Computer vision based first floor elevation estimation from mobile LiDAR data
Computer vision based first floor elevation estimation from mobile LiDAR data
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DOI：
10.1016/j.autcon.2023.105258
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发表时间：
2024-03
影响因子：
10.3
通讯作者：
Jiahao Xia;Jie Gong
中科院分区：
文献类型：
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作者：
Jiahao Xia;Jie Gong
房屋的第一层高程(FFE)是洪水管理和准确评估房产洪水暴露风险的关键信息。然而，缺乏大量地理范围内可靠的FFE数据，大大限制了减轻洪水风险的努力，例如决定提高房产的高度。收集房屋高程数据的传统方法依赖于由有执照的测量师或工程师进行耗时和劳动密集型的现场检查。本文提出了一种从移动LiDAR点云数据中自动、可扩展地提取FFE的方法。使用微调的yolov5模型在基于强度的点云投影上检测门、窗和车库门，获得了0.689的地图@0.5：0.95。随后，使用检测到的对象来估计FFE。我们评估了曼维尔、文特纳和朗波特估计的FFE的中位数绝对误差(MAE)指标，结果分别为0.2英尺、0.27英尺和0.24英尺。FFE数据的可获得性可能为设定洪水保险费和促进针对洪水风险较高的住宅建筑的买断计划的收益-成本分析提供有价值的指导。
First Floor Elevation (FFE) of a house is crucial information for flood management and for accurately assessing the flood exposure risk of a property. However, the lack of reliable FFE data on a large geographic scale significantly limits efforts to mitigate flood risk, such as decision on elevating a property. The traditional method of collecting elevation data of a house relies on time-consuming and labor-intensive on-site inspections conducted by licensed surveyors or engineers. In this paper, we propose an automated and scalable method for extracting FFE from mobile LiDAR point cloud data. The fine-tuned yolov5 model is employed to detect doors, windows, and garage doors on the intensity-based projection of the point cloud, achieving an mAP@0.5:0.95 of 0.689. Subsequently, FFE is estimated using detected objects. We evaluated the Median Absolute Error (MAE) metric for the estimated FFE in Manville, Ventnor, and Longport, which resulted in values of 0.2 ft, 0.27 ft, and 0.24 ft, respectively. The availability of FFE data has the potential to provide valuable guidance for setting flood insurance premiums and facilitating benefit-cost analyses of buyout programs targeting residential buildings with a high flood risk.