Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in urban areas using traditional methods such as remote sensing and digital elevation models (DEMs) yields large errors due to reshaped surface topography and microtopographic variations combined with vegetation bias. This paper presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads and intersections, coupled with Canny edge detection and probabilistic Hough transform to calculate pole length and estimate floodwater depth. Additionally, a tilt correction technique is implemented to address the problem of sideways tilt in visual analysis of submerged stop signs. An in-house dataset, named BluPix 2020.1 consisting of paired web-mined photos of submerged stop signs across 10 FEMA regions (for U.S. locations) and Canada is used to evaluate the models. Overall, pole length is estimated with an RMSE of 17.43 and 8.61 in. in pre- and post-flood photos, respectively, leading to a mean absolute error of 12.63 in. in floodwater depth estimation. Findings of this research are sought to equip jurisdictions, local governments, and citizens in flood-prone regions with a simple, reliable, and scalable solution that can provide (near-) real time estimation of floodwater depth in their surroundings.

全世界许多地方每年都会遭受严重的洪灾。除了减轻和破坏财产的高昂费用外，洪水使道路无法通行，并阻碍了社区疏散，货物和服务的运输以及救援任务。知道洪水的深度对于随后的响应和恢复行动的成功至关重要。但是，洪水地图尤其是在城市地区使用遥感和数字高程模型（DEM）等传统方法进行绘制，由于曲面形状的重新塑形和微观地形的变化以及植被的偏差，会产生较大的误差。本文提出了一种深度神经网络方法，用于检测从淹没的道路和交叉路口拍摄的照片中的淹没停车标志，结合Canny边缘检测和概率Hough变换来计算极点长度并估算洪水深度。另外，实施倾斜校正技术以解决在淹没停车标志的视觉分析中的侧向倾斜的问题。内部数据集名为BluPix 2020.1，它由横跨10个FEMA地区（适用于美国）和加拿大的水下停车标志的成对网络开采照片组成，用于评估模型。总体而言，在洪水前和洪水后的照片中，杆长的估计均方根误差（RMSE）为17.43英寸和8.61英寸，导致洪水深度估算中的平均绝对误差为12.63英寸。寻求本研究的结果，以便为易受洪灾地区的辖区，地方政府和公民提供简单，可靠，