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| Created: | Jul 30, 2026 at 6:34 p.m. (UTC) | |
| Last updated: | Jul 30, 2026 at 8:07 p.m. (UTC) | |
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Abstract
This resource contains the datasets and code used in a study of camera-based urban flood water depth estimation. The original surveillance camera video and pressure sensor measurements of water depth were obtained from Zamanizadeh et al. (2025). The resource includes (1) image samples extracted from the original surveillance video, (2) resampled ground-truth water depth measurements derived from pressure sensors, (3) a deep learning-based image segmentation model for flood extent extraction developed by Wang et al. (2024), and (4) code for flood extent segmentation and water-depth estimation using DEM-based and proxy-based (SOFI-based) approaches. The resource is provided to support the reproducibility and transparency of the analyses presented in the submitted manuscript while giving full credit to the original data source described by Wang et al. which is under peer review.
Reference:
Wang, Y., Goodall., J. L., Leal Sobral, V. A., & Tahvildari, N. (Under Review). Proxy-Based Urban Flood Water Depth Estimation from Surveillance Camera Imagery Using the Static Observer Flooding Index. Remote Sensing.
Wang, Y., Shen, Y., Salahshour, B., Cetin, M., Iftekharuddin, K., Tahvildari, N., ... & Goodall, J. L. (2024). Urban flood extent segmentation and evaluation from real-world surveillance camera images using deep convolutional neural network. Environmental Modelling & Software, 173, 105939.
Zamanizadeh, M., Cetin, M., Shahabi, A., & Tahvildari, N. (2025). Depth estimation in urban flooding using surveillance cameras and high-resolution LiDAR data. Environmental Modelling & Software, 192, 106572.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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