Abstract: Comprehensive remote sensing identification of hidden landslide hazards has been promoted nationwide and has achieved certain results. However, with the introduction of the new prevention and control strategy, "dual control of points and surfaces", new technical challenges have emerged. Focusing on collaborative identification of hidden landslide hazards (points) and potential deformation slopes (surfaces), this study proposed a comprehensive remote sensing technique for "point-surface" collaborative recognition. This technique aims at addressing the identification challenges and improving the identification accuracy of both point-based and surface-based hazards. First, the historical and ongoing deformation characteristics of landslide hazards were examined using interferometric synthetic aperture Radar (InSAR) combined with three-dimensional optical remote sensing. Then, incorporated with unmanned aerial vehicle (UAV) remote sensing, GIS-based spatial analysis of terrain slope, aspect, and stratigraphic lithology was conducted for identifying slopes prone to deformation. Finally, hazardous areas were delineated and key field survey zones were mapped based on the identification platform. Taking Daguan County, Yunnan Province as the study area for geological hazard assessment, this study identified a total of 142 hidden landslide hazards and 236 potential deformation slopes across the county, with a key investigation area delimited covering 582.9 km². Detailed investigation revealed that 127 hidden landslide hazards (72.6%) and 194 potential deformation slopes (82.3%) were located within the key investigation area. Validated by the receiver operating characteristic (ROC) curve, the comprehensive remote sensing point-surface collaborative identification achieved an accuracy of 80.6%. The findings can serve as a reference or theoretical basis for research on similar geological hazards in Yunnan Province.