地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (6): 1330-1338.doi: 10.12082/dqxxkx.2020.200072

• 大数据与城市管理 • 上一篇    下一篇

基于街景影像多特征融合的广州市越秀区街道空间品质评估

崔成1,2, 任红艳1,*(), 赵璐1,2, 庄大方1   

  1. 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
    2. 中国科学院大学资源与环境学院,北京 100190
  • 收稿日期:2020-02-11 修回日期:2020-03-20 出版日期:2020-06-25 发布日期:2020-08-25
  • 通讯作者: 任红艳 E-mail:renhy@lreis.ac.cn
  • 作者简介:崔 成(1995— ),男,河北保定人,硕士生,研究方向为空间数据挖掘。E-mail: cuic@lreis.ac.cn
  • 基金资助:
    国家自然科学基金项目(41571158);国家重点研发计划项目(2016YFC1302602)

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

CUI Cheng1,2, REN Hongyan1,*(), ZHAO Lu1,2, ZHUANG Dafang1   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
  • Received:2020-02-11 Revised:2020-03-20 Online:2020-06-25 Published:2020-08-25
  • Contact: REN Hongyan E-mail:renhy@lreis.ac.cn
  • Supported by:
    National Natural Science Foundation of China(41571158);National Key Research and Development Program of China(2016YFC1302602)

摘要:

全面准确地描述街景影像的多层次特征在基于街景影像对街道空间品质进行评估的研究中具有重要意义。以广州市越秀区为例,获取前后左右各视角街景影像中手工设计的特征(SURF特征、HOG特征)和基于深度学习的特征(语义特征),基于单一特征和多特征融合采用支持向量机(Support Vector Machine, SVM)、随机森林(Random Forest, RF)训练各视角的评估模型。结果表明,以基于SVM建立的单特征模型为例,基于HOG特征(73.03%)、语义特征(72.28%)的模型平均精度优于SURF特征(56.00%),基于SVM前后左右各视角模型的最优分类精度为82.8%(前)、81.7%(后)、76.6%(左)、76.6%(右),而基于RF各视角模型的最优分类精度为82.8%(前)、85.0%(后)、78.1%(左)、70.3%(右)。前后视角的模型精度略优于左右视角。各视角最优模型均为多特征融合模型,最优模型平均分类精度和Kappa系数可达80.6%和0.62。利用街景影像评估街道空间品质时,各算法之间性能差异微弱,而特征选择及组合方式是提升精度的关键。越秀区街道空间品质存在明显空间分异,其西南部的街道空间品质亟待提升。本研究构建了基于街景影像多特征融合的大规模高精度街道空间品质测度方法,实现了对越秀区街道空间品质的评估,研究结果可为相关部门进行街道环境综合整治提供参考。

关键词: 街景影像, 街道空间品质, 多特征融合, 随机森林, 支持向量机, 手工设计特征, 基于深度学习的特征, 广州市

Abstract:

Street View Imagery (SVI) is one of the important data sources for the quantitative research of urban built environment. However, it is difficult to fully represent all the information with one type of feature in the SVI due to its complexity and diversity. In this paper, we proposed an effective multi-feature fusion method to evaluate the street space quality based on SVI. Taking Yuexiu district in Guangzhou city as the study area, the Baidu SVIs in the four orientations (front, behind, left, right) at the sample points were obtained. Speeded Up Robust Feature (SURF) and Histogram of Oriented Gradient (HOG) were derived from SVIs as handcrafted features. Semantic features were also derived from SVIs using ENet convolution neural network as features based on deep learning. Based on single feature and multi-feature fusion, Support Vector Machine (SVM) and Random Forest (RF) were used to train the street space quality evaluation model for the four orientations on the training set. The optimal model and the combination of features were selected according to the classification accuracy and Kappa coefficient on the test set. Results showed that: (1) The optimal classification accuracy of models based on SVM was 82.8% (front), 81.7% (behind), 76.6% (left), 76.6% (right), respectively. In the models based on single feature, the average accuracy of the models based on HOG feature (73.03%) or semantic feature (72.28%) was better than the SURF feature (56.00%). The optimal classification accuracy of the models based on RF algorithm was 82.8% (front), 85.0% (behind), 78.1% (left), 70.3% (right). (2) The accuracy of front and behind orientation model was slightly better than that of left and right orientation. The optimal models of each orientation all are multi-feature fusion models. The average classification accuracy and Kappa coefficient of these optimal evaluation models was 80.6% and 0.62, respectively. These results showed that the proposed method could achieve a high recognition performance. (3) The selection and fusion of features were more determined to the model performance when the SVI were used to evaluate the street space quality, while the performance difference between the two algorithms was small. (4) There were obvious spatial differences in the street space quality of Yuexiu district. The street space quality in the southeast of Yuexiu district needed to be improved. A large scale and high precision street space quality evaluation method was proposed based on multi-feature fusion of SVI and achieved a high recognition performance in this study. And the street space quality score in Yuexiu district was obtained. These results could provide valuable clues for local authorities to conduct comprehensive renovations of urban built environment.

Key words: street view imagery, street space quality, multi-feature fusion, random forest, support vector machine, handcrafted feature, feature based on deep learning, Guangzhou city