Dataset for Geological Hazard Identification along the G109 Qinghai-Tibet Highway based on Time-Series InSAR Technology

This dataset is derived using time-series InSAR technology (SBAS-InSAR) and wide-swath interferometric mode SAR images provided by the European Space Agency's (ESA) Sentinel-1A satellite, covering the entire length of the G109 Qinghai-Tibet Highway from January 2023 to May 2025. It provides the average annual deformation rate for the areas along the G109 corridor. Based on the annual deformation rate, the spatial settlement gradient field of the study area was extracted using the Sobel operator. Two thresholds were applied for effective identification of high-risk areas exhibiting significant deformation and intense spatial variation in deformation (which may indicate uneven settlement, landslide boundaries, etc.): an absolute deformation rate greater than 10 mm/year or a spatial gradient magnitude greater than 1.6 mm/year/m. The resulting identified geohazard zones along the G109 Qinghai-Tibet Highway were output as binary and vectorized data.The dataset adopts the China Geodetic Coordinate System 2000 (CGCS 2000), with an EPSG code of 4490. The raster data for the surface deformation rate and the spatial gradient magnitude of the deformation rate along the G109 corridor are stored in GeoTIFF format, named "Deformation_Rate_G109_2023-2025.tif" and "Gradient_Magnitude_G109_2023-2025.tif" respectively. The pixel values in the deformation rate raster represent mm·year⁻¹, where negative values indicate movement away from the satellite (typically interpreted as subsidence) and positive values indicate movement towards the satellite (typically interpreted as uplift). The vector polygon data identifying potential geohazards along G109, derived based on the dual-criteria, is stored in SHP format and named "Risk_Zones_G109_2023-2025.shp".During data processing, precise orbit ephemerides and the COP-DEM GLO30 topographic data were utilized. Combined with linear elevation fitting and spatial filtering, this approach effectively suppressed non-geological deformation signals. Transitions in multi-track stitching areas are smooth, and the distribution of subsidence rates conforms to algorithmic assumptions, ensuring reliable data accuracy. This dataset systematically reveals the characteristics of surface deformation induced by permafrost degradation along the G109 highway, providing a scientific basis for road subgrade disease identification, permafrost engineering research, and traffic management.