Abstract: Objective: On 18th December 2023, an Ms 6.2 earthquake struck Jishishan County, Gansu Province, China, which triggered a large number of coseismic landslides and caused varying degrees of buildings damage, leading to serious casualties and economic losses. Timely acquisition of the coseismic landslide susceptibility, emergency identification of coseismic landslides and building damage, as well as analysis of influencing factors related to coseismic landslides, are crucial for post-disaster emergency rescue and recovery efforts. Methods: The support vector machine algorithm was employed to acquire the spatial probability distribution of coseismic landslide susceptibility in the Jishishan earthquake. Emergency identification of coseismic landslides was conducted using high-resolution optical satellite imagery before and after the earthquake. Furthermore, a comprehensive analysis was undertaken by analyzing the impact of seismic, topographic, geomorphic, and human activity factors on coseismic landslides. Additionally, by using the multi-temporal interferometric synthetic aperture radar(InSAR） coherence change method, a building damage proxy map (BDPM) was generated to assess earthquake-induced structural damage. Results: The Ms 6.2 Jishishan Earthquake triggered 3 767 coseismic landslides in the region with an area of 9.67 km². The majority of these landslides were composed of loess and were predominantly occurred in the region with an elevations range of 1 900-2 200 m, slope range of 20° -40°, southeast orientation, locating approximately 10 km from faults and 2.2 km from the river. The huge number of loess coseismic landslides reflects the evident amplification effect of loess. Our research group conducted a field trip following the earthquake and confirmed 59 of those coseismic landslides, which verified the accuracy of the remote sensing identification results. In addition, BDPM results indicate that the towns of Dahejia and Guanting within the seismic zone experienced the most severe structural damage. These findings of this study provide crucial data support for post-earthquake rehabilitation and reconstruction as well as assessment of secondary seismic hazards.