Abstract Characterizing urban hydrographs during rain storms, hurricanes, and river floods is important to decrease loss of lives and assist emergency responders with mapping disruptions to operation of major cities. High water marks, stream gages, and rapidly deployed instrumentation are the current state-of-practice for hydrological data during a flood event. The objective of this study was to develop technology that can provide accurate and timely flood hydrographs while harnessing the Big Data generated from videos and images. In particular, levels are predicted from images by using reference objects as a scale. The novelty of this work involved leveraging object-based image analysis (OBIA), which used image segmentation training algorithms to differentiate areas of images or videos. In particular, the deep learning-based semantic segmentation technique was trained using images from an MIT database along with images compiled from traffic cameras and the experiments and a case study. The fully convolutional network was used for image segmentation and subsequent object labeling. This algorithm was applied to a laboratory and two field experiments before demonstration at Buffalo Bayou in Houston, TX during Hurricane Harvey. The laboratory and field experiments indicated that the image segmentation technique was reproducible and accurate from a controlled environment to rain storms and localized flooding in small streams on the LSU campus. Moreover, the segmentation algorithm successfully estimated flood levels in Buffalo Bayou in downtown Houston, Texas during Hurricane Harvey. This signifies that if time-lapse imagery is available, this algorithm- and program-estimated water elevations can provide insight to the hydrograph and spatial inundation during flooding from rainstorms or hurricanes.

中文翻译：

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使用实时摄像头和计算机视觉技术进行实时水位监测

摘要 在暴雨、飓风和河流洪水期间表征城市水文过程线对于减少生命损失和协助应急响应人员绘制对主要城市运营中断的地图非常重要。在洪水事件期间，高水位线、流量计和快速部署的仪器是水文数据的当前实践状态。本研究的目的是开发能够在利用视频和图像生成的大数据的同时提供准确及时的洪水过程线的技术。特别是，水平是通过使用参考对象作为尺度从图像中预测的。这项工作的新颖之处在于利用基于对象的图像分析 (OBIA)，该分析使用图像分割训练算法来区分图像或视频的区域。特别是，基于深度学习的语义分割技术使用来自 MIT 数据库的图像以及从交通摄像头编译的图像以及实验和案例研究进行训练。全卷积网络用于图像分割和后续的对象标记。在哈维飓风期间，在德克萨斯州休斯顿的布法罗河口进行演示之前，该算法已应用于实验室和两次现场实验。实验室和现场实验表明，从受控环境到暴雨和路易斯安那州立大学校园小溪流中的局部洪水，图像分割技术是可重复和准确的。此外，分割算法成功地估计了飓风哈维期间德克萨斯州休斯顿市中心布法罗河口的洪水水位。这意味着如果延时图像可用，