Flood Depth Assessment with Location-Based Social Network Data and Google Street View - A Case Study with Buildings as Reference Objects
Flood Depth Assessment with Location-Based Social Network Data and Google Street View - A Case Study with Buildings as Reference Objects
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DOI：
10.1109/igarss46834.2022.9884254
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Boyuan Zou;Bo Peng;Qunying Huang
中科院分区：
文献类型：
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作者：
Boyuan Zou;Bo Peng;Qunying Huang
准确的洪灾损失评估，如洪灾深度，对救灾非常有帮助。然而，现有的大多数方法都有一些局限性，要么需要昂贵的数据(例如水文观测)，要么只能用基于位置的社交网络(LBSN)数据产生粗略的评估。为了获得准确和实时的洪灾损失评估，本文首先从LBSNs和Google Street View分别采集了同一位置的洪灾前后图像对。接下来，使用机器学习方法(例如，MASK R-CNN)在图像中分割建筑物。最后，进行校正和建筑物高度提取，通过对比两幅图像，计算出准确的洪水深度值。使用日本和美国洪水灾害产生的真实数据集对该方法进行了评估。实验结果表明，该方法能够准确、实时地进行洪涝灾害评估。
Flood damage accurate assessment, such as flood depth, is very helpful for disaster relief. However, most of existing methods have some limitations, either the expensive data requirement (e.g., hydrological observations), or only producing a rough assessment with location-based social network (LBSN) data. To obtain an accurate and real-time flood damage assessment, this paper firstly collects pairs of pre- and post-flood images for the same location from LBSNs and Google Street View respectively. Next, buildings were segmented in images using machine learning methods (e.g., Mask R-CNN). Finally, the rectifying and building height extraction were developed to calculate the accurate value of the flood depth by comparing the paired images. The method was evaluated by using the real datasets generated from flood disasters in Japan and US. Experiment results show that the proposed method provides an accurate and real-time flood disaster assessment.