Flood Depth Estimation from Web Images
Flood Depth Estimation from Web Images
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DOI：
10.1145/3356395.3365542
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Zonglin Meng;Bo Peng;Qunying Huang
中科院分区：
文献类型：
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作者：
Zonglin Meng;Bo Peng;Qunying Huang
自然灾害对我们的城市造成了严重的破坏，洪水是美国和世界上最具灾难性的灾害之一。因此，至关重要的是，制定有效的方法，自然灾害后的风险和损害评估，如洪水深度估计。现有的工作主要利用照片和图像捕捉洪水场景，使用传统的计算机视觉和机器学习技术来估计洪水深度。然而，深度学习（DL）方法的进步使得更准确地估计洪水深度成为可能。因此，基于现有技术的DL技术（即，Mask R-CNN）和互联网上公开的图像，本研究旨在调查和改进洪水深度估计。具体而言，人体对象被检测和分割从洪水图像推断洪水的深度。这项研究提供了一个新的框架，从大量可访问的在线数据中提取关键信息，供救援队甚至机器人在城市地区执行救灾和救援任务的适当计划，为洪水深度的实时检测提供了帮助。
Natural hazards have been resulting in severe damage to our cities, and flooding is one of the most disastrous in the U.S and worldwide. Therefore, it is critical to develop efficient methods for risk and damage assessments after natural hazards, such as flood depth estimation. Existing works primarily leverage photos and images capturing flood scenes to estimate flood depth using traditional computer vision and machine learning techniques. However, the advancement of deep learning (DL) methods make it possible to estimate flood depth more accurate. Therefore, based on state-of-the-art DL technique (i.e., Mask R-CNN) and publicly available images from the Internet, this study aims to investigate and improve the flood depth estimation. Specifically, human objects are detected and segmented from flooded images to infer the floodwater depth. This study provides a new framework to extract critical information from large accessible online data for rescue teams or even robots to carry out appropriate plans for disaster relief and rescue missions in the urban area, shedding lights on the real-time detection of the flood depth.