Many geospatial workflows critically depend on high-fidelity 3D point clouds for applications such as change detection, orthophoto generation, and modeling. However, NeRF-generated point clouds often suffer from sampling inefficiencies inherent in the predominant random pixel sampling approach. We identify spatial redundancy as one such inefficiency: random sampling has the inevitable consequence of sampling large, low-texture patches more frequently than detailed, high-frequency textured regions. As a result, low-texture areas tend to be oversampled and other pixels remain unsampled &ndash; regardless of their importance to the reconstruction task. To overcome this, we propose <strong>CENS</strong> (Coverage-Efficient Non-Redundant Sampling), a deterministic pixel sampling strategy that maximizes spatial coverage, eliminates intra-image sample repetition, and ensures reproducibility via structured initialization. Evaluated on the Jamtal valley dataset, CENS achieves comparable geometric accuracy (cloud-to-mesh (C2M) distances: <em>&mu;</em> = - 0.0027 vs. -0.0011 m; <em>&sigma;</em> = 0.027 vs. 0.028 m) using 50% fewer training steps (11,232 vs. 22,464), while yielding 28.2% more points, higher orthophoto fidelity, and improved point cloud completeness. Beyond CENS, we also explored NeRFs for ALS point cloud simulation, achieving realistic occlusion patterns and accuracy within UAV photogrammetry standards (V<em>_RMSE</em> = 24 mm; H<em>_RMSE</em> = 17 mm). Crucially, CENS positions NeRFs as a scalable, practical solution for geospatial point cloud and orthophoto generation, advancing them toward real-world mapping workflows, and integrates seamlessly into NeRFStudio.