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城市绿地的视觉质量评估是景观设计学领域的重要话题，但传统研究方法在实际操作中存在一定局限。人工智能技术与街景大数据的发展为城市绿地感知评估带来了契机。然而，由于中国城市的绿地尚未被街景服务全面覆盖，相关研究的开展受到了限制。本文立足于景观的公众感知评价，以中国广州市珠江公园为例，采用便捷的全景相机图像采集与处理流程，利用Segformer-B5语义分割模型和Vi T-base-p16图像分类模型分别对公园图像计算客观评价指标（绿视率、天空视域因子、路面占比、人工构筑物占比）与主观评价指标（吸引力、丰富度、自然程度、压抑程度），从而进行公园绿地视觉质量评估。基于各项评价指标空间分布图，进行综合分析并识别低分值区域。结果发现，植被与水体有助于提升公园的吸引力与游客的积极感知，而过多的天空与构筑物则可能会产生相反效果；消极的人工景观和压抑的建筑也会降低景观质量。本研究所提出的图像采集与视觉感知评估方法可为城市绿地更新与管理提供科学依据。
Abstract：Visual quality assessment of urban green spaces is a major topic in landscape architecture research, yet traditional methods face limitations in practice. The rapid development of artificial intelligence and street-view big data offers opportunities for advancing green space perception studies. However, the lack of full street view image coverage of green spaces in China poses challenges for related research. Focusing on public landscape perception evaluation, this research took Zhujiang Park in Guangzhou, China as a case study. The research team utilized a convenient image collection method by panoramic camera and an effective processing workflow, and then employed the Segformer-B5 semantic segmentation model and the Vi T-base-p16 image classification model to calculate four objective evaluation metrics(green view index, sky view factor, road visibility index, and artificial structure visibility index) and four subjective evaluation metrics(attractiveness, richness, naturalness, and depression) for visual quality assessment. Based on the spatial distribution results of these metrics, comprehensive analyses were conducted and low-score areas were identified. Research results indicate that vegetation and water features significantly enhance park attractiveness and positive perceptions, while excessive sky and artificial structures produce negative effects; oppressive artificial landscapes and constrained architectural views also lower overall landscape quality. The image collection and visual perception evaluation methods proposed in this study provide a scientific basis for the renovation and management of urban green spaces.
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(1)Model comparison data are available on the Open MMlab Git Hub page.
(2) The 13 common visual elements in parks include wall, building, sky, tree, shrub,ground cover, first-class road, second-class road, third-class road, fence,skyscraper, bench, and streetlight.
(3)Manual screening refers to image selection based on subjective perception and personal experience by the collector, without rigid quantitative criterion.
(1)模型比较数据可通过Open MMlab的github网页获取。
(2)13种公园场景常见视觉要素包括墙体、建筑、天空、乔木、灌木、地被、一级道路、二级道路、三级道路、围栏、摩天大楼、座椅、路灯。
(3)人工判读指图像采集者依据主观感知和个人经验筛选图像，无量化指标。
基本信息:
中图分类号:TU986.5;TP18;TP391.41
引用信息:
[1]赵旭凯,林广思.基于全景影像采集与深度学习技术的城市绿地感知评价研究——以广州市珠江公园为例[J].景观设计学(中英文),2024,12(06):7-24.
基金信息:
国家自然科学基金面上项目“应对主被动排斥的城市绿色空间游憩场所包容性设计研究”（编号：52378054）; 中央高校基本科研业务费专项资金项目“基于公众感知的绿地供给评价方法研究”（编号：CGPY202410）; 华南理工大学百步梯攀登计划项目“深度学习驱动的公园感知评价方法研究及其应用”（编号：j2tw202402095）~~
2024-12-15
2024-12-15