Abstract
High resolution satellite images serve as an eye in the sky and are helpful in generating landslide inventories. Satellite images of landslides can be used as an input for deep learning models that are specifically designed for image processing and assess the risk posed by landslides on communities. Fully satellite image-based landslide risk evaluation is still a new venture that needs a lot of improvements before it can be deployed in real-time. In this work, we assess landslide hazard to communities near debris scars of slopes that have experienced landslidesĀ in the past, using three pre-trained deep learning algorithms. We followed a 70:30 scheme for training and validation with over 2000 images of two classes: Urban and Debris. Finally, the trained neural network is tested on images that contain human settlements near the debris scars. Based on class probabilities predicted by the algorithm, the sites were ranked for eminent risks in the future. It was observed that the validation accuracy of Alexnet, Resnet and NASNet-Large were 92%, 96% and 98%, respectively. The risk classification for four testĀ images indicates: Alexnet and NASNet-Large extracted features same as observed in the satellite images however, Resnet50 is very sensitive to the features and could not predict the same as observed in satellite images. Given the accuracy of predictions, such algorithms can be further modified and deployed to create landslide hazard risk maps for various communities around the world.
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References
Ali, R., Kuriqi, A., Kisi, O.: Human-environment natural disasters interconnection in china: a review. Climate 8, 48 (2020). https://doi.org/10.3390/cli8040048
Younis, A., Shixin, L., Jn, S., Hai, Z.: Real-time object detection using pre-trained deep learning models MobileNet-SSD. In: Proceedings of 2020 the 6th International Conference on Computing and Data Engineering, pp. 44ā48. ACM, New York, NY, USA (2020). https://doi.org/10.1145/3379247.3379264
Wurm, M., Stark, T., Zhu, X.X., Weigand, M., Taubenbƶck, H.: Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks. ISPRS J. Photogramm. Remote. Sens. 150, 59ā69 (2019). https://doi.org/10.1016/j.isprsjprs.2019.02.006
Fan, X., Scaringi, G., Korup, O., West, A.J., Westen, C.J., Tanyas, H., Hovius, N., Hales, T.C., Jibson, R.W., Allstadt, K.E., Zhang, L., Evans, S.G., Xu, C., Li, G., Pei, X., Xu, Q., Huang, R.: Earthquake-induced chains of geologic hazards: patterns, mechanisms, and impacts. Rev. Geophys. 57, 421ā503 (2019). https://doi.org/10.1029/2018RG000626
Kattenborn, T., Eichel, J., Wiser, S., Burrows, L., Fassnacht, F.E., Schmidtlein, S.: Convolutional neural networks accurately predict cover fractions of plant species and communities in unmanned aerial vehicle imagery. Remote Sens. Ecol. Conserv. 6, 472ā486 (2020). https://doi.org/10.1002/rse2.146
Naveenkumar, K.S., Vinayakumar, R., Soman, K.P.: Amrita-CEN-SentiDB:Twitter dataset for sentimental analysis and application of classical machine learning and deep learning. In: Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2019), pp. 1522ā1527. IEEE (2019)
Sujadevi, V.G., Soman, K.P., Vinayakumar, R., Prem Sankar, A.U.: Anomaly detection in phonocardiogram employing deep learning. In: Advances in Intelligent Systems and Computing, pp. 525ā534. Springer (2019). https://doi.org/10.1007/978-981-10-8055-5_47
Akarsh, S., Simran, K., Poornachandran, P., Menon, V.K., Soman, K.P.: Deep Learning Framework and Visualization for Malware Classification (2019)
AydoÄan, M., Karci, A.: Improving the accuracy using pre-trained word embeddings on deep neural networks for Turkish text classification. Phys. A Stat. Mech. Appl. 541, 123288 (2020). https://doi.org/10.1016/j.physa.2019.123288
Peng, C., Li, Y., Jiao, L., Shang, R.: Efficient convolutional neural architecture search for remote sensing image scene classification. IEEE Trans. Geosci. Remote Sens. 59, 6092ā6105 (2021). https://doi.org/10.1109/TGRS.2020.3020424
Chaabani, C., Abdelfattah, R., Melgani, F.: Deep learning approach for post-flood soil deformation mapping using Insar data. In: 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), pp. 77ā80. IEEE (2020). https://doi.org/10.1109/M2GARSS47143.2020.9105155
Catani, F.: Landslide detection by deep learning of non-nadiral and crowdsourced optical images. Landslides 18, 1025ā1044 (2021). https://doi.org/10.1007/s10346-020-01513-4
Ozcan, T., Basturk, A.: Performance improvement of pre-trained convolutional neural networks for action recognition. Comput. J. 00 (2020)
Xu, C., Xu, X., Yao, X., Dai, F.: Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis. Landslides 11, 441ā461 (2014)
New Zealand, T. great journeys of: Kaikoura earthquake rips through New Zealand, https://www.greatjourneysofnz.co.nz/blog/kaikoura-earthquake-derails-the-main-north-line/. Last accessed 2 Oct 2019
Illarionova, S., Nesteruk, S., Shadrin, D., Ignatiev, V., Pukalchik, M., Oseledets, I.: MixChannel: advanced augmentation for multispectral satellite images. Remote Sens. 13, 2181 (2021). https://doi.org/10.3390/rs13112181
Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 843ā852. IEEE (2017). https://doi.org/10.1109/ICCV.2017.97
Sridharan, A., RA., A.S., Gopalan, S.: A novel methodology for the classification of debris scars using discrete wavelet transform and support vector machine. Procedia Comput. Sci. 171, 609ā616 (2020). https://doi.org/10.1016/j.procs.2020.04.066
Clausen, L.B.N., Nickisch, H.: Automatic classification of Auroral images from the Oslo Auroral THEMIS (OATH) data set using machine learning. J. Geophys. Res. Space Physics 123, 5640ā5647 (2018). https://doi.org/10.1029/2018JA025274
Acknowledgements
The Authors would like to thank Dr. Vivek Menon, Dept of Computer Sciences, Amrita Vishwa Vidyapeetham, Amritapuri, for guiding us throughout the work. We would also like to express our gratitude to the Chancellor of Amrita University āMata Amritanandamayi Deviā for being the source of immense inspiration behind this work.
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Sridharan, A., Ajai, A.S.R., Gopalan, S. (2022). Landslide Susceptibility for Communities Based on Satellite Images Using Deep Learning Algorithms. In: Reddy, V.S., Prasad, V.K., Mallikarjuna Rao, D.N., Satapathy, S.C. (eds) Intelligent Systems and Sustainable Computing. Smart Innovation, Systems and Technologies, vol 289. Springer, Singapore. https://doi.org/10.1007/978-981-19-0011-2_41
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