We present a resource-efficient framework that enables fast reconstruction and real-time rendering of urban-level scenarios while maintaining robustness against appearance variations across multi-view captures. Our approach begins with scene partitioning for parallel training, employing a visibility-based image selection strategy to optimize resource utilization. A controllable level-of-detail (LOD) strategy regulate the Gaussian density during training and rendering to balance quality, memory efficiency, and performance. The appearance transformation module mitigates inconsistencies across images while enabling flexible adjustments. Additionally, we utilize enhancement modules, such as depth regularization, scale regularization, and anti-aliasing, to improve reconstruction fidelity. Experimental results demonstrate that our method effectively reconstructs urban-scale scenes and outperforms previous approaches in both efficiency and quality.