Highlights What are the main findings? An SBAS-InSAR post-processing workflow integrating velocity direction conversion and Z-score clustering was developed for classifying the state of landslide activity across the Loess Plateau. Conversion of the LOS direction velocity vector into a slope direction is used to identify and monitor the landslides' displacement change. The Z-score clustering method is applied to group measurement points for classification, thereby enhancing efficiency, albeit with a reduction in classification completeness. This work combined time series displacement with precipitation to analyze the kinematic characteristics of the landslides, and used historical optical images for verification. What are the implications of the main findings? The experiment shows that open and easily accessible Sentinel-1 data can be one data source for identifying and monitoring landslides. The SBAS-InSAR post-processing workflow, incorporating velocity direction conversion and Z-score clustering, is an effective method for updating landslide activity across most of the Loess Plateau, except in forested areas. The results suggest that the creep deformation of landslides was highly sensitive to seasonal rainfall.Highlights What are the main findings? An SBAS-InSAR post-processing workflow integrating velocity direction conversion and Z-score clustering was developed for classifying the state of landslide activity across the Loess Plateau. Conversion of the LOS direction velocity vector into a slope direction is used to identify and monitor the landslides' displacement change. The Z-score clustering method is applied to group measurement points for classification, thereby enhancing efficiency, albeit with a reduction in classification completeness. This work combined time series displacement with precipitation to analyze the kinematic characteristics of the landslides, and used historical optical images for verification. What are the implications of the main findings? The experiment shows that open and easily accessible Sentinel-1 data can be one data source for identifying and monitoring landslides. The SBAS-InSAR post-processing workflow, incorporating velocity direction conversion and Z-score clustering, is an effective method for updating landslide activity across most of the Loess Plateau, except in forested areas. The results suggest that the creep deformation of landslides was highly sensitive to seasonal rainfall.Abstract This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions in China due to frequent rains, strong topographical gradients and severe soil erosion. By constructing subsets of interferograms, SBAS-InSAR can mitigate the influence of decorrelation to a certain extent, making it a highly effective technique for monitoring regional surface deformation and identifying landslides. To overcome the limitations of the satellite's one-dimensional Line-of-Sight (LOS) measurements and the challenge of distinguishing true landslide signals from noise, two optimization strategies were implemented. First, LOS velocities were projected onto the local steepest slope direction, assuming translational movement parallel to the slope. Second, a Z-score clustering algorithm was employed to aggregate measurement points with consistent kinematic signatures, enhancing identification robustness, with a slight trade-off in spatial completeness. Based on 205 Sentinel-1 Single-Look Complex (SLC) images acquired from 2014 to 2024, the integrated workflow identified 69 "active, very slow" and 63 "active, extremely slow" landslides. These results were validated through high-resolution historical optical imagery. Time series analysis reveals that creep deformation in this region is highly sensitive to seasonal rainfall patterns. This study demonstrates that the SBAS-InSAR post-processing framework provides a cost-effective, millimeter-scale solution for updating landslide inventories and supporting regional risk management and early warning systems in loess-covered terrains, with the exception of densely forested areas.