[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Browse::
Journal Info::
Guide for Authors::
Submit Manuscript::
Articles archive::
For Reviewers::
Contact us::
Site Facilities::
Reviewers::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 13, Issue 4 (6-2024) ::
JGST 2024, 13(4): 41-56 Back to browse issues page
Flood detection in UAV images using PSPNet and uncertainty quantification with Monte-Carlo Dropout technique
Seyed Ali Ahmadi * , Ali Mohammadzadeh
Abstract:   (664 Views)
Floods are one of the most frequent natural disasters that affect the society, impact human lives, and make costs for governments. Utilizing new technologies helps managers and first responders to decrease the damaging effect of floods and save time. Unmanned Aerial Vehicles equipped with accurate sensors along with powerful computer vision and deep learning techniques can act as potential platforms for surrveilance, mapping and detection of flooded regions. In this study, PSPNet as the main architecture enhanced by ResNeSt as the encoder, are utilized for semantic segmentation of very high resolution drone imagery acquired from urban flooded regions. Furthermore, in order to interpret and study the performance of the method, Monte-Carlo Dropout (MCD) technique is used as a Bayesian estimator for uncertainty quantification of the results. Comparing the results of our method with other models indicated that increasing the complexity and number of parameters of the model would increase its performance during training and testing by 10% and 3%, respectively, and the certainty of the models will increase in inference time. The Accuracy of semantic segmentation is 97.93% and F1-score is about 89%.
Article number: 4
Keywords: UAV, Disaster management, Deep learning, Flood detection, Semantic segmentation, Building extraction
Full-Text [PDF 3393 kb]   (184 Downloads)    
Type of Study: Research | Subject: Photo&RS
Received: 2023/11/28
References
1. C. Lin, Y. Li, Y. Liu, X. Wang, and S. Geng, "Building damage assessment from post-hurricane imageries using unsupervised domain adaptation with enhanced feature discrimination," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-10, 2021. [DOI:10.1109/TGRS.2021.3054869]
2. S. S. Matin and B. Pradhan, "Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)," Sensors, vol. 21, no. 13, p. 4489, Jun. 2021, doi: 10.3390/s21134489. [DOI:10.3390/s21134489]
3. T. G. J. Rudner et al., "Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery," 2018, [Online]. Available: http://arxiv.org/abs/1812.01756 [DOI:10.1609/aaai.v33i01.3301702]
4. M. N. Abdel-Mooty, A. Yosri, W. El-Dakhakhni, and P. Coulibaly, "Community Flood Resilience Categorization Framework," International Journal of Disaster Risk Reduction, vol. 61, p. 102349, 2021, doi: https://doi.org/10.1016/j.ijdrr.2021.102349 [DOI:10.1016/j.ijdrr.2021.102349.]
5. O. M. Nofal and J. W. van de Lindt, "High-resolution flood risk approach to quantify the impact of policy change on flood losses at community-level," International Journal of Disaster Risk Reduction, vol. 62, p. 102429, 2021, doi: https://doi.org/10.1016/j.ijdrr.2021.102429 [DOI:10.1016/j.ijdrr.2021.102429.]
6. U. Iqbal, P. Perez, W. Li, and J. Barthelemy, "How computer vision can facilitate flood management: A systematic review," International Journal of Disaster Risk Reduction, vol. 53, p. 102030, 2021, doi: https://doi.org/10.1016/j.ijdrr.2020.102030 [DOI:10.1016/j.ijdrr.2020.102030.]
7. M. Zelev{n}áková, "Flood risk assessment and management in Slovakia," WIT Transactions on Ecology and the Environment, vol. 146, pp. 61-70, 2011.
