Abstract
Flooding in urban areas is one of the serious problems and needs special attention in urban development and improving people’s living quality. Flood detection to promptly provide data for hydrometeorological forecasting systems will help make timely forecasts for life. In addition, providing information about rain and flooding in many locations in the city will help people make appropriate decisions about traffic. Therefore, in this paper, we present our FL-Former solution for detecting and classifying rain and inundation levels in urban locations, specifically in Ho Chi Minh City, based on images recorded from cameras using Vision Transformer. We also build the HCMC-URF dataset with more than 10 K images of various rainy and flooding conditions in Ho Chi Minh City to serve the community’s research. Finally, we propose the software architecture and construction of an online API system to provide timely information about rain and flooding at several locations in the city as extra input for hydrometeorological analysis and prediction systems, as well as provide information to citizens via mobile or web applications.
Q.-C. Le and M.-Q. Le—Equal contribution.
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References
Anusha, N., Bharathi, B.: Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data. Egypt. J. Remote Sens. Space Sci. 23(2), 207–219 (2020)
Bonafilia, D., Tellman, B., Anderson, T., Issenberg, E.: Sen1floods11: a georeferenced dataset to train and test deep learning flood algorithms for sentinel-1. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 835–845 (2020)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3–7 May 2021. OpenReview.net (2021)
Giannakeris, P., Avgerinakis, K., Karakostas, A., Vrochidis, S., Kompatsiaris, I.: People and vehicles in danger - a fire and flood detection system in social media. In: 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pp. 1–5 (2018)
Kim, G., Kwon, T., Ye, J.C.: DiffusionCLIP: text-guided diffusion models for robust image manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2426–2435 (2022)
Li, M., et al.: Clip-event: Connecting text and images with event structures. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16420–16429 (June 2022)
Li, S., et al.: Automatic near real-time flood detection using Suomi-NPP/VIIRS data. Remote Sens. Environ. 204, 672–689 (2018)
Li, Y., Martinis, S., Wieland, M.: Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence. ISPRS J. Photogramm. Remote. Sens. 152, 178–191 (2019)
Mangalam, K., et al.: Reversible vision transformers. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10820–10830 (2022)
Materzyńska, J., Torralba, A., Bau, D.: Disentangling visual and written concepts in clip. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16410–16419 (2022)
Munawar, H.S., Ullah, F., Qayyum, S., Khan, S.I., Mojtahedi, M.: UAVs in disaster management: application of integrated aerial imagery and convolutional neural network for flood detection. Sustainability 13(14), 7547 (2021)
Notti, D., Giordan, D., Caló, F., Pepe, A., Zucca, F., Galve, J.P.: Potential and limitations of open satellite data for flood mapping. Remote Sens. 10(11), 1673 (2018)
Oddo, P.C., Ahamed, A., Bolten, J.D.: Socioeconomic impact evaluation for near real-time flood detection in the lower Mekong river basin. Hydrology 5(2), 23 (2018)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18–24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 8748–8763. PMLR (2021)
Sanghi, A., et al.: Clip-forge: towards zero-shot text-to-shape generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18603–18613 (2022)
Shahabi, H., et al.: Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sens. 12(2), 266 (2020)
Shen, X., Wang, D., Mao, K., Anagnostou, E., Hong, Y.: Inundation extent mapping by synthetic aperture radar: a review. Remote Sens. 11(7), 879 (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Acknowledgements
This work is supported by the Department of Science and Technology, Ho Chi Minh City, in the project 97/2020/HD-QPTKHCN.
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Le, QC., Le, MQ., Tran, MK., Le, NQ., Tran, MT. (2023). FL-Former: Flood Level Estimation with Vision Transformer for Images from Cameras in Urban Areas. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham. https://doi.org/10.1007/978-3-031-27077-2_35
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DOI: https://doi.org/10.1007/978-3-031-27077-2_35
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