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Technical Note

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

1
Department of Geoscience and Remote Sensing, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands
2
Department of Water Management, Delft University of Technology, 2600 GA Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(5), 1425; https://doi.org/10.3390/s20051425
Received: 29 January 2020 / Revised: 24 February 2020 / Accepted: 27 February 2020 / Published: 5 March 2020
(This article belongs to the Special Issue Remote Sensing of Geohazards)
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. View Full-Text
Keywords: deep-seated landslide; machine learning; remote sensing; early warning systems; hazard assessment deep-seated landslide; machine learning; remote sensing; early warning systems; hazard assessment
MDPI and ACS Style

van Natijne, A.L.; Lindenbergh, R.C.; Bogaard, T.A. Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting. Sensors 2020, 20, 1425. https://doi.org/10.3390/s20051425

AMA Style

van Natijne AL, Lindenbergh RC, Bogaard TA. Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting. Sensors. 2020; 20(5):1425. https://doi.org/10.3390/s20051425

Chicago/Turabian Style

van Natijne, Adriaan L., Roderik C. Lindenbergh, and Thom A. Bogaard. 2020. "Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting" Sensors 20, no. 5: 1425. https://doi.org/10.3390/s20051425

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