Abstract
Assessing the destruction caused by a tsunami is a challenging task that must be completed quickly with limited resources and information. To address this issue, we propose a method for accurate damage mapping using binary classification of high-resolution satellite imagery, where we enhance the performance of three pre-trained deep neural network models (Vgg19, Inception, and Xception). The pre-trained models are used, which have been previously trained on large datasets, and transferred to our tsunami problem. We also develop a custom network architecture specifically designed for tsunami damage detection using high-resolution remote sensing data, improving the accuracy of automated binary classification. We investigate the impact of various parameters and learning rates to detect small objects, demonstrating the suitability of our approach for tsunami damage assessment. Our network outperforms traditional and current deep learning-based approaches, as it shows low bias and high variance datasets that result in a skillful model. Specifically, we observe that Inception-v3 performs best on the dataset, exhibiting good behavior with low errors and achieving the best overall score with 24.11 min, while other models score between 30.50 min for Vgg19 and 45.33 min for Xception. Our study focuses on two important binary classification categories, tsunami-stricken and non-stricken areas, for which we train the proposed framework on a dataset comprising 30,000 small tiles of high-resolution satellite images obtained from Mexer satellite images. The model is validated on 8000 images using the Jupyter notebook of the Anaconda deep learning framework.
Similar content being viewed by others
References
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Craig Citro GS, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems, 2015. Google Brain Team. Software available from tensorflow.org
Anaconda Software Distribution (2020) Anaconda Documentation. Anaconda Inc. Retrieved from https://docs.anaconda.com/
Arnold T, Tilton L (2017) Automatic building damage assessment using deep learning and ground level image data. In: Applied sciences: school of computing science pp 113–129. Retrieved from http://link.springer.com.ezp-prod1.hul.harvard.edu/chapter/10.1007/978-3-319-20702-5_8.
Bai Y, Mas E, Koshimura S (2018) Towards operational satellite-based damage-mapping using U-net convolutional network: a case study of 2011 Tohoku earthquake-Tsunami. Remote Sens 10(10):1626. https://doi.org/10.3390/rs10101626
Bhattacharya D, Shrestha S, Riggan B, Salinas V (2019) Deep learning for automatic detection of earthquake damage using optical satellite imagery. IEEE Trans Geosci Remote Sens 57(10):7581–7596
Casper da Costa-Luis SK, Larroque SK, Mary H, Altendorf K, Yorav-Raphael N, Korobov M, Ivanov I, Bargull M, Rodrigues N, Chen G, Dektyarev M, Mjstevens M, Pagel D, Zugnoni M, Charles J, Newey T, Malmgren S, Umer A (2020) tqdm: a fast, extensible progress bar for Python and CLI (v4.46.0) [Software]. Zenodo. https://doi.org/10.5281/zenodo.3783558
Cha Y, Choi JW, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng 32(5):361–378. https://doi.org/10.1111/mice.12263
Chini M, Piscini A, Cinti FR, Amici S, Nappi R, DeMartini PM (2013) Tohoku (Japan) Tsunami inundation and liquefaction investigated through optical, thermal, and SAR data. IEEE Geosci Remote Sens Lett 10:347–351
Chollet F, Others. (2015). Keras [Computer software]. GitHub. https://github.com/fchollet/keras.
Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195
Diederik J, Jimmy D (2014) The effects of sleep deprivation on cognitive performance. J Sleep Res 23(5):535–542. https://doi.org/10.1111/jsr.12145
Endo Y, Adriano B, Mas E, Koshimura S (2018) New insights into multiclass damage classification of Tsunami-induced building damage from SAR images. Remote Sens 10:2059. https://doi.org/10.3390/rs10122059
Fujita A, Sakurada K, Imaizumi T, Ito R, Hikosaka S, Nakamura R (2017) Damage detection from aerial images via convolutional neural networks. In: Proceedings of the 2017 fifteenth IAPR international conference on machine vision applications (MVA), Nagoya, Japan, pp 5–8
Guo H, Wang X, Guo T, Liu Z (2020) Research on fast damage assessment of sudden natural disasters based on deep learning. J Ambient Intell Humaniz Comput 11(9):3739–3751
Hashemi H, Abdelghany K (2018) End-to-end deep learning methodology for real-time traffic network management. Comput Aided Civ Infrastruct Eng 33(10):849–863. https://doi.org/10.1111/mice.12376
Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9(3):90–95
Imamura F, Boret SP, Suppasri A, Muhari A (2019) Recent occurrences of serious tsunami damage and the future challenges of tsunami disaster risk reduction. Progress Disaster Sci 1:100009. https://doi.org/10.1016/j.pdisas.2019.100009
