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.

Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation

“…Some studies apply modern technology, such as AI and VR, for QoL and walkability. Sou et al [18] proposed an AI and human co-operative evaluation (AIHCE) framework that facilitated communication design between designers and stakeholders based on human emotions and values for evaluating street space. The study suggested that the proposed framework can contribute to fostering people's awareness of streets as public goods, reflecting the essential functions of public spaces and the residents' values and regional characteristics, improving the city's sustainability.…”

“…Vichiensan et al [17] conclude that walking is essential to provide equitable access and mobility in a city. Sou et al [18] developed a framework for evaluating street space by considering human emotions and values, aiming to improve communication between designers and stakeholders. Nakamura [19,20] highlights the importance of individual functions in pedestrian spaces and the effectiveness of design elements and proposes using virtual reality (VR) evaluation to improve the design process and create more effective pedestrian spaces.…”

AI-Based Evaluation of Streets for People in Bangkok: Perspectives from Walkability and Lingerability

“…However, it is crucial to note that Walk Score is currently available only in some cities in America, the UK, Canada, and Australia. Sou et al have developed an image recognition technique based on deep learning to evaluate the walkability and lingerability of streets [17,18]. This facilitates the continual and objective evaluation of street space.…”

“…This study represents a significant advancement in the assessment of street spaces through the innovative integration of AI and Human Cooperative Evaluation (AIHCE). The AIHCE method, a deep learning-based street space evaluation developed by the authors [17,18], leverages labeled image data to infer spatial impressions, providing a nuanced understanding of street environments. Building upon our previous work in AI-based evaluation, which was confined to road cross-sections and segments, this study extends its scope to encompass entire road networks.…”

Multi-Level Transformer-Based Social Relation Recognition

“…Understanding the social relationships among people is thus essential for identifying the link between social relationships and health outcomes. In addition, effective social relation recognition (SRR) can also provide valuable interactive information for other related tasks, such as an activity analysis [ 2 ] and group emotion detection [ 3 ], which further benefits more comprehensive tasks, such as smart city design [ 4 ] and social sustainability [ 5 ].…”