Abstract: In mid- to late-April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal. Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified. After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the north-east-south-west direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides. This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification.