基于街景图像的城市街道绿视率计量方法比较分析

Comparison of Computational Methods for Urban Street Green View Indexes Based on Street View Images

【目的】针对街景图像在绿视率计量研究中的使用做出了说明,综合研究现状,对不同的绿视率数据获取方法和计算方法进行了介绍。【方法】对比了传统方法和利用算法模型(PSPNet或SegNet)的自动化方法,阐述了传统方法存在效率低、损耗大、精确度低等不足,而自动化方法有效的解决了这些问题。【结果】基于卷积神经网络模型的图像语义分割,使街景图片的处理与分析变得更为简便,但自动化方法仍有不足之处需要完善。【结论】文章指出利用机器学习来处理数据问题是未来研究发展的新趋势,预测融合机器学习和遥感技术的街景图像自动化计量方法将在城市规划建设、绿道效益评估等方面具有良好应用前景。

[Objective]By reviewing the application of street view imagery on the measurement of green view index(GVI),different methods for data acquisition and calculation were investigated.[Method]This paper compares the traditional method with the automation approach using the algorithm model(e.g.,PSPNet or SegNet),and explained the shortcomings of the traditional method,such as low efficiency,high loss and low accuracy,which can be effectively solved by the automation method.[Result]Through the semantic segmentation of convolution neural network model,the processing and analysis of street scenery pictures become simpler and efficient,though the new method still needs to be improved.[Conclusion]It is pointed out that using machine learning to process data is a new trend of future research and development;however,sufficient sample size and new algorithm are critical for robust GVI calculation.Finally,the paper proposes the combination of street view image with machine learning and remote sensing,and prospects practical application of the mentioned technologies in many fields such as urban planning and construction,and greenway benefit evaluation.