DEEP LEARNING APPLIED TO WATER SEGMENTATION

20 citations as recorded by crossref.

1. A Dual-Source Deep Learning Model for Mapping Wet Anabranches in Braided Rivers on the Tibetan Plateau X. Liu et al. https://doi.org/10.1109/JSTARS.2025.3568272
2. Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia E. Ansari et al. https://doi.org/10.3390/su13179568
3. CAM-UNet: A Novel Water Environment Perception Method Integrating CoAtNet Structure X. Gao et al. https://doi.org/10.3390/s25226963
4. Efficient Water Segmentation with Transformer and Knowledge Distillation for USVs J. Zhang et al. https://doi.org/10.3390/jmse11050901
5. Japan’s Urban-Environmental Exposures: A Tripartite Analysis of City Shrinkage, SAR-Based Deep Learning Versus Forward Modeling in Inundation Mapping, and Future Flood Schemes M. Safabakhshpachehkenari et al. https://doi.org/10.3390/urbansci9030071
6. Automatic Segmentation of Water Bodies Using RGB Data: A Physically Based Approach M. García et al. https://doi.org/10.3390/rs15051170
7. Deep Neural Network-Based Flood Monitoring System Fusing RGB and LWIR Cameras for Embedded IoT Edge Devices Y. Lee et al. https://doi.org/10.3390/rs16132358
8. DBEENet: Dual-branch edge-enhanced network for semantic segmentation of USV maritime images G. Sun et al. https://doi.org/10.1016/j.oceaneng.2025.122731
9. RFD-BiSeNet V2: A Lightweight Floodwater Segmentation Network for Vision-Based Environmental Sensing X. Li et al. https://doi.org/10.3390/s26092841
10. Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi https://doi.org/10.3390/w15183308
11. Convolutional Neural Network-Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data O. Andrew et al. https://doi.org/10.3390/ijgi12050194
12. Integration of object detection and semantic segmentation based on convolutional neural networks for navigation and monitoring of cyanobacterial blooms in lentic water scenes F. Barrientos-Espillco et al. https://doi.org/10.1016/j.asoc.2024.111849
13. SGCAD: A SAR-Guided Confidence-Gated Distillation Framework of Optical and SAR Images for Water-Enhanced Land-Cover Semantic Segmentation J. Ma et al. https://doi.org/10.3390/rs18060962
14. Evaluation of polarization modes and spatial resolution of PALSAR-2 for canal detection accuracy in Indonesia peatlands M. Haidar & W. Takeuchi https://doi.org/10.1080/10106049.2025.2509295
15. A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery A. Moghimi et al. https://doi.org/10.1109/ACCESS.2024.3385425
16. Fine-Tunned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar Images N. Quang et al. https://doi.org/10.1109/ACCESS.2024.3516519
17. Deep learning and LiDAR integration for surveillance camera-based river water level monitoring in flood applications N. Muhadi et al. https://doi.org/10.1007/s11069-024-06503-6
18. Fast Segmentation Algorithm of USV Accessible Area Based on Attention Fast Deeplabv3 L. Cheng et al. https://doi.org/10.1109/JSEN.2024.3410403
19. Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. https://doi.org/10.3390/app11209691
20. Classification of River Sediment Fractions in a River Segment including Shallow Water Areas Based on Aerial Images from Unmanned Aerial Vehicles with Convolution Neural Networks M. Irie et al. https://doi.org/10.3390/rs16010173