8. X. Wang and P. Li, "Extraction of urban building damage using spectral, height and corner information from VHR satellite images and airborne LiDAR data," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 159, pp. 322-336, 2020. [DOI:10.1016/j.isprsjprs.2019.11.028]
9. L. Dong and J. Shan, "A comprehensive review of earthquake-induced building damage detection with remote sensing techniques," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 84, pp. 85-99, Oct. 2013, doi: 10.1016/j.isprsjprs.2013.06.011. [DOI:10.1016/j.isprsjprs.2013.06.011]
10. S. Cho, H. Xiu, and M. Matsuoka, "Backscattering Characteristics of SAR Images in Damaged Buildings Due to the 2016 Kumamoto Earthquake," Remote Sens (Basel), vol. 15, no. 8, p. 2181, Apr. 2023, doi: 10.3390/rs15082181. [DOI:10.3390/rs15082181]
11. M. Chini, N. Pierdicca, and W. J. Emery, "Exploiting SAR and VHR optical images to quantify damage caused by the 2003 Bam earthquake," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 1, pp. 145-152, 2008. [DOI:10.1109/TGRS.2008.2002695]
12. N. Kerle, F. Nex, M. Gerke, D. Duarte, and A. Vetrivel, "UAV-based structural damage mapping: A review," ISPRS Int J Geoinf, vol. 9, no. 1, pp. 1-23, 2019, doi: 10.3390/ijgi9010014. [DOI:10.3390/ijgi9010014]
13. S. Ghaffarian and N. Kerle, "Towards post-disaster debris identification for precise damage and recovery assessments from uav and satellite images," International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, vol. 42, no. 2/W13, pp. 297-302, 2019, doi: 10.5194/isprs-archives-XLII-2-W13-297-2019. [DOI:10.5194/isprs-archives-XLII-2-W13-297-2019]
14. B. Bauer-Marschallinger et al., "Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube," Remote Sens (Basel), vol. 14, no. 15, p. 3673, 2022. [DOI:10.3390/rs14153673]
15. J. Zhao et al., "Urban-aware U-Net for large-scale urban flood mapping using multitemporal sentinel-1 intensity and interferometric coherence," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-21, 2022. [DOI:10.1109/TGRS.2022.3199036]
16. C. Krullikowski et al., "Estimating ensemble likelihoods for the Sentinel-1 based Global Flood Monitoring product of the Copernicus Emergency Management Service," arXiv preprint arXiv:2304.12488, 2023. [DOI:10.36227/techrxiv.22688101]
17. E. Hamidi, B. G. Peter, D. F. Muñoz, H. Moftakhari, and H. Moradkhani, "Fast Flood Extent Monitoring With SAR Change Detection Using Google Earth Engine," IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-19, 2023. [DOI:10.1109/TGRS.2023.3240097]
18. S. Kundu, V. Lakshmi, and R. Torres, "Estimation of flood inundation and depth during Hurricane Florence using Sentinel-1 and UAVSAR data," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022. [DOI:10.1109/LGRS.2022.3165444]
19. A. F. S. Putri, W. Widyatmanti, and D. A. Umarhadi, "Sentinel-1 and Sentinel-2 data fusion to distinguish building damage level of the 2018 Lombok Earthquake," Remote Sens Appl, vol. 26, p. 100724, 2022. [DOI:10.1016/j.rsase.2022.100724]
20. S. A. Ahmadi, A. Mohammadzadeh, N. Yokoya, and A. Ghorbanian, "BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images," Remote Sens (Basel), vol. 16, no. 1, 2024, doi: 10.3390/rs16010182. [DOI:10.3390/rs16010182]
21. L. Hashemi-Beni and A. A. Gebrehiwot, "Flood Extent Mapping: An Integrated Method Using Deep Learning and Region Growing Using UAV Optical Data," IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 14, pp. 2127-2135, 2021, doi: 10.1109/JSTARS.2021.3051873. [DOI:10.1109/JSTARS.2021.3051873]
22. L. Hashemi-Beni, J. Jones, G. Thompson, C. Johnson, and A. Gebrehiwot, "Challenges and Opportunities for UAV-Based Digital Elevation Model Generation for Flood-Risk Management: A Case of Princeville, North Carolina," Sensors, vol. 18, no. 11, 2018, doi: 10.3390/s18113843. [DOI:10.3390/s18113843]
