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

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.

Expanding Data Imaginaries in Urban Planning: Foregrounding lived experience and community voices in studies of cities with participatory and digital visual methods

“…The literature is saturated with projects that use social media images to capture the whereabouts of users, relying on geotag and timestamp metadata to map spatial-temporal dynamics of city life, without looking into the visual content of the images. Examples range from studies of Flickr (Becker et al, 2015;Haider & Ali, 2018;Hollenstein & Purves, 2010;, Panoramio (García-Palomares et al, 2015), and Instagram (Boy & Uitermark, 2016Domínguez et al, 2017;Mukhina et al, 2017), Weibo (Cai et al, 2017), to Getty Images . Such projects typically use image data to map the pulse of the city, identify urban hot spots, or detect urban events and clusters.…”