Street Space Quality Evaluation in Yuexiu District of Guangzhou City based on Multi-feature Fusion of Street View Imagery

Cheng CUI; Hongyan REN; Lu ZHAO; Dafang ZHUANG

doi:10.12082/dqxxkx.2020.200072

Journals >Journal of Geo-information Science >Volume 22 >Issue 6 >Page 1330 > Article

Journal of Geo-information Science
Vol. 22, Issue 6, 1330 (2020)

Street Space Quality Evaluation in Yuexiu District of Guangzhou City based on Multi-feature Fusion of Street View Imagery

Cheng CUI^{1、1、2、2}, Hongyan REN^1、1、*, Lu ZHAO^{1、1、2、2}, and Dafang ZHUANG^1、1

Author Affiliations

¹. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101

¹State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

². 中国科学院大学资源与环境学院,北京 100190

²College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

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DOI: 10.12082/dqxxkx.2020.200072 Cite this Article

Cheng CUI, Hongyan REN, Lu ZHAO, Dafang ZHUANG. Street Space Quality Evaluation in Yuexiu District of Guangzhou City based on Multi-feature Fusion of Street View Imagery[J]. Journal of Geo-information Science, 2020, 22(6): 1330 Copy Citation Text

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Fig. 1. Sample of SVIs with high/low street space quality from 4 orientations

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Fig. 2. Technical flow chart of street space quality evaluation

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Fig. 3. Street space quality in Yuexiu District

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参数名	描述	取值
ak	开发者密钥,通过申请获取	32位字符
width	图片宽度,单位为像素,范围[10,1024]	1024
height	图片高度,单位为像素,范围[10,512]	512
location	街景采样点的位置	采样点经纬度坐标
coordtype	街景采样点的坐标类型	wgs84ll
heading	朝向角,表示与正北方向夹角,范围[0,360]	平行/垂直于道路方向
pitch	俯仰角,街景相机低头的角度,范围[0,90]	0
fov	视场角,范围[10,360]	90

Table 1. Required parameters for Baidu map street view imagery

	各视角模型的分类精度/Kappa系数
	SVM算法				RF算法
	前向街景	后向街景	左向街景	右向街景	前向街景	后向街景	左向街景	右向街景
SURF特征	50.0%/0.00	60.0%/0.23	60.9%/0.22	53.1%/0.08	46.9%/-0.07	71.7%/0.43	60.9%/0.23	56.3%/0.13
HOG特征	76.6%/0.53	73.3%/0.46	70.3%/0.40	71.9%/0.44	79.7%/0.59	70.0%/0.39	73.4%/0.47	70.3%/0.41
语义特征	75.0%/0.51	75.0%/0.51	70.3%/0.42	68.8%/0.38	81.3%/0.62	81.7%/0.63	71.9%/0.44	65.6%/0.32
SURF特征+HOG特征	68.8%/0.37	75.0%/0.50	73.4%/0.47	67.2%/0.35	76.6%/0.53	75.0%/0.50	68.8%/0.38	62.5%/0.25
SURF特征+语义特征	81.3%/0.63	68.3%/0.37	70.3%/0.41	70.3%/0.41	82.8%/0.65	83.8%/0.67	73.4%/0.47	57.8%/0.15
HOG特征+语义特征	82.8%/0.66	81.7%/0.63	76.6%/0.54	71.9%/0.44	82.8%/0.66	85.0%/0.70	78.1%/0.57	67.2%/0.35
SURF特征+HOG特征+语义特征	82.8%/0.66	78.3%/0.57	76.6%/0.54	76.6%/0.53	82.8%/0.65	81.7%/0.63	78.1%/0.57	62.5%/0.25

Table 2. Model classification accuracy and Kappa coefficient

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