Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 11
Articles | Volume 25, issue 11
https://doi.org/10.5194/nhess-25-4361-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-4361-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 11
Research article
 | 
05 Nov 2025
Research article |  | 05 Nov 2025

Identification of nighttime urban flood inundation extent using deep learning

Jiaquan Wan, Xing Wang, Yannian Cheng, Cuiyan Zhang, Fengchang Xue, Tao Yang, Fei Tong, and Quan J. Wang

Viewed

Total article views: 3,103 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,013 978 112 3,103 88 125
  • HTML: 2,013
  • PDF: 978
  • XML: 112
  • Total: 3,103
  • BibTeX: 88
  • EndNote: 125
Views and downloads (calculated since 28 Jan 2025)
Cumulative views and downloads (calculated since 28 Jan 2025)

Viewed (geographical distribution)

Total article views: 3,103 (including HTML, PDF, and XML) Thereof 3,011 with geography defined and 92 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 02 May 2026
Download
Short summary
Urban flooding is a growing issue in cities, often disrupting daily life, especially at night, when the extent of flooding is harder to identify. This study introduces NWseg, a new deep learning model designed to identify the extent of urban flooding at night. Using a dataset of 4000 nighttime images, we found that NWseg outperforms existing models in accuracy. This research offers a practical solution for real-time flood monitoring, helping improve urban disaster response and management.
Read more
Share
Altmetrics
Final-revised paper
Preprint