van Natijne Adriaan L, Lindenbergh Roderik C, Bogaard Thom A
Department of Geoscience and Remote Sensing, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands.
Department of Water Management, Delft University of Technology, 2600 GA Delft, The Netherlands.
Sensors (Basel). 2020 Mar 5;20(5):1425. doi: 10.3390/s20051425.
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.
滑坡灾害的临近预报和早期预警系统大多是在斜坡或集水区尺度上实施的。这些系统在区域尺度或偏远地区往往难以实施。机器学习和卫星遥感产品为局部和区域监测深层滑坡变形及相关过程提供了新机遇。在此，我们列出了滑坡过程的关键变量、相关的卫星遥感产品，以及可用的机器学习算法及其目前在该领域的应用。此外，我们还讨论了将其集成到早期预警系统中所面临的挑战，以及机器学习中有限物理约束所带来的风险和机遇。本综述表明，数据产品和算法是可用的，并且该技术已准备好用于区域应用测试。
