论文推送 | 使用可穿戴式相机和机器学习进行人与城市绿化的接触研究

导读

本期为大家推介的内容为论文《Assessing personal exposure to urban greenery using wearable cameras and machine learning》(使用可穿戴式相机和机器学习评估个体的绿化暴露).

城市绿化与人们的行为密切相关。随着人工智能的发展，可穿戴传感器和云计算技术的进步，人们不断探索通过新数据和新技术研究人与城市绿化之间关系的潜力，例如使用多源数据评估人口对城市绿化的暴露程度。本文以一个参与者为例，提出并验证了使用可穿戴式摄像头（Narrative Clip 2）和机器学习（Applications Programming Interface of Microsoft Cognitive Service）评估个人暴露于城市绿化的有效性。Microsoft API用于识别可穿戴设备拍摄的个人图像中的城市绿化标签，包括“花”，“森林”，“花园”，“草”，“绿色”，“植物”，“场景”和“树” 。通过计算所有拍摄图像中城市绿化标签的频率来评估个人对城市绿化的暴露程度。此外，还探讨了个人对城市绿化暴露的总体评估和规律（包括“静态暴露”和“动态暴露”），以确定个人的绿色生活记录的特征。这项研究做出了勇敢的尝试，可能会为应用个人大数据研究个人行为提供新的视角。

—论文相关—

题目：《Assessing personal exposure to urban greenery using wearable cameras and machine learning》

作者：Zhaoxi Zhang , Ying Long* , Long Chen, Chun Chen

发表刊物：《Cities》

URL:https://doi.org/10.1016/j.cities.2020.103006

(点击文末“阅读原文”或复制粘贴至浏览器搜索可查看)

摘要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.

—论文原文—