Hill-shading, also referred to as relief shading, is an important visualization method in small- and medium-scale topographic mapping. Although analytical hill-shading methods offer high generation efficiency, they often suffer from terrain detail overload. Some deep learning generative models have introduced terrain priors, but they still have deficiencies in the controllable representation and reasonable simplification of terrain structures. This study proposes a Swiss-style hill-shading method based on hierarchical terrain structure lines. By introducing ridgelines with hierarchical semantics as priors, a synergistic regulation mechanism of enhancement and suppression is constructed to emphasize major terrain structures while moderately weakening secondary details. The model adopts a hybrid architecture integrating a Transformer encoder and a convolutional decoder to model global terrain structures. In the testing phase, by adjusting the combinations of different levels of ridgelines, the controllable adjustment of the level of detail and structural significance in hill-shading representation is realized. Multi-region experimental results show that the proposed method can generate hill-shading results with clear structural hierarchies and distinct primary–secondary relationships. Compared with other models, the proposed method demonstrates more stable performance in achieving reasonable hierarchical representation of terrain structures and detail simplification, approaching the visual style of Swiss-style manual hill-shading.

Terrain generalization—the scale-driven abstraction of digital elevation models (DEMs) that retains dominant landforms while filtering trivial microtopography—has long been dominated by rule-based algorithms, whereas deep learning has mainly targeted the inverse task of DEM super-resolution. This study proposes TG-CNN, a feature-fusion convolutional neural network for DEM down-scaling generalization. The network compresses the high-resolution DEM through a max- or average-pooling front end, combines normalized elevation with slope and sine–cosine circularly encoded aspect as complementary input channels, and is trained with a composite elevation–slope–aspect loss. Training, validation, and testing are conducted on geographically independent mountainous areas under a leave-one-area-out scheme, followed by adjustable IDW fusion post-processing. On the test region, classical bilinear interpolation retains the best elevation accuracy, while the standalone TG-CNN does not reach a comparable level; fused accuracy improves monotonically as the IDW share increases, indicating that the fusion benefit originates from the interpolation component. On the ridge–valley structure, however, TG-CNN behaves as an aggressive yet reliable filter: it achieves the highest precision and the smallest positional offset among all compared methods but retains only part of the reference skeleton, leaving its F1-score below the interpolation baseline. TG-CNN thus realizes a genuine, controllable terrain-abstraction effect, yet deep learning does not yet surpass mature interpolation in pixel-level elevation accuracy—a negative result that clarifies current limitations and points toward residual learning and information-preserving architectures.