Street Design for Hedonistic Sustainability through AI and Human Co-Operative Evaluation

Abstract

Recently, there has been an increasing emphasis on community development centered on the well-being and quality of life of citizens, while pursuing sustainability. This study proposes an AI and human co-operative evaluation (AIHCE) framework that facilitates communication design between designers and stakeholders based on human emotions and values and is an evaluation method for street space. AIHCE is an evaluation method based on image recognition technology that performs deep learning of the facial expressions of both people and the city; namely, it consists of a facial expression recognition model (FERM) and a street image evaluation model (SIEM). The former evaluates the street space based on the emotions and values of the pedestrian’s facial expression, and the latter evaluates the target street space from the prepared street space image. AIHCE is an integrated framework for these two models, enabling continuous and objective evaluation of space with simultaneous subjective emotional evaluation, showing the possibility of reflecting it in the design. It is expected to contribute to fostering people’s awareness that streets are public goods reflecting the basic functions of public spaces and the values and regional characteristics of residents, contributing to the improvement of the sustainability of the entire city.