Flood events can cause extensive damage to physical infrastructure, pose risks to human life, and necessitate the reoccupation and rehabilitation of affected areas. A key parameter for flood vulnerability assessment is the first floor height (FFH), which also plays an important role in setting insurance premiums. Traditional methods for FFH estimation rely on ground surveys and site inspections, yet these approaches are both time‐consuming and labor‐intensive. In this study, we propose an alternative approach based on measurements derived from Google Street View (GSV) images and Deep Learning (DL). We employ the YOLOv5s algorithm, which belongs to a family of compound‐scaled object detection models trained on the COCO dataset, for the detection of crucial building elements such as the Front Door (FD), stairs, and overall building extent. Additionally, we utilized the YOLOv5s algorithm to identify basement windows and assess the existence of basements. To validate our methodology, we conducted tests in both the Greater Toronto Area (GTA) and the state of Virginia in the United States. The results demonstrate an achievement of RMSE and Bias values of 81 cm and −50 cm for GTA, and 95 cm and −20 cm for the Virginia region, respectively.

中文翻译：

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使用深度学习和 Google 街景自动估计一层高度以进行洪水脆弱性分析

洪水事件可能对有形基础设施造成广泛破坏，给人类生命带来风险，并需要重新占领和恢复受影响地区。洪水脆弱性评估的一个关键参数是首层高度（FFH），它在确定保险费方面也起着重要作用。传统的 FFH 估算方法依赖于地面调查和现场检查，但这些方法既费时又费力。在本研究中，我们提出了一种基于 Google 街景 (GSV) 图像和深度学习 (DL) 测量值的替代方法。我们采用 YOLOv5s 算法，该算法属于在 COCO 数据集上训练的复合尺度对象检测模型系列，用于检测关键建筑元素，例如前门 (FD)、楼梯和整体建筑范围。此外，我们还利用 YOLOv5s 算法来识别地下室窗户并评估地下室是否存在。为了验证我们的方法，我们在大多伦多地区 (GTA) 和美国弗吉尼亚州进行了测试。结果表明，GTA 的 RMSE 和偏差值分别为 81 cm 和 -50 cm，弗吉尼亚地区的 RMSE 和偏差值分别为 95 cm 和 -20 cm。