Quantifying urban streetscapes with deep learning: focus on aesthetic evaluation
This work addresses the need for scalable quantification of aesthetic factors in urban streetscapes for researchers and practitioners, but it is incremental as it applies existing deep learning methods to a new domain-specific dataset.
The paper tackled the problem of quantifying urban streetscape disorder, specifically billboards on building facades, by developing a deep learning model that achieved 63.17% accuracy in recognizing facade and billboard areas using Intersection-over-Union (IoU) on a dataset from Tokyo.
The disorder of urban streetscapes would negatively affect people's perception of their aesthetic quality. The presence of billboards on building facades has been regarded as an important factor of the disorder, but its quantification methodology has not yet been developed in a scalable manner. To fill the gap, this paper reports the performance of our deep learning model on a unique data set prepared in Tokyo to recognize the areas covered by facades and billboards in streetscapes, respectively. The model achieved 63.17 % of accuracy, measured by Intersection-over-Union (IoU), thus enabling researchers and practitioners to obtain insights on urban streetscape design by combining data of people's preferences.