Deep learning approaches for landslide information recognition: Current scenario and opportunities
Author:
Funder
Department of Science and Technology, Science and Engineering Research Board, New Delhi
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12040-024-02281-8.pdf
Reference199 articles.
1. Abraham M T, Satyam N, Pradhan B, Segoni S and Alamri A 2022 Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring; Geosci. J. 26(2) 289–301, https://doi.org/10.1007/s12303-021-0026-2.
2. Amarasingam N, Ashan Salgadoe A S, Powell K, Gonzalez L F and Natarajan S 2022 A review of UAV platforms, sensors, and applications for monitoring of sugarcane crop; Remote Sens. Appl.: Soc. Environ. 26 100712, https://doi.org/10.1016/j.rsase.2022.100712.
3. Amatya P, Kirschbaum D, Stanley T and Tanyas H 2021 Landslide mapping using object-based image analysis and open source tools; Eng. Geol. 282 106000, https://doi.org/10.1016/j.enggeo.2021.106000.
4. Bai D, Tang J, Lu G, Zhu Z, Liu T and Fang J 2020a The design and application of landslide monitoring and early warning system based on microservice architecture; Geomat. Nat. Hazards Risk 11(1) 1, https://doi.org/10.1080/19475705.2020.1766580.
5. Barbu M, Radoi A and Suciu G 2020 Landslide monitoring using convolutional autoencoders; 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), https://doi.org/10.1109/ECAI50035.2020.9223121.
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