FLOOD DETECTION IN TIME SERIES OF OPTICAL AND SAR IMAGES

52 citations as recorded by crossref.

1. AgriFloodNet: a dual patch CNN architecture for mapping flooded agricultural lands via bi-temporal multi-sensor images A. Jenifer et al. https://doi.org/10.1080/10106049.2022.2082549
2. MFF-Net: Flood Detection from SAR Images Using Multi-Frequency and Fuzzy Uncertainty Fusion Y. Gao et al. https://doi.org/10.3390/rs18010123
3. SD-CapsNet: A Siamese Dense Capsule Network for SAR Image Registration with Complex Scenes B. Li et al. https://doi.org/10.3390/rs15071871
4. Detection of Forest Fires through Deep Unsupervised Learning Modeling of Sentinel-1 Time Series T. Di Martino et al. https://doi.org/10.3390/ijgi12080332
5. Towards global flood mapping onboard low cost satellites with machine learning G. Mateo-Garcia et al. https://doi.org/10.1038/s41598-021-86650-z
6. SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping S. Garske et al. https://doi.org/10.1109/JSTARS.2025.3579330
7. A SAR-based flood mapping approach: application of SAR-SIFT registration and modified DeepLabV3 segmentation in flood hazard assessment Z. Zhang et al. https://doi.org/10.1080/10106049.2025.2512188
8. Rethinking the Flow: A Canal-Based Machine Learning Approach to Urban Flood Detection Q. Euler & C. Gerlein-Safdi https://doi.org/10.1109/TGRS.2026.3671457
9. EarthNets: Empowering artificial intelligence for Earth observation Z. Xiong et al. https://doi.org/10.1109/MGRS.2024.3466998
10. A new multi-source remote sensing image sample dataset with high resolution for flood area extraction: GF-FloodNet Y. Zhang et al. https://doi.org/10.1080/17538947.2023.2230978
11. Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications F. Mena et al. https://doi.org/10.1109/JSTARS.2024.3361556
12. Mapping global floods with 10 years of satellite radar data A. Misra et al. https://doi.org/10.1038/s41467-025-60973-1
13. AIFloodSense: A Global Aerial Imagery Dataset for Semantic Segmentation and Understanding of Flooded Environments G. Simantiris et al. https://doi.org/10.3390/rs18060938
14. Sentinel-1 SAR Images and Deep Learning for Water Body Mapping F. Pech-May et al. https://doi.org/10.3390/rs15123009
15. A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps R. Mukherjee et al. https://doi.org/10.5194/essd-16-4311-2024
16. Evaluation of Automated Water Surface Extraction Using Multi-Source Remote Sensing Data: A Case Study of the Veľká Domaša Reservoir, Slovakia Ľ. Kseňak et al. https://doi.org/10.3390/rs18040545
17. SAR-Based Flood Extent Mapping with a Lightweight Siamese U-Net and Differential Attention Mechanism A. Kaçmaz & U. Alganci https://doi.org/10.3390/earth7030087
18. Survey on Disaster Management Datasets for Remote Sensing-Based Emergency Applications A. Ndigande et al. https://doi.org/10.1109/TGRS.2026.3692760
19. Segmentation and Visualization of Flooded Areas Through Sentinel-1 Images and U-Net F. Pech-May et al. https://doi.org/10.1109/JSTARS.2024.3387452
20. Multisource SAR-Based Rural Flood and Partially Submerged Vegetation Mapping Using Fuzzy Logic and Machine Learning Z. Li et al. https://doi.org/10.1109/JSTARS.2025.3599905
21. Flood inundation monitoring with multi-source satellite imagery based on deep learning and explainable frameworks H. Yu et al. https://doi.org/10.1016/j.jhydrol.2026.135409
22. Efficient Water Body Detection Based on Knowledge Distillation for SAR Imagery J. Zhu et al. https://doi.org/10.1109/LGRS.2025.3597141
23. Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation Z. Li & I. Demir https://doi.org/10.1080/01431161.2024.2305629
24. On the Use of SAR Images for Predicting Vegetation Indices: Challenges and Limitations M. Barbato et al. https://doi.org/10.3390/rs18142400
25. Deep Self-Supervised Disturbance Mapping With the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product H. Hardiman-Mostow et al. https://doi.org/10.1109/JSTARS.2025.3557311
