Can We Predict the Scenic Beauty of Locations from Geo-tagged Flickr Images?
This work addresses urban planning and tourism by enabling aesthetic predictions from social media data, but it is incremental as it applies existing methods to new datasets.
The paper tackles the problem of predicting the scenic beauty of locations by using machine learning classifiers trained on Flickr photo metadata and TripAdvisor aesthetic ratings for cities like Rome and Paris, achieving up to 79.48% accuracy on the Rome dataset.
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.