Abstract: Visualizing Digital Elevation Model (DEM) as hillshade maps is a widely used method for remote sensing identification of geological hazards. However, existing hillshade maps are limited by single-directional light sources, which can result in misjudgments or omissions of geological information during interpretation. To overcome this issue, this study selects three visualization factors—Sky-View Factor (SVF), Openness, and Slope Gradient—that are not influenced by light direction, and integrates them through various blending modes into a single layer, forming an improved visualization enhancement method called SOG (Slope-Openness-SVF Group). This method is applied in the identification and information extraction of landslides, collapses, and debris flows. The results show that compared to conventional DEM visibility methods, the SOG enhancement method provides better visual enhancement. Moreover, feature extraction using edge detection reveals that the SOG layer identifies significantly more micro-topographic features of geological hazards than other visibility layers. Therefore, the SOG enhanced display method addresses problems such as data redundancy, storage inefficiency, complicated steps, and suboptimal results during the visualization process, offering a new approach for the effective identification of geological hazard micro-topographic features.