Machine learning: New potential for local and regional deep-seated landslide nowcasting

van Natijne, A.L.; Lindenbergh, R.C.; Bogaard, T.A.

doi:10.3390/s20051425

Machine learning

Title

Machine learning: New potential for local and regional deep-seated landslide nowcasting

Author

van Natijne, A.L. (TU Delft Optical and Laser Remote Sensing)
Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing)
Bogaard, T.A. (TU Delft Water Resources)

Date

2020

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.

Subject

Deep-seated landslide
Early warning systems
Hazard assessment
Machine learning
Remote sensing

To reference this document use:

http://resolver.tudelft.nl/uuid:6e70a980-40f6-461c-b74a-408bea1b13b8

DOI

https://doi.org/10.3390/s20051425

ISSN

1424-8220

Source

Sensors, 20 (5)

Part of collection

Institutional Repository