["Transactions in GIS, Volume 30, Issue 2, April 2026. ", "\nABSTRACT\nDespite the widespread application of Digital Elevation Models (DEMs) in Geographic Information Systems (GIS) and terrain analysis, generating high‐fidelity virtual terrain from sketches remains a significant challenge, particularly regarding feature recognition and realistic pattern generation. This paper proposes a novel method based on Conditional Generative Adversarial Networks (CGANs) for virtual terrain synthesis, effectively addressing key issues in feature extraction, denoising, and preservation. Specifically, the Feature Enhancement Module (FEM) leverages deep learning‐based attention mechanisms to refine terrain features, maintaining the integrity of both global and local patterns. The Elevation Denoising Module (EDM) detects and eliminates noise artifacts introduced during generation, while the Collaborative Loss Module (CLM) optimizes feature preservation. Evaluated on a comprehensive dataset of 15,680 sub‐DEM tiles from six geographically distinct regions, our experiments demonstrate the model's effectiveness. Quantitative results show that our method achieves a Root Mean Square Error (RMSE) of 9.38 m. Furthermore, our approach outperforms existing deep learning models, showing performance improvements of 7.3% over Diffusion, 8.6% over FEN, 10.9% over TFaSR, and 15.3% over IETA. Beyond numerical metrics, the proposed model exhibits superior qualitative advantages, particularly in preserving hydrological connectivity—achieving a mean Intersection over Union (mIoU) of 85.01% for valley lines—and effectively mitigating high‐frequency generative artifacts.\n"]