首页|城市滨水绿道景观视觉质量评估及影响因素分析——以南京夹江滨江绿道为例

城市滨水绿道景观视觉质量评估及影响因素分析——以南京夹江滨江绿道为例

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城市滨水绿道是城市绿地建设中的重要一环,兼具生态、游憩、经济、连接等多类功能,是城市景观的重要组成部分,评估和提升其视觉质量对城市特色、风貌的塑造具有积极意义.以南京夹江两岸滨江绿道为例,运用OpenStreetMap获取路网数据生成街景采样点,借助Python和百度街景API爬取街景图像.通过深度学习技术对图像进行语义分割,结合美景度评价,分析景观要素与视觉质量的影响机制.结果显示,南京滨水绿道景观视觉质量仍有提升空间,夹江以南滨江绿道景观视觉质量优于夹江以北;道路宽广度、色彩丰富指数、绿视率、建筑可视度均显著影响滨水绿道景观视觉质量,其中建筑可视度负向影响视觉质量,其余三项均为正向影响;城市滨水绿道景观视觉质量是公众视觉感知和环境空间双向作用的结果,建议关注显著影响指标,依据其影响力度设置优先级以有效提升滨水绿道景观质量.研究结果揭示城市滨水绿道景观视觉感知的影响因素,为滨水绿道景观的优化提供参考.
Visual Quality Assessment and Influencing Factors Analysis of Urban Waterfront Greenway Landscape:A Case Study of Nanjing Jiajiang Riverside Greenway
Urban waterfront greenway is an important part of the construction of urban green space,which has multiple functions such as ecology,recreation,economy,and connection.As an important part of the urban landscape,the visual quality of the urban waterfront has a positive significance for shaping urban characteristics and style.Taking the riverside greenway on both sides of the Jiajiang River in Nanjing as an example,OpenStreetMap is used to obtain road network data to generate street view sam-pling points,and street view images are crawled with the help of Python and Baidu Street View API.The semantic segmenta-tion of the image was carried out by deep learning technology,and the influencing mechanism of landscape elements and visual quality was analyzed in combination with the evaluation of scenic beauty.The results show that there is still room for improve-ment in the visual quality of the Nanjing waterfront greenway landscape,and the visual quality of the riverside greenway south of the Jiajiang River is better than that of the north of the Jiajiang River.Road Width Index,Green Visual Index,and Color Richness Index positively affect visual quality,while Building Visibility is restrained.The visual quality of urban waterfront greenway landscape is the result of the two-way effect of public visual perception and environmental space,and it is recom-mended to pay attention to the significant impact indicators and set priorities according to their impact intensity to effectively improve the landscape level of waterfront greenway.This study reveals the factors that influence the visual perception of urban waterfront greenway landscapes and provides a reference for the optimization of waterfront greenway landscapes.

deep learningstreet view imagerywaterfront greenwayvisual qualitylandscape evaluation

熊星、温晓雨、杨善睿、刘澜、曾伟

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南京农业大学园艺学院,南京 210095

三江学院建筑学院,南京 210012

东南大学艺术学院,南京 214135

深度学习 街景图像 滨水绿道 视觉质量 景观评价

江苏省高校哲学社会科学项目江苏省研究生实践创新计划项目

SKYC2024017SJCX24_0243

2024

园林
中国风景园林学会 上海市园林科学研究所

园林

影响因子:0.13
ISSN:1000-0283
年,卷(期):2024.41(9)