URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 -URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站

THE 3rd VISION


第三只眼 THE 3rd VISION

A-Stra Advisors, Atoms Atelier, Currant.ai (Waishan Qiu 邱外山, Xun Liu 刘浔, Xiaokai Huang 黄笑恺, Xu Zhang 张旭, Ruijun Liu 刘瑞珺, Kaining Peng 彭凯宁, Xiaojiang Li 李小江, Junjiao Gan 甘骏焦)

街道环境测量主要集中在环境物理特征的总量上,缺乏好的工具描述人们感知。因此,研究人类对街道环境的感知是极其困难的。在这种情况下,设计和规划是一个主观的自上而下的过程,只有专家和建筑师才能参与和评估。先前的研究表明,围合、尺度、街道的多样性直接关系到一个人对那个地方的好恶(尤因 &克莱门特,2013)。随着自动驾驶汽车产业的发展,目前计算机视觉和人工智能已被广泛应用于监控道路状况。使用谷歌街景图像,我们可以获取人类对建成环境的整体感知。本研究以深圳为例,运用最先进的技术识别街道空间质量。大数据成为街道环境的有效测量工具。换句话说,它变成了我们用来观察城市的眼睛。它有助于我们根据公众的城市形象调查城市的机制,揭示人类心理体验与物质环境之间的关系。

在展览期间,观看带有AR(增强现实)和VR(虚拟现实)设备的投影屏幕,参观者可以体验机器如何看到街景图像。通过谷歌街景图像和在线调研收集的数据,可以证明人类感知与街道中某些元素(例如尤因的理论所提及的围合、尺度等)之间的相关性。当测量应用于深圳这座城市,人们可以确定哪条街是最适合步行的街道,这体现的是人类活动与城市环境之间的关系。测量过程也在全球6个城市实施。其结果可以帮助我们探讨高品质街道环境的吸引因子。

Measuring the street environment mostly focused on gross quantities of the environment’s physical features, without figures describing people’s overall perception. Therefore, it is extremely difficult to study the human perception of the street environment. In this case, design and planning is a subjective top-down process and only can be participated and evaluated by experts and architects. Prior research suggests that the enclosure, the human scale, the diversity of a street is directly related to a person’s appreciation of that place (Ewing & Clemente, 2013). With the advance of the autonomous vehicle industry, nowadays computer vision and artificial intelligence have been widely applied to monitor road conditions. Using Google street images, it becomes possible for us to access human overall perception of the built environment. This study takes Shenzhen as an example and applies those state-of-the-art technologies to identify street space quality. Big data becomes an efficient audit tool of the street environment. In other words, it becomes the eyes we used to observe the city. It helps us to investigate the mechanism of the city according to the public’s image of the city and reveals the relationship between human mental experience and the physical environment.

During the exhibition, watching the projection screen with AR & VR devices, visitors can experience how the machine sees the street images. With Google Street images and data collected from an online survey, the correlation between human perception and certain elements (enclosure, human scale and so on in Ewing’s theories, for example) of the street can be testified. When the measurement applied in Shenzhen city, people can identify which street is the most walking-friendly street, showing the relationship between physical activity and urban environment. The measurement process is also implemented in 6 global cities. From the result we can study the attractive factor which contributes to the high-quality street environment.