THEME ARTICLE: Visualization for Smart City Applications Mapping and Visualizing Deep-Learning Urban Beautification Information visualization has great potential to make sense of the increasing amount of data generated by complex machine-learning algorithms. We design a set of visualizations for a new deep-learning algorithm called FaceLift (goodcitylife.org/facelift). This algorithm is able to generate a beautified version of a given urban image (such as from Google Street View), and our visualizations compare pairs of original and beautified images. With those visualizations, we aim at helping practitioners understand what happened during the algorithmic beautification without requiring them to be machine-learning experts. We evaluate the effectiveness of our visualizations to do just that with a survey among practitioners. From the survey results, we derive general design guidelines on how information visualization makes complex machine- learning algorithms more understandable to a general audience. Beautiful places make us feel better. Stendhal’s motto “beauty is the promise of happiness” speaks to this and has been made use of in various studies to show that specific visual cues affect our well-being. 1,2 But what are these visual cues of beauty? Our public realm is filled with exam- ples people perceive as beautiful. Regardless of whether beauty emerges from planning or seren- dipitously, we can identify its cues, and that is useful for supporting evidence-based design of the urban spaces we intuitively love. Based on that premise, here we present a design study that visualizes data-intensive and complex results stemming from a range of deep neural networks. These generative adversarial networks (GANs) beautify a Google Street View scene according to a trained concept of beauty. 3 To vali- date the beautification process, we compare the original image and the beautified one in terms of the elements that have been added or removed. These elements are then mapped into urban de- sign metrics that the urban design literature has identified to characterize great urban spaces. 4 In Tobias Kauer University of Applied Science Potsdam Sagar Joglekar King’s College London Miriam Redi, Luca Maria Aiello, and Daniele Quercia Nokia Bell Labs Cambridge 70 IEEE Computer Graphics and Applications Published by the IEEE Computer Society 0272-1716/18/$33.00 ©2018 IEEE September/October 2018