Objectives  On 5th September 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province, China. This earthquake triggered extensive geological hazards in the mountainous area, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of the induced geological hazards is crucial for emergency decision-making and rescue after an earthquake.

  Methods  Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtained the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle(UAV)and satellite remote sensing images, machine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km² were interpreted. Finally, the model was optimized by integrating these landslide data, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved.

  Results  The results show that the coseismic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geological hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue.

  Conclusions  Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital information of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake.