Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning

Abstract

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.

How the ecological structure affects the aesthetic atmosphere of the landscape: Evaluation of the landscape Beauty of Xingqing Palace Park in Xi’an

“…as if the landscape was like a painting [26]. Therefore, landscape designers usually use the visual methods of landscape picture [27] to develop design ideas. However, this approach can sometimes neglect the balance of ecosystem and the involvement of sensory aesthetic.…”

Innovative Design of Environmental Visual Art Based on Edge Cloud Computing Technology under the Background of Artificial Intelligence

“…It can not only protect the environment and optimize the quality of life, but also has the characteristics of low price, easy acquisition and repeated use [9]. Feng et al (2022) integrated multi-source urban data and deep learning algorithms, taking the urban greenway planning research on both banks of Suzhou River in Shanghai as an example, to study human-oriented urban design planning issues, and provide strong support for human-oriented urban design practice [10]. Pan and Jiang (2022) constructed an evaluation index system for the suitability of urban greenway layout under the background of park city, and obtained the road with the highest suitability score through big data analysis.…”

Retracted: Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”