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

Guanqing Feng,Guangtian Zou,Pengjin Wang,Baiyuan Ding

doi:10.1155/2022/3287117

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.

Highlights

In the landscape engineering system, landscape resources are divided into 3 categories: human resources, natural resources, and landscape visual resources [1]
It has become increasingly evident in the last decades that the visual aesthetic quality of landscapes is considered an important resource for the maintenance of people’s psychological wellbeing, as well as for the conservation of biodiversity, cultural heritage, and the potential of landscapes [5–8]. erefore, the development of a methodology for the analysis and evaluation of landscape aesthetics that can be accepted by the scientific community as a whole is a major challenge facing the academic community today
One of the focuses of this study is to focus on the visual aesthetic preferences of fire-escapes at the subjective level. erefore, subjective opinions from different groups of people had to be collected in order to determine local preferences for the visual aesthetics of the streets

Summary

Introduction

In the landscape engineering system, landscape resources are divided into 3 categories: human resources, natural resources, and landscape visual resources [1]. The ambiguity of landscape aesthetics is due to the subjectivity of its definition, the lack of standardisation of its methodology, the lack of clarity in its practical application, and its own particular lack of replicability [4]. It has become increasingly evident in the last decades that the visual aesthetic quality of landscapes is considered an important resource for the maintenance of people’s psychological wellbeing, as well as for the conservation of biodiversity, cultural heritage, and the potential of landscapes [5–8]. It has become increasingly evident in the last decades that the visual aesthetic quality of landscapes is considered an important resource for the maintenance of people’s psychological wellbeing, as well as for the conservation of biodiversity, cultural heritage, and the potential of landscapes [5–8]. erefore, the development of a methodology for the analysis and evaluation of landscape aesthetics that can be accepted by the scientific community as a whole is a major challenge facing the academic community today

Objectives

Methods

Results