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作者

Zhaoxi Zhang;Ying Long;Long Chen;Chun Chen

来源

CITIES,Vol.109,Issue1,Article 103006

语言

英文

关键字

Personal imagery;Greenery lifelogging;Image detection;Microsoft API

作者单位

School of Architecture, Tsinghua University, Beijing, China;School of Architecture and Hang Lung Center for Real Estate, Key Laboratory of Eco Planning & Green Building, Ministry of Education, Tsinghua University, China;School of Architecture, Tsinghua University, Beijing, China;School of Architecture and Hang Lung Center for Real Estate, Key Laboratory of Eco Planning & Green Building, Ministry of Education, Tsinghua University, China

摘要

Urban greenery is closely related to people's behaviour. With the advancement of science and technology in Artificial Intelligence, wearable sensors and cloud computing, the potential for studying the relationship between people and urban greenery through new data and technology is constantly being explored, such as assessing population exposure to urban greenery using multi-source big data. Taking one individual participant as a case study, this paper proposes and validates the effectiveness of using wearable camera (Narrative Clip 2) and machine learning (Applications Programming Interface of Microsoft Cognitive Service) to assess personal exposure to urban greenery. Microsoft API is used to identify urban greenery tags, including “flower”, “forest”, “garden”, “grass”, “green”, “plant”, “scene” and “tree”, in personal images taken by the wearable camera. Personal exposure to urban greenery is assessed by calculating the frequency of the urban greenery tags in all the images taken. Furthermore, the overall evaluation and regularity of personal exposure to urban greenery (including “static exposure” and “dynamic exposure”) are explored to identify the characteristics of individual's greenery lifelogging. This study makes a brave attempt that may contribute a new perspective in applying personal big data in studying individual behaviour.