  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.
