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首页> 外文期刊>Building and Environment >Classification and mapping of urban canyon geometry using Google Street View images and deep multitask learning
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Classification and mapping of urban canyon geometry using Google Street View images and deep multitask learning

机译：使用Google街景图像和深度多任务学习对城市峡谷的几何形状进行分类和映射

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摘要

Urban canyon classification plays an important role in analyzing the impact of urban canyon geometry on urban morphology and microelimates. Existing classification methods using aspect ratios require a large number of field surveys, which are often expensive and laborious. Moreover, it is difficult for these methods to handle the complex geometry of street canyons, which is often required by specific applications. To overcome these difficulties, we develop a street canyon classification approach using publicly available Google Street View (GSV) images. Our method is inspired by the latest advances in deep multitask learning based on densely connected convolutional networks (DenseNets) and tailored for multiple street canyon classification, i.e., H/W-based (Level 1), symmetry-based (Level 2), and complex-geometry-based (Level 3) classifications. We conducted a series of experiments to verify the proposed method. First, taking the Hong Kong area as an example, the method achieved an accuracy of 89.3%, 86.6%, and 86.1%, respectively for the three levels. Even using the field survey data as the ground truth, it gained approximately 80% for different levels. Then, we tested our pretrained model in five other cities and compared the results with traditional methods. The transferability and effectiveness of the scheme were demonstrated. Finally, to enrich the representation of more complicated street geometry, the approach can separately generate thematic maps of street canyons at multiple levels to better facilitate microclimatic studies in high-density built environments. The developed techniques for the classification and mapping of street canyons provide a cost-effective tool for studying the impact of complex and evolving urban canyon geometry on microclimate changes.
机译：城市峡谷分类在分析城市峡谷几何形状对城市形态和微消隐的影响中起着重要作用。现有的使用纵横比的分类方法需要大量的现场调查，这通常是昂贵且费力的。此外，这些方法很难处理复杂的街道峡谷几何形状，而这正是特定应用程序经常需要的。为了克服这些困难，我们开发了使用公开可用的Google Street View（GSV）图像的街道峡谷分类方法。我们的方法受到基于密集连接的卷积网络（DenseNets）的深度多任务学习的最新进展的启发，并针对多条街道峡谷分类进行了量身定制，即基于H / W（级别1），基于对称性（级别2）和基于复杂几何的（第3级）分类。我们进行了一系列实验以验证所提出的方法。首先，以香港地区为例，该方法在三个级别上的准确度分别为89.3％，86.6％和86.1％。即使使用现场调查数据作为基本事实，不同级别的数据也可以获取约80％的收益。然后，我们在其他五个城市测试了我们的预训练模型，并将结果与​​传统方法进行了比较。证明了该方案的可转让性和有效性。最后，为了丰富更复杂的街道几何图形的表示，该方法可以分别生成多层街道峡谷的专题图，以更好地促进在高密度建筑环境中的微气候研究。发达的街道峡谷分类和制图技术为研究复杂且不断发展的城市峡谷几何形状对微气候变化的影响提供了一种经济高效的工具。

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