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用于摄影测量洪水预防的近实时变化检测工具。

Near real-time change detection tool for photogrammetric flood preparedness.

作者信息

Kögel Michael, Carstensen Dirk

机构信息

Technische Hochschule Nürnberg Georg Simon Ohm, Institute of Hydraulic Engineering and Water Resources Management, Nuremberg, Germany.

出版信息

Environ Monit Assess. 2025 Jan 4;197(2):129. doi: 10.1007/s10661-024-13597-9.

DOI:10.1007/s10661-024-13597-9
PMID:39753910
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11698855/
Abstract

Through the mobilization of movable objects due to the extreme hydraulic conditions during a flood event, blockages, damage to infrastructure, and endangerment of human lives can occur. To identify potential hazards from aerial imagery and take appropriate precautions, a change detection tool (CDT) was developed and tested using a study area along the Aisch River in Germany. The focus of the CDT development was on near real-time analysis of point cloud data generated by structure from motion from aerial images of temporally separated surveys, enabling rapid and targeted implementation of measures. The differences identified in the study area using distance comparison (M3C2) were segmented into individual components and categorized. Subsequently, the data was compared to existing two-dimensional hydrodynamic numerical calculation results (HQ). The implementation of the CDT is feasible for a variety of RGB camera-equipped aerial vehicles due to the point cloud-based analysis and postprocessing. By overlaying and visualizing the detected changes with numerical simulation results, a quick assessment of the hazard potential in the event of a possible flood can be made. In the case of the study area along the Aisch River, the localization of construction materials, a steel container with debris pile, and a motor vehicle in the flood hazard zone of a potential HQ event could be confirmed, although no mobilization of the materials was to be expected due to the expected hydraulic conditions of a flood event.

摘要

在洪水事件中,由于极端水力条件导致可移动物体移动,可能会出现堵塞、基础设施损坏以及危及生命的情况。为了从航空影像中识别潜在危险并采取适当预防措施,开发并测试了一种变化检测工具(CDT),该工具使用德国艾施河沿岸的一个研究区域进行测试。CDT开发的重点是对通过对时间上分离的航空影像进行运动结构分析生成的点云数据进行近实时分析,从而能够快速且有针对性地实施措施。在研究区域中使用距离比较(M3C2)识别出的差异被分割成各个部分并进行分类。随后,将这些数据与现有的二维水动力数值计算结果(HQ)进行比较。由于基于点云的分析和后处理,CDT对于各种配备RGB相机的无人机都是可行的。通过将检测到的变化与数值模拟结果叠加并可视化,可以对可能发生洪水时的潜在危险进行快速评估。对于艾施河沿岸的研究区域,尽管由于洪水事件预期的水力条件预计不会出现材料移动,但在潜在HQ事件的洪水危险区域中,可以确认建筑材料、一个装有残骸堆的钢制集装箱和一辆机动车辆的位置。

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Plants (Basel). 2024 Aug 29;13(17):2417. doi: 10.3390/plants13172417.
2
Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data.基于无人机数据的深度卷积神经网络洪水淹没范围制图
Sensors (Basel). 2019 Mar 27;19(7):1486. doi: 10.3390/s19071486.
3
Fully Convolutional Networks for Semantic Segmentation.全卷积网络用于语义分割。
IEEE Trans Pattern Anal Mach Intell. 2017 Apr;39(4):640-651. doi: 10.1109/TPAMI.2016.2572683. Epub 2016 May 24.