Big slopes, little data: data-driven nowcasting of deep-seated landslide deformation

Landslides are a major geohazard in hilly and mountainous environments. We focus on slow-moving, deep-seated landslides that are characterized by gradual, non-catastrophic deformations of millimeters to decimeters per year and cause extensive economic damage. To assess their potential impact and for the design of mitigation solutions, a detailed...

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

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...

System design for ARTEMIS environmental monitoring

This paper describes aspects of system design applied to the ARTEMIS environmental monitoring system of the Food and Agriculture Organization (FAO) of the United Nations. The design aspects are derived from characteristics of the monitoring function and are based on methods of rainfall and vegetation monitoring from satellite observations which...