| 01 Sep 2025
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.
This manuscript presents a well-motivated and carefully executed contribution to digital terrain modeling by combining a directional sector–based neighbor selection with an ANN regressor and a gradient-based blending strategy. The evaluation over three real sites against established baselines (NetCAD TIN, MATLAB linear, MATLAB natural) is convincing, and the writing is clear. In my view, the paper is suitable for publication in its current form.
Two minor, optional suggestions could further strengthen the presentation without being prerequisites for acceptance. First, in the Results and Discussion section, directly below Table 3, adding a single, concise sentence that summarizes the uncertainty and significance checks (e.g., that 95% bootstrap CIs were computed for RMSE/MAE and paired comparisons showed inter-method differences to be statistically significant) would make the quantitative conclusions more explicit. If desired, a brief, number-free nod to these checks can also be included in the Conclusion to close the loop, but this is optional.
Second, for polish, I recommend a quick pass for terminology consistency limited to method names—use a single convention for MATLAB linear and MATLAB natural throughout (avoid mixing “Linear/Natural” vs “linear/natural”). This small edit will improve stylistic uniformity.
Overall, I enjoyed reading the paper. The method is practical, the validation is sound, and the manuscript is well aligned with GMD’s scope. The optional tweaks above would only enhance clarity; the work is acceptable as is.