Deep Sensing of Urban Waterlogging
| Autor: | Jo-Yu Chang, Fang-Pang Lin, Shi-Wei Lo, Jyh-Horng Wu, Meng-Wei Lin, Chien-Hao Tseng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
General Computer Science Computer science Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition urban flood Deep neural network Computer Science - Computers and Society visual sensing Computers and Society (cs.CY) Information system General Materials Science urban waterlogging Emergency management Flood myth business.industry Deep learning internet of video things Environmental resource management General Engineering TK1-9971 Artificial Intelligence (cs.AI) Information and Communications Technology Scalability The Internet Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business Waterlogging (agriculture) |
| Zdroj: | IEEE Access, Vol 9, Pp 127185-127203 (2021) |
| Popis: | 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. 19 pages, 14 figures, under submitting and patenting |
| Databáze: | OpenAIRE |
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