EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 675 (2025) E3S Web Conf., 675 (2025) 03004 References
Open Access
Issue
E3S Web Conf.
Volume 675, 2025
International Scientific Conference on Geosciences and Environmental Management (GeoME’5.5 2025)
Article Number 03004
Number of page(s) 11
Section Artificial Intelligence and Smart Modeling for Resilient Civil Infrastructure and Environmental Systems
DOI https://doi.org/10.1051/e3sconf/202567503004
Published online 11 December 2025
  1. Q. Feng, J. Liu, and J. Gong, “Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China,” Water, vol. 7, no. 4, pp. 1437–1455, Mar. 2015, doi: 10.3390/w7041437. [Google Scholar]
  2. V. Klemas, “Remote Sensing of Floods and Flood-Prone Areas: An Overview,” Journal of Coastal Research, vol. 314, pp. 1005–1013, July 2015, doi: 10.2112/JCOASTRES-D-14-00160.1. [Google Scholar]
  3. N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone,” Remote Sensing of Environment, vol. 202, pp. 18–27, Dec. 2017, doi: 10.1016/j.rse.2017.06.031. [CrossRef] [Google Scholar]
  4. J. Bouaida, O. Witam, M. Ibnoussina, A. E. F. Delmaki, and M. Benkirane, “Contribution of remote sensing and GIS to analysis of the risk of flooding in the Zat basin (High Atlas-Morocco),” Nat Hazards, vol. 108, no. 2, pp. 1835–1851, Sept. 2021, doi: 10.1007/s11069-021-04758-x. [Google Scholar]
  5. S. Qi, D. G. Brown, Q. Tian, L. Jiang, T. Zhao, and K. M. Bergen, “Inundation Extent and Flood Frequency Mapping Using LANDSAT Imagery and Digital Elevation Models,” GIScience & Remote Sensing, vol. 46, no. 1, pp. 101–127, Jan. 2009, doi: 10.2747/1548-1603.46.1.101. [Google Scholar]
  6. H. Xu, “Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery,” International Journal of Remote Sensing, vol. 27, no. 14, pp. 3025–3033, July 2006, doi: 10.1080/01431160600589179. [CrossRef] [Google Scholar]
  7. M.-J. Chang et al., “A Support Vector Machine Forecasting Model for Typhoon Flood Inundation Mapping and Early Flood Warning Systems,” Water, vol. 10, no. 12, p. 1734, Nov. 2018, doi: 10.3390/w10121734. [Google Scholar]
  8. S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, “1D convolutional neural networks and applications: A survey,” Mechanical Systems and Signal Processing, vol. 151, p. 107398, Apr. 2021, doi: 10.1016/j.ymssp.2020.107398. [CrossRef] [Google Scholar]
  9. R. Johary et al., “Detection of Large-Scale Floods Using Google Earth Engine and Google Colab,” Remote Sensing, vol. 15, no. 22, p. 5368, Nov. 2023, doi: 10.3390/rs15225368. [Google Scholar]
  10. T.-N. Doan and D.-N. Le-Thi, “Map Floodwater Radar Imagery using Machine Learning Algorithms,” IIUMEJ, vol. 26, no. 1, pp. 97–112, Jan. 2025, doi: 10.31436/iiumej.v26i1.3157. [Google Scholar]
  11. B. Zhao, H. Sui, C. Xu, and J. Liu, “DEEP LEARNING APPROACH FOR FLOOD DETECTION USING SAR IMAGE: A CASE STUDY IN XINXIANG,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XLIII-B3-2022, pp. 1197–1202, May 2022, doi: 10.5194/isprs-archives-XLIII-B3-2022-1197-2022. [Google Scholar]
  12. O. Andrew, A. Apan, D. R. Paudyal, and K. Perera, “Convolutional Neural Network- Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data,” IJGI, vol. 12, no. 5, p. 194, May 2023, doi: 10.3390/ijgi12050194. [Google Scholar]
  13. V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY: Springer New York, 1995. doi: 10.1007/978-1-4757-2440-0. [Google Scholar]
  14. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” 2015, arXiv. doi: 10.48550/ARXIV.1505.04597. [Google Scholar]
  15. A. Stoian, V. Poulain, J. Inglada, V. Poughon, and D. Derksen, “Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems,” Remote Sensing, vol. 11, no. 17, p. 1986, Aug. 2019, doi: 10.3390/rs11171986. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.