A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Assessing personal exposure to urban greenery using wearable cameras and machine learning
Abstract 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.
Highlights Test the validity of adopting a wearable camera to track personal exposure constantly and effortlessly in an urban context Take advantage of the Microsoft API to simplify the application of machine learning in processing images Measure and evaluate the overall level and characteristics of personal ‘Greenery lifelogging’ by individual lifelogging data Verify the effectiveness, accuracy and feasibility of the innovation supported by wearable camera and machine learning
Assessing personal exposure to urban greenery using wearable cameras and machine learning
Abstract 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.
Highlights Test the validity of adopting a wearable camera to track personal exposure constantly and effortlessly in an urban context Take advantage of the Microsoft API to simplify the application of machine learning in processing images Measure and evaluate the overall level and characteristics of personal ‘Greenery lifelogging’ by individual lifelogging data Verify the effectiveness, accuracy and feasibility of the innovation supported by wearable camera and machine learning
Assessing personal exposure to urban greenery using wearable cameras and machine learning
Zhang, Zhaoxi (author) / Long, Ying (author) / Chen, Long (author) / Chen, Chun (author)
Cities ; 109
2020-08-29
Article (Journal)
Electronic Resource
English
Assessing urban greenery using remote sensing
| SPIE | 2022
Assessing the visibility of urban greenery using MLS LiDAR data
| Elsevier | 2022