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

Sensors
Adriaan L van NatijneThom A Bogaard

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.

References

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Jun 26, 2004·Science·George E HilleyFabio Rocca
Aug 8, 2009·Science·Pedro A SanchezGan-Lin Zhang
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Aug 11, 2017·PloS One·Christopher Barrington-Leigh, Adam Millard-Ball
May 10, 2018·Scientific Reports·Federico RaspiniNicola Casagli
Mar 22, 2018·Earth's Future·Dalia Kirschbaum, Thomas Stanley
Mar 14, 2019·International Journal of Digital Earth·Martin SudmannsThomas Blaschke
May 1, 2017·Natural Hazards·Thomas Stanley, Dalia B Kirschbaum

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Software Mentioned

IMERG
MERIT
CORINE
OpenStreetMap
DEM90
ELS
EarthEnv
ASTER
DAT
GDEM

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