficacy of our approach, and
using human evaluation, we establish the appropriateness of our model at
representing different civic issues.
