Existing NeRF-based methods for reconstructing large-scale scenes face challenges in visual quality and rendering speed due to spectral biases and extensive sampling requirements. Recent 3DGS-based methods for real-time rendering of 3D objects and small scenes outperform NeRF, but several issues persist when extending these techniques to large-scale scenes. These include robust rendering in weak-texture areas, effective densification under memory constraints, finer detail rendering, and achieving natural lighting transitions. To address these, we introduce a geometry-aware 3DGS method for efficient real-time rendering of large scenes. First, we propose a geometry-guided anchor point initialization that reduces noise away from structural surfaces and generates new points in weak texture areas, particularly for large-scale datasets. We also present a structure-aware joint densification strategy combining surface- and curvature-based densification, ensuring Gaussian points are near structural surfaces and increasing density in low-curvature areas. Additionally, we propose a hash grid-assisted, viewpoint-sensitive feature enhancement scheme to improve detail rendering and natural lighting transitions. Our method achieves superior rendering quality compared to state-of-the-art methods while maintaining reasonable memory usage. Extensive experiments across 16 scenes, including 11 from five public datasets (MatrixCity-Aerial, Mill-19, Tanks & Temples, WHU, and UrbanScene3D) and five self-collected scenes from SCUT-CA and plateau regions, demonstrate its generalization capability. Codes are available athttps://github.com/SCUT-BIP-Lab/Geo_gs.