Articles | Volume 25, issue 8
https://doi.org/10.5194/hess-25-4435-2021
https://doi.org/10.5194/hess-25-4435-2021
Research article
 | 
16 Aug 2021
Research article |  | 16 Aug 2021

Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning

Remy Vandaele, Sarah L. Dance, and Varun Ojha

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Revised manuscript under review for HESS
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Cited articles

Bargoti, S. and Underwood, J. P.: Image segmentation for fruit detection and yield estimation in apple orchards, J. Field Robot., 34, 1039–1060, https://doi.org/10.1002/rob.21699, 2017. a
Baruch, A.: An investigation into the role of crowdsourcing in generating information for flood risk management, PhD thesis, Loughborough University, Loughborough, 2018. a
Caesar, H., Uijlings, J., and Ferrari, V.: Coco-stuff: Thing and stuff classes in context, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1209–1218, https://doi.org/10.1109/CVPR.2018.00132, 2018. a, b
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE T. Pattern Anal., 40, 834–848, https://doi.org/10.1109/TPAMI.2017.2699184, 2017. a, b, c
Civil Aviation Authority: Unmanned aircraft and drones, available at: https://www.caa.co.uk/Consumers/Unmanned-aircraft-and-drones/, last access: 16 November 2020. a
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Short summary
The acquisition of river-level data is a critical task for the understanding of flood events but is often complicated by the difficulty to install and maintain gauges able to provide such information. This study proposes applying deep learning techniques on river-camera images in order to automatically extract the corresponding water levels. This approach could allow for a new flexible way to observe flood events, especially at ungauged locations.