Deep learning can predict global earthquake-triggered landslides

Earthquake-triggered (coseismic) landsliding is among the most lethal of disasters, and rapid response is crucial to prevent cascading hazards that further threaten lives and infrastructure. Current prediction approaches are limited by oversimplified physical models, regionally focused databases, and retrospective statistical methods, which impede timely and accurate hazard assessments. To overcome these constraints, we developed the first comprehensive global database of ∼400 000 landslides associated with 38 of the most catastrophic earthquakes over the past 50 years. Leveraging this extensive dataset, we developed advanced deep-learning models that predict the probability of landsliding for any earthquake worldwide with an average spatial accuracy of ∼82% in less than a minute, without relying on prior local knowledge. Our framework enables swift disaster evaluation during the critical early hours following an earthquake while also enhancing pre-event hazard planning. This study offers a scalable and efficient tool to mitigate the catastrophic impacts of earthquake-triggered landslides, representing a transformative advance in global geohazard prediction.