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Journal Article

Citation

van Natijne AL, Lindenbergh RC, Bogaard TA. Sensors (Basel) 2020; 20(5): e1425.

Affiliation

Department of Water Management, Delft University of Technology, 2600 GA Delft, The Netherlands.

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s20051425

PMID

32151069

Abstract

Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated landslide deformation and associated processes. Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field. Furthermore, we discuss both the challenges for the integration in an early warning system, and the risks and opportunities arising from the limited physical constraints in machine learning. This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications.


Language: en

Keywords

deep-seated landslide; early warning systems; hazard assessment; machine learning; remote sensing

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