Under the influence of extreme climatic conditions, landslide disasters occur frequently in the Loess Plateau due to complex geological structures, loose soil, and frequent intense rainfall. These events are often concealed, posing significant challenges for disaster prevention. High-resolution optical remote sensing combined with field surveys can improve identification accuracy; however, concerns persist regarding issues such as omission and misidentification during hazard identification and monitoring processes. To address these challenges, this study proposes an integrated remote-sensing identification approach, focusing specifically on the central region of Tianshui, a typical landslide-prone area within the Loess Plateau. Utilizing Sentinel-1 and JL1LF01A remote-sensing imagery collected from 2022 to 2023, we conducted ground deformation monitoring through the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. By integrating deformation results with optical imagery features indicative of potential landslide sites, a comprehensive identification method was developed to precisely detect potential landslide hazards. Verification of the identified sites was subsequently performed using the Google Earth platform, resulting in the establishment of a final dataset of potential landslide hazards within the study area. This outcome clearly demonstrates the high applicability and accuracy of the integrated remote-sensing identification method in the context of landslide hazard assessment. Furthermore, this research provides a solid scientific foundation for geological hazard identification efforts and plays a critical guiding role in disaster prevention and mitigation in Tianshui City, thereby enhancing the region's capacity to withstand disaster risks and effectively safeguarding local lives and property.