Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 23, issue 11
Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4621-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-4621-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 11
Research article
 | 
15 Nov 2019
Research article |  | 15 Nov 2019

Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

Matthew Moy de Vitry, Simon Kramer, Jan Dirk Wegner, and João P. Leitão

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Short summary
This work demonstrates a new approach to obtain flood level trend information from surveillance footage with minimal prior information. A neural network trained to detect flood water is applied to video frames to create a qualitative flooding metric (namely, SOFI). The correlation between the real water trend and SOFI was found to be 75 % on average (based on six videos of flooding under various circumstances). SOFI could be used for flood model calibration, to increase model reliability.
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