Artificial Intelligence Techniques for Landslides Prediction Using Satellite Imagery
DOI:
https://doi.org/10.32628/IJSRSET2512311Keywords:
Landslide classification, satellite image classification, support vector machine, fuzzy based classification, landslide prediction, landcover classificationAbstract
Landslides in hilly areas can be triggered by natural factors like heavy rainfall and earthquakes, or by human activities such as unplanned construction. These events often result in significant loss of life and property. Machine learning (ML) and deep learning (DL) algorithms have been increasingly used for automatic landslide detection from satellite images. While there has been progress in semiautomatic detection, fully automatic systems with high accuracy are still limited. One of the biggest challenges is the lack of appropriate training datasets. This study reviews various ML and DL techniques for landslide classification and identifies research gaps. It proposes a novel prototype using a modified ResNet101 deep learning model, achieving an accuracy of 96.88% on an augmented Beijing satellite dataset. The findings offer valuable insights for future research in landslide detection and classification using satellite imagery
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References
A. K. Turner, ‘‘Social and environmental impacts of landslides,’’ Innov. Infrastruct. Solutions, vol. 3, no. 1, Dec. 2018, Art. no. 70, doi: 10.1007/s41062-018-0175-y.
Landslide Atlas. emphGeological Survey of India. Accessed: Dec. 12, 2023. [Online]. Available: https://www.gsi.gov.in
N. Singh, S. K. Gupta, and D. P. Shukla, ‘‘Analysis of landslide reactivation using satellite data: A case study of Kotrupi landslide, Mandi, Himachal Pradesh, India,’’ Int. Arch. Photogramm., Remote Sens. Spatial Inf. Sci., vol. XLII-3, pp. 137–142, Feb. 2020, doi: 10.5194/isprs-archives-xlii-3- w11-137-2020.
W. Pollock and J. Wartman, ‘‘Human vulnerability to landslides,’’ GeoHealth, vol. 4, no. 10, Oct. 2020, Art. no. e2020GH000287, doi: 10.1029/2020gh000287.
D. Petley, ‘‘Global patterns of loss of life from landslides,’’ Geology, vol. 40, no. 10, pp. 927–930, Oct. 2012, doi: 10.1130/g33217.1.
B. Koley, A. Nath, S. Saraswati, S. Bhattacharya, B. C. Ray, T. Choudhury, and J.-S. Um, ‘‘Landslide hazard zones differentiated according to thematic weighting: Road alignment in North Sikkim Himalayas, India,’’ Spatial Inf. Res., vol. 32, no. 1, pp. 29–46, Feb. 2024, doi: 10.1007/s41324-023-00533- 1.
A. Goel, A. K. Goel, and A. Kumar, ‘‘The role of artificial neural network and machine learning in utilizing spatial information,’’ Spatial Inf. Res., vol. 31, no. 3, pp. 275–285, Jun. 2023, doi: 10.1007/s41324-022-00494-x.
A. Sharma, K. K. Sharma, and S. G. Sapate, ‘‘A prototype model for detection and classification of landslides using satellite data,’’ J. Phys., Conf. Ser., vol. 2327, no. 1, 2022, Art. no. 012029, doi: 10.1088/1742- 6596/2327/1/012029.
S. Dridi, ‘‘Unsupervised learning—A systematic literature review,’’ Tech. Rep., 2021, doi: 10.13140/RG.2.2.16963.12323.
N. Casagli, F. Cigna, S. Bianchini, D. Hölbling, P. Füreder, G. Righini, S. D. Conte, B. Friedl, S. Schneiderbauer, C. Iasio, J. Vlcko, V. Greif, H. Proske, K. Granica, S. Falco, S. Lozzi, O. Mora, A. Arnaud, F. Novali, and M. Bianchi, ‘‘Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER,’’ Remote Sens. Appl., Soc. Environ., vol. 4, pp. 92–108, Oct. 2016, doi: 10.1016/j.rsase.2016.07.001.
Dixit, S., & Jangid, J. (2024). Asynchronous SCIM profile for security event tokens. Journal of Computational Analysis and Applications, 33(6), 1357–1371. https://eudoxuspress.com/index.php/pub/article/view/1935
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