Deep Sensing of Urban Waterlogging
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 near real-time disaster info...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9535161/ |
| _version_ | 1818724784158015488 |
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| author | Shi-Wei Lo Jyh-Horng Wu Jo-Yu Chang Chien-Hao Tseng Meng-Wei Lin Fang-Pang Lin |
| author_facet | Shi-Wei Lo Jyh-Horng Wu Jo-Yu Chang Chien-Hao Tseng Meng-Wei Lin Fang-Pang Lin |
| author_sort | Shi-Wei Lo |
| collection | DOAJ |
| description | 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 near 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. |
| first_indexed | 2024-12-17T21:31:55Z |
| format | Article |
| id | doaj.art-bfa48fcb32d04499b64e930824bbb04c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T21:31:55Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bfa48fcb32d04499b64e930824bbb04c2022-12-21T21:31:51ZengIEEEIEEE Access2169-35362021-01-01912718512720310.1109/ACCESS.2021.31116239535161Deep Sensing of Urban WaterloggingShi-Wei Lo0https://orcid.org/0000-0003-3449-6780Jyh-Horng Wu1Jo-Yu Chang2Chien-Hao Tseng3Meng-Wei Lin4Fang-Pang Lin5National Center for High-Performance Computing (NCHC), Hsinchu, TaiwanNational Center for High-Performance Computing (NCHC), Hsinchu, TaiwanNational Center for High-Performance Computing (NCHC), Hsinchu, TaiwanNational Center for High-Performance Computing (NCHC), Hsinchu, TaiwanNational Center for High-Performance Computing (NCHC), Hsinchu, TaiwanNational Center for High-Performance Computing (NCHC), Hsinchu, TaiwanIn 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 near 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.https://ieeexplore.ieee.org/document/9535161/Deep neural networkinternet of video thingsurban floodurban waterloggingvisual sensing |
| spellingShingle | Shi-Wei Lo Jyh-Horng Wu Jo-Yu Chang Chien-Hao Tseng Meng-Wei Lin Fang-Pang Lin Deep Sensing of Urban Waterlogging IEEE Access Deep neural network internet of video things urban flood urban waterlogging visual sensing |
| title | Deep Sensing of Urban Waterlogging |
| title_full | Deep Sensing of Urban Waterlogging |
| title_fullStr | Deep Sensing of Urban Waterlogging |
| title_full_unstemmed | Deep Sensing of Urban Waterlogging |
| title_short | Deep Sensing of Urban Waterlogging |
| title_sort | deep sensing of urban waterlogging |
| topic | Deep neural network internet of video things urban flood urban waterlogging visual sensing |
| url | https://ieeexplore.ieee.org/document/9535161/ |
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