Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3525-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
23 Sep 2025
Research article |
| 23 Sep 2025
| 23 Sep 2025
Automated urban flood level detection based on flooded bus dataset using YOLOv8
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Cited articles
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Baranowski, D. B., Flatau, M. K., Flatau, P. J., Karnawati, D., Barabasz, K., Labuz, M., Latos, B., Schmidt, J. M., Paski, J. A. I., and Marzuki, M.: Social-media and newspaper reports reveal large-scale meteorological drivers of floods on Sumatra, Nat. Commun., 11, 2503, https://doi.org/10.1038/s41467-020-16171-2, 2020.
Betterle, A. and Salamon, P.: Water depth estimate and flood extent enhancement for satellite-based inundation maps, Nat. Hazards Earth Syst. Sci., 24, 2817–2836, https://doi.org/10.5194/nhess-24-2817-2024, 2024.
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. Hydroinformatics, 21, 240–256, https://doi.org/10.2166/hydro.2018.044, 2018.
Cai, Y., Bian, H., Lin, J., Wang, H., Timofte, R., and Zhang, Y.: Retinexformer: One-stage retinex-based transformer for low-light image enhancement, in: Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 1–6 October 2023, 12470–12479, https://doi.org/10.1109/ICCV51070.2023.01149, 2023.