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach FR, Blei DM (Eds), Proceedings of the 32nd international conference on machine learning, ICML 2015, vol 37, Lille, France, pp 448–456)
Ji Y, Sumantyo JTS, Chua MY, Waqar MM (2018) Earthquake/Tsunami damage assessment for urban areas using post-event PolSAR data. Remote Sens 10:1088
Kalinicheva E, Sublime J, Trocan M (2018) Neural network autoencoder for change detection in satellite image time series. In: Proceedings of the 25th IEEE international conference on electronics, circuits and systems (ICECS 2018), Bordeaux, France, pp 641–642
Khiali L, Ndiath M, Alleaume S, Ienco D, Ose K, Teisseire M (2019) Detection of spatio-temporal evolutions on multi-annual satellite image time series: a clustering-based approach. Int J Appl Earth Obs Geoinf 74:103–119. https://doi.org/10.1016/j.jag.2018.08.013
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the neural information processing systems conference, Stateline, NV
Lin Y, Nie Z, Zhang H, Ma HW (2017) Structural damage detection with automatic feature-extraction through deep learning. Comput Aided Civ Infrastruct Eng 32(12):1025–1046. https://doi.org/10.1111/mice.12313
Liu Z, Xu Y, Xu J, Li Y, Li H, Li J (2021) A deep learning approach for automatic building damage assessment from high-resolution remote sensing images after natural disasters. Remote Sens 13(3):491
MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol 1. pp 281–297
Makinoshima F, Oishi Y, Yamazaki T (2021) Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks. Nat Commun 12:2253. https://doi.org/10.1038/s41467-021-22348-0
McKinney W (2010) Data structures for statistical computing in Python. In: Proceedings of the 9th Python in science conference vol 445, pp 51–56
Minghelli A, Spagnoli J, Lei M, Chami M, Charmasson S (2020) Shoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method. Remote Sens 12(16):2664. https://doi.org/10.3390/rs12162664
Mori N, Takahashi T, Yasuda T, Yanagisawa H (2011) Survey of 2011 Tohoku earthquake tsunami inundation and run-up. Geophys Res Lett. https://doi.org/10.1029/2011GL047440
Nabian A, Meidani H (2018) Deep learning for accelerated seismic reliability analysis of transportation networks. Comput Aided Civ Infrastruct Eng 33(6):443–458. https://doi.org/10.1111/mice.12359
Ohta Y, Murakami H, Watoh Y, Koyama M (2004) A model for evaluating life span characteristics of entrapped occupants by an earthquake. In: Proceedings of the 13th world conference on earthquake engineering, Vancouver, BC
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In Navab N, Hornegger JMWIII, Frangi AF (Eds), Medical image computing and computer-assisted intervention—MICCAI 2015—18th international conference, Munich, Germany, 5–9 October 2015; Proceedings, Part III, Lecture Notes in Computer Science vol 9351, Springer, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28
Seide F, Agarwal A (2016) CNTK: Microsoft's open-source deep-learning toolkit. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining pp 2135–2135. Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the international conference on learning representations (ICLR), San Diego, CA
Sublime J (2021) The 2011 Tohoku Tsunami from the sky: a review on the evolution of artificial intelligence methods for damage assessment. Geosciences 11(3):133. https://doi.org/10.3390/geosciences11030133
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1–9. IEEE. https://doi.org/10.1109/CVPR.2015.7298594
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2016a) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016b) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826. https://doi.org/10.1109/CVPR.2016.308
Wieland M, Liu W, Yamazaki F (2016) Learning change from synthetic aperture radar images: performance evaluation of a support vector machine to detect earthquake and Tsunami-induced changes. Remote Sens 8(10):792
Yang X, Li H, Yu Y, Luo X, Huang T, Yang X (2018) Automatic pixel-level crack detection and measurement using fully convolutional network. Comput Aided Civ Infrastruct Eng 33(12):1090–1109. https://doi.org/10.1111/mice.12412
Zhang A, Wang KCP, Li B, Yang E, Dai X, Peng Y, Chen C (2017) Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput Aided Civ Infrastruct Eng 32(10):805–819. https://doi.org/10.1111/mice.12297
Zheng Y, Yang C, Merkulov A (2018). Breast cancer screening using convolutional neural network and follow-up digital mammography, In: Proc. SPIE 10669, Computational Imaging III, p 1066905. https://doi.org/10.1117/12.2304564
Funding
The authors have not disclosed any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No conflict of interest exists. We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ahmed, S.M.S., Güneyli, H. Automatic post-tsunami loss modeling using deep learning CNN case study: Miyagi and Fukushima Japan tsunami. Nat Hazards 117, 3371–3397 (2023). https://doi.org/10.1007/s11069-023-05991-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-023-05991-2