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Automated flood extent identification using WorldView imagery for the insurance industry

机译：使用WorldView影像自动识别保险行业的洪水范围

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Flooding is the most common and costly natural disaster around the world, causing the loss of human life and billions in economic and insured losses each year. In 2016, pluvial and fluvial floods caused an estimated 5.69 billion USD in losses worldwide with the most severe events occurring in Germany, France, China, and the United States. While catastrophe modeling has begun to help bridge the knowledge gap about the risk of fluvial flooding, understanding the extent of a flood - pluvial and fluvial - in near real-time allows insurance companies around the world to quantify the loss of property that their clients face during a flooding event and proactively respond. To develop this real-time, global analysis of flooded areas and the associated losses, a new methodology utilizing optical multi-spectral imagery from DigitalGlobe (DGI) WorldView satellite suite is proposed for the extraction of pluvial and fluvial flood extents. This methodology involves identifying flooded areas visible to the sensor, filling in the gaps left by the built environment (i.e. buildings, trees) with a nearest neighbor calculation, and comparing the footprint against an Industry Exposure Database (IE) to calculate a loss estimate. Full-automation of the methodology allows production of flood extents and associated losses anywhere around the world as required. The methodology has been tested and proven effective for the 2016 flood in Louisiana, USA.
机译：洪水是世界上最常见，代价最高的自然灾害，每年造成人员伤亡，并造成数十亿的经济和保险损失。 2016年，暴雨和河流洪水在全球造成约56.9亿美元的损失，其中最严重的事件发生在德国，法国，中国和美国。尽管巨灾建模已开始帮助弥合有关洪水泛滥风险的知识鸿沟，但近乎实时地了解洪水泛滥的程度（暴雨和暴雨），使全世界的保险公司都能量化其客户所面临的财产损失在发生洪水事件时做出积极反应。为了开发这种对洪水区域及其相关损失的实时，全局分析，提出了一种利用来自DigitalGlobe（DGI）WorldView卫星套件的光学多光谱图像的新方法，以提取河流和河流的洪水范围。这种方法包括识别传感器可见的淹没区域，用最近的邻居计算来填充建筑环境（即建筑物，树木）留下的空隙，并将足迹与行业暴露数据库（IE）进行比较以计算损失估算值。该方法的完全自动化可根据需要在世界各地生产洪水范围和相关损失。该方法论已经过测试，并证明对2016年美国路易斯安那州的洪水有效。

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