FLOOD-WATER LEVEL ESTIMATION FROM SOCIAL MEDIA IMAGES

42 citations as recorded by crossref.

1. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. https://doi.org/10.3390/w15091760
2. Collaborative spatial information as an alternative data source for hydrodynamic model calibration: a Pernambuco State case study, Brazil G. de Oliveira et al. https://doi.org/10.1007/s11069-023-06073-z
3. Ungauged Basin Flood Prediction Using Long Short-Term Memory and Unstructured Social Media Data J. Lee & S. Hwang https://doi.org/10.3390/w15213818
4. A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network J. Huang et al. https://doi.org/10.3390/su12052149
5. Assessing flood severity from crowdsourced social media photos with deep neural networks J. Pereira et al. https://doi.org/10.1007/s11042-020-09196-8
6. Using social media images for building function classification E. Hoffmann et al. https://doi.org/10.1016/j.cities.2022.104107
7. Measuring urban waterlogging depth from video images using human body models K. Gao et al. https://doi.org/10.1016/j.measurement.2025.119086
8. Application of Deep Learning for Imaging‐Based Stream Gaging R. Vanden Boomen et al. https://doi.org/10.1029/2021WR029980
9. Computer Vision–Based Estimation of Flood Depth in Flooded-Vehicle Images S. Park et al. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000956
10. Automatic segmentation of urban flood extent in video image with DSS-YOLOv8n J. Wan et al. https://doi.org/10.1016/j.jhydrol.2025.132974
11. A deep learning method for identifying waterlogging depth on urban roadways from surveillance camera images M. Ouyang et al. https://doi.org/10.1016/j.rsase.2025.101827
12. WaRENet: A Novel Urban Waterlogging Risk Evaluation Network X. Yu et al. https://doi.org/10.1145/3651163
13. Deep learning for water quality W. Zhi et al. https://doi.org/10.1038/s44221-024-00202-z
14. An improved ResNet method for urban flooding water depth estimation from social media images W. Du et al. https://doi.org/10.1016/j.measurement.2024.116114
15. Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo https://doi.org/10.3390/s21165614
16. Robust Pedestrian Tracking With Severe Occlusions in Public Spaces Using 3D Point Clouds R. Ukyo et al. https://doi.org/10.1109/TITS.2025.3547418
17. Cloud Modelling of Property-Level Flood Exposure in Megacities C. Iliadis et al. https://doi.org/10.3390/w15193395
18. Estimating urban flooding depth by integrating multimodal image-text data: A segment-level direct preference optimization-based multimodal large language model T. Chu et al. https://doi.org/10.1016/j.isprsjprs.2025.10.013
19. Investigating pedestrian behavioral patterns under different floodwater conditions: A video analysis on real flood evacuations E. Quagliarini et al. https://doi.org/10.1016/j.ssci.2023.106083
20. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. https://doi.org/10.3390/hydrology10080158
21. Extraction and analysis of natural disaster-related VGI from social media: review, opportunities and challenges Y. Feng et al. https://doi.org/10.1080/13658816.2022.2048835
22. Robust human detection system in flood related images with data augmentation D. M et al. https://doi.org/10.1007/s11042-022-13760-9
23. Drone-Based Water Level Detection in Flood Disasters H. Rizk et al. https://doi.org/10.3390/ijerph19010237
24. Measuring urban waterlogging depths from video images based on reference objects K. Gao et al. https://doi.org/10.1111/jfr3.12948
25. Social media-driven multi-scale flood sensing: A systematic review of applications from global monitoring to community resilience J. Rui & W. Gong https://doi.org/10.1016/j.jenvman.2026.129895
26. Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery S. Kwon & S. Lee https://doi.org/10.1007/s11269-023-03714-7
27. Scalable flood inundation mapping using deep convolutional networks and traffic signage B. Alizadeh & A. Behzadan https://doi.org/10.1007/s43762-023-00090-1
28. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery D. Buscombe et al. https://doi.org/10.1029/2021EA002085
29. WATERSensing: A Smart Warning System for Natural Disasters in Spain J. Cecilia et al. https://doi.org/10.1109/MCE.2021.3063703
30. An objective method for pedestrian occlusion level classification S. Gilroy et al. https://doi.org/10.1016/j.patrec.2022.10.028
31. Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: A case study of Hurricane Harvey Y. Feng et al. https://doi.org/10.1016/j.isprsjprs.2020.09.011
32. Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network M. Moy de Vitry et al. https://doi.org/10.5194/hess-23-4621-2019
33. Estimating the spatial-temporal distribution of urban street ponding levels from surveillance videos based on computer vision X. Hao et al. https://doi.org/10.1007/s11269-022-03107-2
34. A Deep Learning Method for Floodwater Depth Prediction on Roadways from Side-View Real and Synthetic Images of Vehicles C. Sazara et al. https://doi.org/10.1007/s42421-022-00056-5
35. Urban hydrological model (UHM) developed for an urban flash flood simulation and analysis of the flood intensity sensitivity to urbanization H. Hu et al. https://doi.org/10.1080/19475705.2024.2302561
36. Deep Sensing of Urban Waterlogging S. Lo et al. https://doi.org/10.1109/ACCESS.2021.3111623
37. Exploring Generative AI for YOLO-Based Object Detection to Enhance Flood Disaster Response in Malaysia J. Teoh et al. https://doi.org/10.1109/ACCESS.2024.3498893
38. A deep learning approach for urban pluvial flood depth estimation using citizen-generated data and physics-based validation I. Sejinja et al. https://doi.org/10.2166/hydro.2025.107
39. The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods Y. Lin et al. https://doi.org/10.3390/s21144942
40. Investigating Pedestrian Behavioral Patterns Under Different Floodwater Conditions: A Video Analysis on Real Flood Evacuations E. Quagliarini et al. https://doi.org/10.2139/ssrn.4167684
41. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. https://doi.org/10.3390/w16091286
42. A Nationwide Flood Forecasting System for Saudi Arabia: Insights from the Jeddah 2022 Event G. Sofia et al. https://doi.org/10.3390/w16141939