Machine learning model doubles accuracy of global landslide 'nowcasts'

Every year, landslides—the movement of rock, soil, and debris down a slope—cause thousands of deaths, billions of dollars in damages, and disruptions to roads and power lines. Because terrain, characteristics of the rocks and soil, weather, and climate all contribute to landslide activity, accurately pinpointing areas most at risk of these hazards at any given time can be a challenge. Early warning systems are generally regional—based on region-specific data provided by ground sensors, field observations, and rainfall totals. But what if we could identify at-risk areas anywhere in the world at any time?