Can We Predict the Scenic Beauty of Locations from Geo-tagged Flickr Images?

TL;DR

This paper introduces a machine learning approach to predict the aesthetic quality of locations using social metadata from Flickr images, aiding urban and tourism planning.

Contribution

It presents a novel method that leverages Flickr social metadata and aesthetic ratings to classify locations by scenic beauty, validated on datasets from Rome and Paris.

Findings

Achieved up to 79.48% accuracy in Rome

Achieved up to 73.78% accuracy in Paris

Models effectively predict scenic beauty from social metadata

Abstract

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.