FLOOD-WATER LEVEL ESTIMATION FROM SOCIAL MEDIA IMAGES

39 citations as recorded by crossref.

1. A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. 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. 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 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. 10.3390/su12052149
5. Assessing flood severity from crowdsourced social media photos with deep neural networks J. Pereira et al. 10.1007/s11042-020-09196-8
6. Using social media images for building function classification E. Hoffmann et al. 10.1016/j.cities.2022.104107
7. More eyes on the road: Sensing flooded roads by fusing real-time observations from public data sources P. Panakkal & J. Padgett 10.1016/j.ress.2024.110368
8. Application of Deep Learning for Imaging‐Based Stream Gaging R. Vanden Boomen et al. 10.1029/2021WR029980
9. Computer Vision–Based Estimation of Flood Depth in Flooded-Vehicle Images S. Park et al. 10.1061/(ASCE)CP.1943-5487.0000956
10. Application of image processing and convolutional neural networks for flood image classification and semantic segmentation R. Pally & S. Samadi 10.1016/j.envsoft.2021.105285
11. WaRENet: A Novel Urban Waterlogging Risk Evaluation Network X. Yu et al. 10.1145/3651163
12. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
13. An improved ResNet method for urban flooding water depth estimation from social media images W. Du et al. 10.1016/j.measurement.2024.116114
14. Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method Z. Song & Y. Tuo 10.3390/s21165614
15. Cloud Modelling of Property-Level Flood Exposure in Megacities C. Iliadis et al. 10.3390/w15193395
16. Investigating pedestrian behavioral patterns under different floodwater conditions: A video analysis on real flood evacuations E. Quagliarini et al. 10.1016/j.ssci.2023.106083
17. Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
18. Extraction and analysis of natural disaster-related VGI from social media: review, opportunities and challenges Y. Feng et al. 10.1080/13658816.2022.2048835
19. Robust human detection system in flood related images with data augmentation D. M et al. 10.1007/s11042-022-13760-9
20. Drone-Based Water Level Detection in Flood Disasters H. Rizk et al. 10.3390/ijerph19010237
21. Measuring urban waterlogging depths from video images based on reference objects K. Gao et al. 10.1111/jfr3.12948
22. Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery S. Kwon & S. Lee 10.1007/s11269-023-03714-7
23. Water level prediction from social media images with a multi-task ranking approach P. Chaudhary et al. 10.1016/j.isprsjprs.2020.07.003
24. Scalable flood inundation mapping using deep convolutional networks and traffic signage B. Alizadeh & A. Behzadan 10.1007/s43762-023-00090-1
25. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery D. Buscombe et al. 10.1029/2021EA002085
26. Safer this way: Identifying flooded roads for facilitating mobility during floods P. Panakkal et al. 10.1016/j.jhydrol.2023.130100
27. WATERSensing: A Smart Warning System for Natural Disasters in Spain J. Cecilia et al. 10.1109/MCE.2021.3063703
28. An objective method for pedestrian occlusion level classification S. Gilroy et al. 10.1016/j.patrec.2022.10.028
29. 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. 10.1016/j.isprsjprs.2020.09.011
30. A simple framework for calibrating hydraulic flood inundation models using Crowd-sourced water levels A. Dasgupta et al. 10.1016/j.jhydrol.2022.128467
31. Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network M. Moy de Vitry et al. 10.5194/hess-23-4621-2019
32. Estimating the spatial-temporal distribution of urban street ponding levels from surveillance videos based on computer vision X. Hao et al. 10.1007/s11269-022-03107-2
33. A Deep Learning Method for Floodwater Depth Prediction on Roadways from Side-View Real and Synthetic Images of Vehicles C. Sazara et al. 10.1007/s42421-022-00056-5
34. Urban hydrological model (UHM) developed for an urban flash flood simulation and analysis of the flood intensity sensitivity to urbanization H. Hu et al. 10.1080/19475705.2024.2302561
35. Deep Sensing of Urban Waterlogging S. Lo et al. 10.1109/ACCESS.2021.3111623
36. The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods Y. Lin et al. 10.3390/s21144942
37. Investigating Pedestrian Behavioral Patterns Under Different Floodwater Conditions: A Video Analysis on Real Flood Evacuations E. Quagliarini et al. 10.2139/ssrn.4167684
38. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. 10.3390/w16091286
39. A Nationwide Flood Forecasting System for Saudi Arabia: Insights from the Jeddah 2022 Event G. Sofia et al. 10.3390/w16141939