  In this work, we propose a novel technique to determine the aesthetic score
of a location from social metadata of Flickr photos. In particular, we built
machine learning classifiers to predict the class of a location where each
class corresponds to a set of locations having equal aesthetic rating. These
models are trained on two empirically build datasets containing locations in
two different cities (Rome and Paris) where aesthetic ratings of locations were
gathered from TripAdvisor.com. In this work we exploit the idea that in a
location with higher aesthetic rating, it is more likely for an user to capture
a photo and other users are more likely to interact with that photo. Our models
achieved as high as 79.48% accuracy (78.60% precision and 79.27% recall) on
Rome dataset and 73.78% accuracy(75.62% precision and 78.07% recall) on Paris
dataset. The proposed technique can facilitate urban planning, tour planning
and recommending aesthetically pleasing paths.
