  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.
