Articles | Volume 23, issue 11
Hydrol. Earth Syst. Sci., 23, 4621–4634, 2019
https://doi.org/10.5194/hess-23-4621-2019
Hydrol. Earth Syst. Sci., 23, 4621–4634, 2019
https://doi.org/10.5194/hess-23-4621-2019

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 et al.

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Cited articles

Bhola, P. K., Nair, B. B., Leandro, J., Rao, S. N., and Disse, M.: Flood inundation forecasts using validation data generated with the assistance of computer vision, J. Hydroinform., 21, 240–256, https://doi.org/10.2166/hydro.2018.044, 2018. 
Blanchard, E.: Hurricane Harvey Flooding in Houston, available at: https://www.youtube.com/watch?v=__IBuu06URY (last access: 8 October 2018), 2017. 
Chaudhary, P.: Floodwater-estimation through semantic image interpretation, Technical University Munich, Munich, Germany, 2018. 
Chaudhary, P., Aronco, S. D., Moy de Vitry, M., Leitao, J. P., and Wegner, J. D.: Flood-Water Level Estimation from Social Media Images, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 5–12, 2019. 
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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.