基于街景图像的城市环境评价研究综述

doi:10.12082/dqxxkx.2019.180311

Abstract

Abstract:

Urban environmental assessment research has traditionally adopted a method based on field survey, which is difficult to evaluate on a large scale and refined scale. Street view image has a wide coverage, can provide street-level landscape and intuitively reflect the city facade information, and have the advantage of lower cost than on-site data collection, so it provides a large sample data source and new research ideas for urban environmental assessment. Different from the sky view of remote sensing image and the user interaction data of geo-tagged social media, street view image is more focused on recording stereoscopic sectional view of the city street level from the perspective of people, which can represent scenes seen or felt from the ground on a fine scale, so it is more suitable to replace on-site observation of urban environmental assessment. The continuous breakthrough of artificial intelligence technology and its application in various fields make it possible to conduct urban environmental assessment research based on street view image on a wide range of spatial scales. In this paper, we first described and compared three categories of data sources commonly used in urban environmental assessment including street view image, remote sensing image and geo-tagged social media data, and summarized the advantages of street view image in urban environmental assessment. Then we classified the methods used in urban environment assessment based on street view image into the following four categories : methods based on image analysis, statistical analysis, artificial intelligence and spatial analysis. Next, from the urban physical, social, economic and aesthetic environment, we summarized the research and application of street view image in urban environmental assessment. Finally, we pointed out the innovations, limitations and future research directions of the urban environmental assessment based on street view image. On one hand, the application of artificial intelligence represented by deep learning promotes the research progress of urban environmental assessment on large-scale and fine-scale. On the other hand, in the era of big data, the integration of data source represented by street view image, remote sensing image, and geo-tagged social media data will help promote urban environmental assessment research from multiple perspectives and multi-level.

Key words: street view image, urban environmental assessment, urban factor, artificial intelligence, deep learning

Liying ZHANG, Tao PEI, Yijin CHEN, Ci SONG, Xiaoqian LIU. A Review of Urban Environmental Assessment based on Street View Images[J].Journal of Geo-information Science, 2019, 21(1): 46-58.DOI:10.12082/dqxxkx.2019.180311

Figures/Tables 3

Tab. 1

Street view image APIs"

街景API	覆盖范围	图像最大分辨率（宽度 $×$ 高度）	使用样例
谷歌	114个国家及地区	2048 $×$ 2048	https://maps.googleapis.com/maps/api/streetview?size=400×400&location=40.720032,73.988354&fov=90&heading=235&pitch=10&key=YOUR_API_ KEY
百度	中国372座城市	1024 $×$ 512	http://api.map.baidu.com/panorama/v2?width=512&height=256&location=116.313393,40.04778&fov=180&ak=YOUR_API_KEY
腾讯	中国296座城市	960 $×$ 640	http://apis.map.qq.com/ws/streetview/v1/image?size=600 $×$ 480&pano=10011 022120723095812200&pitch=0&heading=0&key= YOUR_API_KEY

Tab. 1

Tab. 2

Tab. 3

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数据类别	采样方式	优缺点	代表性研究
街景图像	地面拍摄	优点：从微观和人的视角精细化记录城市街道层级的立体剖面景象;覆盖范围广、数据量大、成本低缺点：数据在空间分布不均匀	社区环境^[11]、城市安全感^[21]、收入预测^[22]、建筑特色^[23]
遥感影像	空中拍摄	优点：从宏观和高空鸟瞰的视角记录城市,覆盖范围广、数据在空间分布均匀缺点：成本高、分辨率低	地表变化分析^[12]、农作物识别^[13]、空气质量评价^[14]、灾情评价^[15]、城市热环境变化^[16]
地理标记社交媒体数据	网络用户发布	优点：用户交互的内容不仅有文本信息,还包含地理位置、时间、图像、视频、情感等信息;具有动态性、时效性和交互性; 缺点：数据稀疏、数据存在口语化、错误拼写和缩写、使用特殊符号等问题	空气质量^[17]、台风灾害^[18]、旅游景点评价^[19]、城市风貌感知^[20]

类别	评价要素	数据集	方法	代表性研究
物理环境	绿色植物、行人安全、行人道设施、机动交通、建筑、交通标志等	谷歌街景	相关性分析泊松回归机器学习	1.街景图像审计社区环境的可行性：Rundle^[11],Badland^[30],Clarke^[45]; 2.城市安全性：Kronkvist^[46],Li等^[44],Mooney^[47]3.土地利用类型：Li^[29]
社会环境	汽车、人行道、行人、建筑、天空等	谷歌街景	机器学习深度学习	1.人口分布与政治倾向：Gebru等^[48];2.城市可步行性：Yin等^[34],Hara等^6],Yin^[4];3.城市安全感：Porzi等^[21],Li^[27]
经济环境	绿色植被、地面,建筑物、树、天空	谷歌街景	基于像元的图像分析机器学习	1.收入预测：Glaeser^[22];2. 收入与物理环境的关系：Li^[26],Arietta^[49]
美学环境	行道树、绿色植被、建筑物	谷歌街景百度街景腾讯街景	机器学习深度学习图像分析	1.街道绿化：郝新华等^[3],Berland^[50],Li等^[24]; 2. 城市风貌：Liu等^[36],Cheng等^[51],唐婧娴等^[52]; 3. 建筑特色：Doersch等^[53],Lee等^[23]

A Review of Urban Environmental Assessment based on Street View Images

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