AI-supported citizen science to monitor high-tide flooding in Newport Beach, California
AI-supported citizen science to monitor high-tide flooding in Newport Beach, California
复制标题
人工智能支持公民科学监测加利福尼亚州纽波特海滩的涨潮洪水
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Ruoqian Wang
中科院分区：
文献类型：
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作者：
Behzad Golparvar;Ruoqian Wang
监测高潮洪水（HTF）具有挑战性，因为HTF通常广泛传播，并根据自然过程和基础设施形成局部积水。固定监测系统和卫星成像有其某些局限性。到目前为止，市民科学被认为是最有前途的监测方法，它提供了广泛和持续的社区覆盖面和实时的洪水事件的第一手证据。在这里，我们提出了一个灵活的人工智能（AI）支持的公民科学平台，用于HTF监测。洪水范围是通过标准的摄影测量算法和称为monotapping的计算机视觉技术确定的，水深可以使用参考对象进行估计。在本文中，monoplotting建立照片和相应的数字高程模型（DEM）数据之间的相关性，允许映射的洪水范围和水深的DEM地图，以尽量减少数据的不确定性，提高数据的可信度，分辨率和整体价值。
Monitoring High-tide Flooding (HTF) is challenging because HTF usually spreads widely and forms localized water accumulations depending on the natural processes and infrastructure. Stationary monitoring systems and satellite imaging have their certain limitations. To date, citizen science is considered as the most promising means to monitor HTF, which provides wide and continuous coverage of the community and real-time first-hand witness of the flooding event. Here, we present a flexible Artificial Intelligence (AI) -supported citizen science platform for HTF monitoring. Flood extent is identified through standard photogrammetry algorithms and a Computer vision technique called monoplotting, and water depth can be estimated using reference objects. In this paper, monoplotting is employed to establish a correlation between photos and the corresponding digital elevation model (DEM) data, allowing to map the flood extent and water depth to the DEM map to minimize the data uncertainty and enhance the data credibility, resolution, and overall value.
影响因子：
3.7
作者：
Neumann B;Vafeidis AT;Zimmermann J;Nicholls RJ
通讯作者：
Nicholls RJ