A New Hybrid Framework for Digital Terrain Modeling Using Sector-Based Neighbor Selection and Neural Network Blending

Abstract. This paper presents a new hybrid framework for digital terrain modeling that combines directional sector-based neighbor selection (DSNS), artificial neural networks (ANN), and gradient-based weighted blending. The framework addresses the spatial imbalance and ripple artifacts commonly seen in interpolation-based terrain models. In the first stage, 12 sector-divided neighbors are selected around each query location to ensure directional balance. Next, ANN models are trained on reference terrains using either expert-adjusted or natural interpolated surfaces, depending on the test region. Finally, a gradient-based weighting mechanism blends ANN outputs with those of linear interpolation to create a coherent and smooth elevation surface. The proposed method is validated on three real-world terrains of varying size and complexity. Results show that the model significantly improves topographic continuity, numerical stability, and generalization across different landscapes. Compared to conventional interpolation methods, the proposed method reduces oscillations, maintains terrain flow, and eliminates the need for manual adjustments. The framework offers a scalable, automated, and accurate approach for terrain surface reconstruction in both regular and anisotropic datasets.