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All issues Volume 502 (2024) E3S Web Conf., 502 (2024) 03010 References
Open Access
Issue
E3S Web Conf.
Volume 502, 2024
2nd International Congress on Coastal Research (ICCR 2023)
Article Number 03010
Number of page(s) 6
Section Coastal Hazards and Risk Assessment
DOI https://doi.org/10.1051/e3sconf/202450203010
Published online 11 March 2024
  1. E. M. Sellami, M. Maanan, and H. Rhinane, “Performance of machine learning algorithms for mapping and forecasting of flash flood susceptibility in tetouan, morocco,” in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society forPhotogrammetry and Remote Sensing, Jan. 2022, pp. 305-313. DOI: 10.5194/isprs-archives-XLVI-4-W3-2021-305-2022. [CrossRef] [Google Scholar]
  2. B. Sahoo and D. Maity, “Damage assessment of structures using hybrid neuro-genetic algorithm,” Applied Soft Computing Journal, vol. 7, no. 1, pp. 89-104, Jan. 2007, DOI: 10.1016/j.asoc.2005.04.001. [CrossRef] [Google Scholar]
  3. B. T. Pham et al., “Flood risk assessment using deep learning integrated with multi-criteria decision analysis,” Knowl Based Syst, vol. 219, May 2021, DOI: 10.1016/j.knosys.2021.106899. [Google Scholar]
  4. L. Yang and G. Cervone, “Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event,” Soft comput, vol. 23, no. 24, pp. 13393-13408, Dec. 2019, DOI: 10.1007/s00500-019-03878-8. [CrossRef] [Google Scholar]
  5. K. Bhuyan, C. Van Westen, J. Wang, and S. R. Meena, “Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence,” Natural Hazards, 2022, DOI: 10.1007/s11069-022-05612-4. [Google Scholar]
  6. T. G. J. Rudner et al., “Multi 3 Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery.” Online. Available: www.aaai.org [Google Scholar]
  7. E. Hamidi, B. G. Peter, D. F. Muñoz, H. Moftakhari, and H. Moradkhani, “Fast Flood Extent Monitoring With SAR Change Detection Using Google Earth Engine,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-19, 2023, DOI: 10.1109/TGRS.2023.3240097. [CrossRef] [Google Scholar]
  8. F. Ahwide, A. Spena, and A. El-Kafrawy, “Estimation of Electricity Generation in Libya Using Processing Technology of Wind Available Data: The Case study in Derna,” APCBEE Procedia, vol. 5, pp. 451-467, 2013, DOI: 10.1016/j.apcbee.2013.05.078. [CrossRef] [Google Scholar]
  9. S. Shinde, C. B. Pande, V. N. Barai, S. D. Gorantiwar, and A. A. Atre, “Flood Impact and Damage Assessment Based on the Sentitnel-1 SAR Data Using Google Earth Engine,” in Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems, C. B. Pande, K. N. Moharir, S. K. Singh, Q. B. Pham, and A. Elbeltagi, Eds., Cham: Springer International Publishing, 2023, pp. 483-502. DOI: 10.1007/978-3-031-19059-9_20. [CrossRef] [Google Scholar]
  10. A. C. Pandey, K. Kaushik, and B. R. Parida, “Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga- Brahmaputra Basin,” Sustainability (Switzerland), vol. 14, no. 7, Apr. 2022, DOI: 10.3390/su14074210. [Google Scholar]
  11. G. Tripathi, A. C. Pandey, B. R. Parida, and A. Kumar, “Flood Inundation Mapping and Impact Assessment Using Multi-Temporal Optical and SAR Satellite Data: a Case Study of 2017 Flood in Darbhanga District, Bihar, India,” Water Resources Management, vol. 34, no. 6, pp. 1871-1892, Apr. 2020, DOI: 10.1007/s11269-020-02534-3. [CrossRef] [Google Scholar]
  12. S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image Segmentation Using Deep Learning: A Survey,” IEEE Trans Pattern Anal Mach Intell, vol. 44, no. 7, pp. 3523-3542, 2022, DOI: 10.1109/TPAMI.2021.3059968. [PubMed] [Google Scholar]
  13. K. A. Korznikov, D. E. Kislov, J. Altman, J. Doležal, A. S. Vozmishcheva, and P. V. Krestov, “Using u-net-like deep convolutional neural networks for precise tree recognition in very high resolution rgb (Red, green, blue) satellite images,” Forests, vol. 12, no. 1, pp. 1-17, Jan. 2021, DOI: 10.3390/f12010066. [Google Scholar]
  14. D. E. Kislov and K. A. Korznikov, “Automatic windthrow detection using very-high-resolution satellite imagery and deep learning,” Remote Sens (Basel), vol. 12, no. 7, Apr. 2020, DOI: 10.3390/rs12071145. [Google Scholar]
  15. T. Kattenborn, J. Eichel, and F. E. Fassnacht, “Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery,” Sci Rep, vol. 9, no. 1, p. 17656, 2019, DOI: 10.1038/s41598-019-53797-9. [CrossRef] [PubMed] [Google Scholar]
  16. Institute of Electrical and Electronics Engineers, Adhiparasakthi Engineering College. Department of Electronics and Communication Engineering, and Institute of Electrical and Electronics Engineers. Madras Section, Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing (ICCSP): 6th-8th April, 2017, Melmaruvathur, India. [Google Scholar]
  17. F. H. Wagner et al., “Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images,” Remote Sens Ecol Conserv, vol. 5, no. 4, pp. 360-375, Dec. 2019, DOI: 10.1002/rse2.111. [CrossRef] [Google Scholar]
  18. B. Sariturk and D. Z. Seker, “A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images,” Sensors, vol. 22, no. 19, Oct. 2022, DOI: 10.3390/s22197624. [CrossRef] [PubMed] [Google Scholar]

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