<p>Digital Elevation Models (DEMs) are fundamental to engineering projects, influencing the accuracy of hydrologic modeling, earthwork calculations, and infrastructure design. The resolution and quality of a DEM are primarily determined by the density of survey points and the interpolation algorithm used. This study presents a comparative evaluation of four common interpolation techniques—Natural Neighbor (NN), Kriging, Inverse Distance Weighting (IDW), and Spline—to generate a high-accuracy local DEM for a bare land area, typical of civil engineering project sites, on the Najran University campus, Saudi Arabia. A total of 7,026 high-precision GPS points were collected and divided into training (80%) and validation (20%) datasets. The vertical accuracy was assessed using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The results demonstrated that the Natural Neighbor interpolation method achieved superior performance with the lowest RMSE of 0.124&#xa0;m and the highest R² of 0.969. Critically, the study evaluated the impact of data density by thinning the training dataset by 0% to 75%. It was found that a 75% reduction in data points—which equates to a significant saving in surveying time and cost—increased the RMSE by only ~ 2&#xa0;cm when using the NN algorithm. This finding indicates that the Natural Neighbor method is not only the most accurate but also the most robust and cost-effective solution for generating reliable DEMs. The outcomes of this research provide a practical framework for engineers to optimize surveying efforts and produce high-fidelity terrain models essential for precise earthwork volume calculation, drainage design, and flood risk assessment in local-scale projects.</p>