23. H. Yao, R. Qin, and X. Chen, "Unmanned Aerial Vehicle for Remote Sensing Applications-A Review," Remote Sens (Basel), vol. 11, no. 12, 2019, doi: 10.3390/rs11121443. [DOI:10.3390/rs11121443]
24. V. V Klemas, "Coastal and Environmental Remote Sensing from Unmanned Aerial Vehicles: An Overview," J Coast Res, vol. 31, no. 5, pp. 1260 - 1267, 2015, doi: 10.2112/JCOASTRES-D-15-00005.1. [DOI:10.2112/JCOASTRES-D-15-00005.1]
25. F. Yamazaki, Y. Yano, and M. Matsuoka, "Visual damage interpretation of buildings in Bam city using QuickBird images following the 2003 Bam, Iran, earthquake," Earthquake Spectra, vol. 21, no. 1_suppl, pp. 329-336, 2005. [DOI:10.1193/1.2101807]
26. S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi, and J. A. Benediktsson, "Deep learning for hyperspectral image classification: An overview," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6690-6709, 2019. [DOI:10.1109/TGRS.2019.2907932]
27. Z.-Q. Zhao, P. Zheng, S. Xu, and X. Wu, "Object detection with deep learning: A review," IEEE Trans Neural Netw Learn Syst, vol. 30, no. 11, pp. 3212-3232, 2019. [DOI:10.1109/TNNLS.2018.2876865]
28. G. Tsagkatakis, A. Aidini, K. Fotiadou, M. Giannopoulos, A. Pentari, and P. Tsakalides, "Survey of deep-learning approaches for remote sensing observation enhancement," Sensors (Switzerland), vol. 19, no. 18, pp. 1-39, 2019, doi: 10.3390/s19183929. [DOI:10.3390/s19183929]
29. Y. Gu, Y. Wang, and Y. Li, "A survey on deep learning-driven remote sensing image scene understanding: Scene classification, scene retrieval and scene-guided object detection," Applied Sciences (Switzerland), vol. 9, no. 10, 2019, doi: 10.3390/app9102110. [DOI:10.3390/app9102110]
30. X. Y. Tong et al., "Land-cover classification with high-resolution remote sensing images using transferable deep models," Remote Sens Environ, vol. 237, no. July 2018, p. 111322, 2020, doi: 10.1016/j.rse.2019.111322. [DOI:10.1016/j.rse.2019.111322]
31. Y. Da, Z. Ji, and Y. Zhou, "Building damage assessment based on siamese hierarchical transformer framework," Mathematics, vol. 10, no. 11, p. 1898, 2022. [DOI:10.3390/math10111898]
32. M. Marjani, S. A. Ahmadi, and M. Mahdianpari, "FirePred: A hybrid multi-temporal convolutional neural network model for wildfire spread prediction," Ecol Inform, vol. 78, p. 102282, Dec. 2023, doi: 10.1016/j.ecoinf.2023.102282. [DOI:10.1016/j.ecoinf.2023.102282]
33. M. Schmitt et al., "There Are No Data Like More Data: Datasets for deep learning in Earth observation," IEEE Geosci Remote Sens Mag, pp. 2-36, 2023, doi: 10.1109/MGRS.2023.3293459. [DOI:10.1109/MGRS.2023.3293459]
34. N. Humaira, S. Samadi, and N. C. Hubig, "DX-FloodLine: End-To-End Deep Explainable Pipeline for Real Time Flood Scene Object Detection from Multimedia Images," IEEE Access, 2023. [DOI:10.1109/ACCESS.2023.3321312]
35. A. Gebrehiwot, L. Hashemi-Beni, G. Thompson, P. Kordjamshidi, and T. E. Langan, "Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data," Sensors, vol. 19, no. 7, 2019, doi: 10.3390/s19071486. [DOI:10.3390/s19071486]
36. K. Yang, S. Zhang, X. Yang, N. Wu, and others, "Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning," Complexity, vol. 2022, 2022. [DOI:10.1155/2022/6155300]
37. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431-3440. [DOI:10.1109/CVPR.2015.7298965]
38. A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, "A Survey of the Recent Architectures of Deep Convolutional Neural Networks," pp. 1-69, 2019, [Online]. Available: http://arxiv.org/abs/1901.06032
39. S. K. He and X. Wang, "Spatial pyramid pooling in deep convolutional neural network for visual recognition," in CVPR, 2014.