26. A Fine Agricultural Flood Segmentation Model for HJ-2E S-Band SAR Data M. Song et al. https://doi.org/10.1109/JSTARS.2025.3587896
27. Monitoring of the Poyang Lake area’s sharp change under extreme drought using multi-temporal SAR and optical data S. Miao et al. https://doi.org/10.1117/1.JRS.19.021011
28. An Efficient Flood Detection Method With Satellite Images Based on Algorithm–Hardware Co-Design D. Pan et al. https://doi.org/10.1109/LGRS.2024.3472050
29. Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning G. Konapala et al. https://doi.org/10.1016/j.isprsjprs.2021.08.016
30. Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey N. Mohammadi Foumani et al. https://doi.org/10.1145/3649448
31. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. https://doi.org/10.3390/rs16040656
32. U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding Z. Li & I. Demir https://doi.org/10.1016/j.scitotenv.2023.161757
33. Registration-aware cross-modal interaction network for optical and SAR images Z. Chen et al. https://doi.org/10.1016/j.inffus.2025.103278
34. Multimodal Colearning Meets Remote Sensing: Taxonomy, State of the Art, and Future Works N. Kieu et al. https://doi.org/10.1109/JSTARS.2024.3378348
35. S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images M. Wieland et al. https://doi.org/10.1109/JSTARS.2023.3333969
36. Deep Learning for Satellite Image Time-Series Analysis: A review L. Miller et al. https://doi.org/10.1109/MGRS.2024.3393010
37. Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data S. Surampudi & V. Kumar https://doi.org/10.1016/j.rsase.2024.101361
38. Multimodal Satellite Image Time Series Analysis Using GAN-Based Domain Translation and Matrix Profile A. Radoi https://doi.org/10.3390/rs14153734
39. SAR-W-MixMAE: Polarization-Aware Self-Supervised Pretraining for Masked Autoencoders on SAR Data A. Caglayan et al. https://doi.org/10.1109/JSTARS.2026.3652404
40. A Survey on the Datasets and Algorithms for Satellite Data Applications M. Affek & J. Szymański https://doi.org/10.1109/JSTARS.2024.3424954
41. PDFID: A high-resolution flood inundation dataset with a long time series L. Chen et al. https://doi.org/10.1016/j.ejrh.2024.101715
42. When Data Augmentation Fails in Remote Sensing Detection: A Systematic Review of Adverse Effects, Failure Classes, and Physics Constraints N. Madaan et al. https://doi.org/10.1007/s11831-026-10608-5
43. The potential of Sentinel-1 imagery for flood event detection: A satellite vs. hydraulic model comparison J. Breznik et al. https://doi.org/10.1016/j.jhydrol.2024.132587
44. Evaluation of open-source SAR-based flood datasets for flood extent mapping in emergency settings I. San Jose et al. https://doi.org/10.1016/j.pdisas.2025.100507
45. DAM-Net: Flood detection from SAR imagery using differential attention metric-based vision transformers T. Saleh et al. https://doi.org/10.1016/j.isprsjprs.2024.05.018
46. RETRACTED ARTICLE: Drought Prediction and Analysis of Water level based on satellite images Using Deep Convolutional Neural Network J. Balajee & M. Saleem Durai https://doi.org/10.1007/s10772-021-09850-y
47. Urban flood mapping using Sentinel-1 and RADARSAT Constellation Mission image and convolutional Siamese network N. Ghasemian Sorboni et al. https://doi.org/10.1007/s11069-024-06434-2
48. Benchmark Datasets for Satellite Image Time Series Classification: A Review A. Zhang et al. https://doi.org/10.3390/rs18101581
49. ISSLIDE: A New InSAR Dataset for Slow SLIding Area DEtection With Machine Learning A. Bralet et al. https://doi.org/10.1109/LGRS.2024.3365299
50. Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR Imagery and Deep Learning V. Katiyar et al. https://doi.org/10.3390/rs13122334
51. A Framework to Assess Remote Sensing Algorithms for Satellite-Based Flood Index Insurance M. Thomas et al. https://doi.org/10.1109/JSTARS.2023.3244098
52. BlessemFlood21: A High-Resolution Georeferenced Dataset for Advanced Analysis of River Flood Scenarios V. Polushko et al. https://doi.org/10.1109/ACCESS.2024.3487413