  Rendering Wikipedia content through mobile and augmented reality mediums can
enable new forms of interaction in urban-focused user communities facilitating
learning, communication and knowledge exchange. With this objective in mind, in
this work we develop a mobile application that allows for the recognition of
notable sites featured on Wikipedia. The application is powered by a deep
neural network that has been trained on crowd-sourced imagery describing sites
of interest, such as buildings, statues, museums or other physical entities
that are present and visually accessible in an urban environment. We describe
an end-to-end pipeline that describes data collection, model training and
evaluation of our application considering online and real world scenarios. We
identify a number of challenges in the site recognition task which arise due to
visual similarities amongst the classified sites as well as due to noise
introduce by the surrounding built environment. We demonstrate how using mobile
contextual information, such as user location, orientation and attention
patterns can significantly alleviate such challenges. Moreover, we present an
unsupervised learning technique to de-noise crowd-sourced imagery which
improves classification performance further.
