Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation
Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation
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DOI：
10.1080/13658816.2021.1981334
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发表时间：
2021-10
影响因子：
5.7
通讯作者：
H. Ning;Zhenlong Li;Xinyue Ye;Shaohua Wang;Wenbo Wang;Xiao Huang
中科院分区：
文献类型：
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作者：
H. Ning;Zhenlong Li;Xinyue Ye;Shaohua Wang;Wenbo Wang;Xiao Huang
谷歌街景等抽象街景图像广泛应用于人们的日常生活中。为了城市建成环境分析，人们已经进行了许多研究，以检测和绘制交通标志和人行道等对象。虽然在水平维度上绘制对象在这些研究中很常见，但大范围的自动垂直测量尚未得到充分开发。街景图像中的垂直信息有助于各种研究。一个值得注意的应用是估计最低楼层高程，这对于建筑物洪水脆弱性评估和保险费计算至关重要。本文利用粘滞测量原理，对街景影像的垂直测量进行了探讨。在使用Google街景图像估计最低楼层高程的案例中，我们训练了一个用于门检测的神经网络(YOLO-v5)，并使用固定的门高度来测量门的高程。结果表明，高程估计的平均误差为0.218 m。利用Google Street View的深度图遍历从路面到目标对象的高程。拟议中的管道提供了一种从街景图像自动估计高程的新方法，预计将有助于未来大范围与地形相关的研究。
ABSTRACT Street view imagery such as Google Street View is widely used in people’s daily lives. Many studies have been conducted to detect and map objects such as traffic signs and sidewalks for urban built-up environment analysis. While mapping objects in the horizontal dimension is common in those studies, automatic vertical measuring in large areas is underexploited. Vertical information from street view imagery can benefit a variety of studies. One notable application is estimating the lowest floor elevation, which is critical for building flood vulnerability assessment and insurance premium calculation. In this article, we explored the vertical measurement in street view imagery using the principle of tacheometric surveying. In the case study of lowest floor elevation estimation using Google Street View images, we trained a neural network (YOLO-v5) for door detection and used the fixed height of doors to measure doors’ elevation. The results suggest that the average error of estimated elevation is 0.218 m. The depthmaps of Google Street View were utilized to traverse the elevation from the roadway surface to target objects. The proposed pipeline provides a novel approach for automatic elevation estimation from street view imagery and is expected to benefit future terrain-related studies for large areas.