40. H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, "Pyramid scene parsing network," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2881-2890. [DOI:10.1109/CVPR.2017.660]
41. O. Elharrouss, Y. Akbari, N. Almaadeed, and S. Al-Maadeed, "Backbones-review: Feature extraction networks for deep learning and deep reinforcement learning approaches," arXiv preprint arXiv:2206.08016, 2022.
42. H. Zhang et al., "Resnest: Split-attention networks," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 2736-2746. [DOI:10.1109/CVPRW56347.2022.00309]
43. E. Tjoa and C. Guan, "A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI," IEEE Trans Neural Netw Learn Syst, vol. 32, no. 11, pp. 4793-4813, 2021, doi: 10.1109/TNNLS.2020.3027314. [DOI:10.1109/TNNLS.2020.3027314]
44. A. Singh, S. Sengupta, and V. Lakshminarayanan, "Explainable deep learning models in medical image analysis," J Imaging, vol. 6, no. 6, pp. 1-18, 2020, doi: 10.3390/JIMAGING6060052. [DOI:10.3390/jimaging6060052]
45. M. Segú, A. Loquercio, and D. Scaramuzza, "A General Framework for Uncertainty Estimation in Deep Learning," ArXiv, 2019.
46. W. J. Maddox, T. Garipov, P. Izmailov, D. Vetrov, and A. G. Wilson, "A simple baseline for bayesian uncertainty in deep learning," ArXiv, no. NeurIPS, pp. 1-25, 2019.
47. Y. Gal and Z. Ghahramani, "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning," in Proceedings of The 33rd International Conference on Machine Learning, M. F. Balcan and K. Q. Weinberger, Eds., in Proceedings of Machine Learning Research, vol. 48. New York, New York, USA: PMLR, Nov. 2016, pp. 1050-1059. [Online]. Available: https://proceedings.mlr.press/v48/gal16.html
48. M. Rahnemoonfar, T. Chowdhury, A. Sarkar, D. Varshney, M. Yari, and R. R. Murphy, "FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding," IEEE Access, vol. 9, pp. 89644-89654, 2021, doi: 10.1109/ACCESS.2021.3090981. [DOI:10.1109/ACCESS.2021.3090981]
49. W. Liu, A. Rabinovich, and A. C. Berg, "Parsenet: Looking wider to see better," arXiv preprint arXiv:1506.04579, 2015.
50. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs," IEEE Trans Pattern Anal Mach Intell, vol. 40, no. 4, pp. 834-848, 2017. [DOI:10.1109/TPAMI.2017.2699184]
51. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770-778, 2016, doi: 10.1109/CVPR.2016.90. [DOI:10.1109/CVPR.2016.90]
52. J. Xia, B. Adriano, G. Baier, and N. Yokoya, "Building Damage Mapping Via Transfer Learning," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 4841-4844, 2019, doi: 10.1109/igarss.2019.8900447. [DOI:10.1109/IGARSS.2019.8900447]
53. J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler, "Efficient object localization using Convolutional Networks," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 648-656, 2014, [Online]. Available: https://api.semanticscholar.org/CorpusID:206592615 [DOI:10.1109/CVPR.2015.7298664]
54. S. Jadon, "A survey of loss functions for semantic segmentation," in 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2020, pp. 1-7. doi: 10.1109/CIBCB48159.2020.9277638. [DOI:10.1109/CIBCB48159.2020.9277638]
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ahmadi S A, Mohammadzadeh A. Flood detection in UAV images using PSPNet and uncertainty quantification with Monte-Carlo Dropout technique. JGST 2024; 13 (4) : 4
URL: http://jgst.issgeac.ir/article-1-1167-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 13, Issue 4 (6-2024) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology