In the monsoon season, sudden flood events occur frequently in urban areas, which hamper the social and economic activities and may threaten the infrastructure and lives. The use of an efficient large-scale waterlogging sensing and information system can provide valuable real-time disaster information to facilitate disaster management and enhance awareness of the general public to alleviate losses during and after flood disasters. Therefore, in this study, a visual sensing approach driven by deep neural networks and information and communication technology was developed to provide an end-to-end mechanism to realize waterlogging sensing and event-location mapping. The use of a deep sensing system in the monsoon season in Taiwan was demonstrated, and waterlogging events were predicted on the island-wide scale. The system could sense approximately 2379 vision sources through an internet of video things framework and transmit the event-location information in 5 min. The proposed approach can sense waterlogging events at a national scale and provide an efficient and highly scalable alternative to conventional waterlogging sensing methods.

翻译：在季风季节,突然的洪涝事件经常发生在城市地区,阻碍了社会和经济活动,并可能威胁到基础设施和生命;使用高效的大型水博客遥感和信息系统可以提供宝贵的实时灾害信息,促进灾害管理,提高公众的认识,以减轻水灾期间和灾后的损失;因此,在这项研究中,开发了由深层神经网络及信息和通信技术驱动的视觉遥感方法,以提供实现水博客感测和事件定位绘图的端至端机制;展示了台湾季风季节使用深海遥感系统的情况,并预测了全岛范围的水隆事件;该系统可以通过视频事物的互联网框架感知大约2379个视觉源,并在5分钟内传输事件定位信息;拟议方法可以感知全国范围的水涝事件,并为传统水博测方法提供高效和高度可扩展的替代方法。