AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES

15 citations as recorded by crossref.

1. An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV Images L. Shi et al. https://doi.org/10.1109/JSTARS.2024.3498865
2. A Review of Onboard AI Technologies for Synthetic Aperture Radar: Techniques, challenges, and opportunities P. Sur et al. https://doi.org/10.1109/MGRS.2026.3689429
3. A Deep Learning-based Approach to Predict the Flood Patterns Using Sentinel-1A Time Series Images M. Siddique et al. https://doi.org/10.1007/s12524-024-02016-8
4. High-precision flood detection and mapping via multi-temporal SAR change analysis with semantic token-based transformer T. Saleh et al. https://doi.org/10.1016/j.jag.2024.103991
5. Cross-modal distillation for flood extent mapping S. Garg et al. https://doi.org/10.1017/eds.2023.34
6. Advancing flood disaster management: leveraging deep learning and remote sensing technologies M. Roohi et al. https://doi.org/10.1007/s11600-024-01481-6
7. High-resolution mapping of China’s flooded croplands J. Qiu et al. https://doi.org/10.1016/j.scib.2025.01.053
8. Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms M. Shafapourtehrany et al. https://doi.org/10.3390/rs15184501
9. Automatic Flood Detection from Sentinel-1 Data Using a Nested UNet Model and a NASA Benchmark Dataset B. Ghosh et al. https://doi.org/10.1007/s41064-024-00275-1
10. Classification and detection of natural disasters using machine learning and deep learning techniques: A review K. Abraham et al. https://doi.org/10.1007/s12145-023-01205-2
11. XFIMNet: an Explainable deep learning architecture for versatile flood inundation mapping with synthetic aperture radar and multi-spectral optical images J. Sanderson et al. https://doi.org/10.1080/01431161.2023.2288945
12. Multi-Source Attention-Based Fusion for Segmentation of Natural Disasters M. Chendeb El Rai et al. https://doi.org/10.1109/LGRS.2024.3414421
13. Flood Detection in Polarimetric SAR Data Using Deformable Convolutional Vision Model H. Yu et al. https://doi.org/10.3390/w15244202
14. Accuracy of Surface Water Maps Derived from Radar Satellite Imagery Compared to Multispectral Satellite Imagery K. Kirby et al. https://doi.org/10.1080/07038992.2024.2433591
15. DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection Z. Wang et al. https://doi.org/10.1109/JSTARS.2025.3584282