Department of Architecture, Faculty of Architecture, Harbin Institute of Technology, Harbin 150006, Heilongjiang, China.
Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology (Harbin Institute of Technology), Harbin 150006, Heilongjiang, China.
Comput Intell Neurosci. 2022 Feb 7;2022:3287117. doi: 10.1155/2022/3287117. eCollection 2022.
This paper integrates classical design theory, multisource urban data, and deep learning to explore an accurate analytical framework in a new data environment, providing a scientific analysis path for the "where" and "how" of greenways in a high-density built environment. The analysis is based on street view data and location service data. Through the integration of multiple data sources such as street scape data, location service data, point-of-interest data, structured web data, and refined built environment data, a systematic measurement of the key elements of density, diversity, design, accessibility to destinations, and distance to transport facilities as defined in the Five Elements of High Quality Built Environment (5D) theory is achieved. The assessment of alignment potential was carried out. The key factors influencing the aesthetics of the street were identified. Based on an extensive landscape perception-based survey, it was found that although different respondents had different views and preferences for the same street scape, their preferences were overwhelmingly influenced by the visual quality of the street scape aesthetics itself, with higher aesthetic quality of the landscape.
本文整合了经典设计理论、多源城市数据和深度学习，在新的数据环境中探索准确的分析框架，为高密度建成环境中的绿道“在哪里”和“如何”提供了科学的分析路径。该分析基于街景数据和位置服务数据。通过整合街景数据、位置服务数据、兴趣点数据、结构化网络数据和精细化建成环境数据等多种数据源，对高密度建成环境的 5D 理论中的密度、多样性、设计、可达性和到交通设施的距离等关键要素进行了系统的测量。对沿线的潜在条件进行了评估。确定了影响街道美观的关键因素。基于广泛的基于景观感知的调查，发现尽管不同的受访者对同一街景有不同的看法和偏好，但他们的偏好主要受到街景美学本身视觉质量的影响，景观的美学质量越高。
