Deep Sensing of Urban Waterlogging

TL;DR

This paper presents a deep neural network-based visual sensing system for real-time, large-scale waterlogging detection and event localization in urban areas, demonstrated in Taiwan during monsoon season.

Contribution

It introduces an end-to-end deep sensing approach utilizing vision sources and ICT to detect and map waterlogging events at a national scale, surpassing traditional methods.

Findings

System sensed approximately 2379 vision sources.

Event-location information transmitted within 5 minutes.

Effective large-scale waterlogging detection demonstrated in Taiwan.

Abstract

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.