Flood monitoring and reservoir management in the transboundary Chenab River Basin using machine learning and remote sensing techniques DOI

Amatul Baseer,

Muhammad Farooq Iqbal

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 11(1)

Published: Dec. 23, 2024

Language: Английский

High-precision flood detection and mapping via multi-temporal SAR change analysis with semantic token-based transformer DOI Creative Commons
Tamer Saleh, Shimaa Holail, Xiongwu Xiao

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 131, P. 103991 - 103991

Published: June 24, 2024

Language: Английский

Citations

7
Introducing a New Index for Flood Mapping Using Sentinel-2 Imagery (SFMI) DOI
Hadi Farhadi, Hamid Ebadi, Abbas Kiani

et al.

Computers & Geosciences, Journal Year: 2024, Volume and Issue: 194, P. 105742 - 105742

Published: Oct. 25, 2024

Language: Английский

Citations

7
Large-scale flood mapping using Sentinel-1 and Sentinel-2 imagery: Spatio-temporal analysis of the 23·7 Haihe basin-wide extreme flood DOI

Ling Lan,

Xiekang Wang

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132777 - 132777

Published: Jan. 1, 2025

Language: Английский

Citations

0
Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves DOI Creative Commons
Xinguo Ming,

Yichao Tian,

Qiang Zhang

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104398 - 104398

Published: Feb. 1, 2025

Language: Английский

Citations

0
Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery DOI
W Chen, Yinfei Zhou, Xiaofeng Li

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 139, P. 104550 - 104550

Published: April 19, 2025

Language: Английский

Citations

0
Tidal flat topography mapping with Sentinel time series using cross-modal sample transfer and deep learning DOI
Pengfei Tang, Shanchuan Guo, Lu Qie

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 225, P. 69 - 87

Published: April 27, 2025

Language: Английский

Citations

0
Flood Extent Mapping in SAR Images using Semi-Supervised Approach DOI Creative Commons

S Girisha,

G. Savitha,

P. Sughosh

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105304 - 105304

Published: May 1, 2025

Language: Английский

Citations

0
A novel convolutional neural network model with hybrid attentional atrous convolution module for detecting the areas affected by the flood DOI
Abdullah ŞENER, Gürkan Doğan, Burhan Ergen

et al.

Earth Science Informatics, Journal Year: 2023, Volume and Issue: 17(1), P. 193 - 209

Published: Nov. 24, 2023

Language: Английский

Citations

8
Assessing the Catastrophic Environmental Impacts on Dam Breach Using Remote Sensing and Google Earth Engine DOI
Rasha M. Abou Samra, R.R. Alí, Bijay Halder

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(13), P. 5079 - 5095

Published: May 31, 2024

Language: Английский

Citations

2
Unsupervised Color Based Flood Segmentation in UAV Imagery DOI Open Access
Georgios Simantiris, Costas Panagiotakis

Published: April 16, 2024

We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from Unmanned Aerial Vehicles (UAVs). To the best of our knowledge, this is first fully in captured by UAVs, without need pre-disaster images. The proposed framework addresses problem based on parameter-free calculated masks image analysis techniques. First, algorithm gradually excludes areas classified as non-flood over each component LAB colorspace, well an RGB vegetation index detected edges original image. Unsupervised techniques, such distance transform, are then applied, producing probability map location flooded areas. Finally, obtained applying hysteresis thresholding segmentation. tested compared with variations, other supervised methods two public datasets, consisting 953 total, yielding high-performance results, 87.4% 80.9% overall accuracy F1-Score, respectively. results computational efficiency show that it suitable board data execution decision-making during UAVs flight.

Language: Английский

Citations

1