SMART Infrastructure Facility, University of Wollongong, Wollongong 2522, NSW, Australia.
The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, NSW, Australia.
Sensors (Basel). 2019 Nov 16;19(22):5012. doi: 10.3390/s19225012.
Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons-an aspect that is under-explored in the literature.
洪水是最常见和最具破坏性的自然灾害之一。每年在全球范围内,与洪水相关的死亡人数和经济损失数量令人震惊,这需要我们提高对洪水风险的应对能力。有趣的是,过去十年提供了很好的机会,一系列学术活动探索了如何利用物联网 (IoT) 网络中的摄像机图像和无线传感器数据来改进洪水管理。本文对基于物联网的传感器和计算机视觉在洪水监测和制图中的应用进行了系统的文献回顾。本文的主要贡献在于强调了文献中用于实时洪水监测、洪水建模、制图和预警系统(包括水位估计)的主要计算机视觉技术和物联网传感器方法。本文进一步通过提供未来研究的建议做出了贡献。特别是,该研究建议如何利用计算机视觉和物联网传感器技术来更好地监测和管理沿海泻湖——这是文献中探讨较少的一个方面。