Articles | Volume 27, issue 22
Research article
15 Nov 2023
Research article |  | 15 Nov 2023

Eye of Horus: a vision-based framework for real-time water level measurement

Seyed Mohammad Hassan Erfani, Corinne Smith, Zhenyao Wu, Elyas Asadi Shamsabadi, Farboud Khatami, Austin R. J. Downey, Jasim Imran, and Erfan Goharian


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-857', Remy Vandaele, 13 Jun 2023
    • AC1: 'Reply on RC1', Erfan Goharian, 09 Aug 2023
  • RC2: 'Comment on egusphere-2023-857', Anonymous Referee #2, 21 Jul 2023
    • AC2: 'Reply on RC2', Erfan Goharian, 09 Aug 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (further review by editor) (21 Aug 2023) by Roger Moussa
AR by Erfan Goharian on behalf of the Authors (31 Aug 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (20 Sep 2023) by Roger Moussa
AR by Erfan Goharian on behalf of the Authors (28 Sep 2023)  Manuscript 
Short summary
Predicting flood magnitude and location helps decision-makers to better prepare for flood events. To increase the speed and availability of data during flooding, this study presents a vision-based framework for measuring water levels and detecting floods. The deep learning models use time-lapse images captured by surveillance cameras to detect water extent using semantic segmentation and to transform them into water level values with the help of lidar data.