论文标题
基于众包的深层卷积网络，用于城市洪水深度映射
Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth Mapping
论文作者
论文摘要
成功的洪水恢复和疏散需要获取可靠的洪水深度信息。大多数现有的洪水地图工具都不提供居民区及其周围地区淹没街道的实时洪水图。在本文中，通过分析淹没的交通标志的众包图像，使用深层卷积网络来确定具有高空间分辨率的洪水深度。在美国和加拿大最近的洪水中测试该模型的模型产生的平均绝对误差为6.978英寸，这与以前的研究相当，因此证明了这种方法对低成本，准确和实时的洪水风险映射的适用性。
Successful flood recovery and evacuation require access to reliable flood depth information. Most existing flood mapping tools do not provide real-time flood maps of inundated streets in and around residential areas. In this paper, a deep convolutional network is used to determine flood depth with high spatial resolution by analyzing crowdsourced images of submerged traffic signs. Testing the model on photos from a recent flood in the U.S. and Canada yields a mean absolute error of 6.978 in., which is on par with previous studies, thus demonstrating the applicability of this approach to low-cost, accurate, and real-time flood risk mapping.
