文章基本信息

标题：Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning
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作者：Guanqing Feng ; Guangtian Zou ; Pengjin Wang 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2022
卷号：2022
DOI：10.1155/2022/3287117
语种：English
出版社：Hindawi Publishing Corporation
摘要：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.