Predicting Impression Evaluation of Building Exterior Appearance Using Street Image Big Data and Deep Learning

In this paper, we propose a method for predicting the impression evaluation of buildings’ exterior appearance using street image big data, and we demonstrate its applicability to architectural design. First, we conduct a large-scale impression evaluation web questionnaire using building exterior images extracted mechanically from street image big data in Ota Ward, Tokyo. Next, by training a deep learning model using the results, we can quantify the impression evaluation of building exterior images taken from different building uses and angles. Furthermore, we analyze the impression evaluation scores predicted by the model and demonstrate this method in architectural design through several case studies.