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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\u2010based 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\u2010DEM 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\u2009m. 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\u2014achieving a mean Intersection over Union (mIoU) of 85.01% for valley lines\u2014and effectively mitigating high\u2010frequency generative artifacts.<\/jats:p>","DOI":"10.1111\/tgis.70232","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T05:01:31Z","timestamp":1773205291000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature\u2010Driven\n                    <scp>DEM<\/scp>\n                    Generation With Enhanced Detail Preservation and Noise Mitigation Using Conditional\n                    <scp>GANs<\/scp>"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0390-6571","authenticated-orcid":false,"given":"Chenhui","family":"Wu","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering China University of Geosciences  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5328-0881","authenticated-orcid":false,"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics China University of Mining and Technology  Xuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering China University of Geosciences  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/14498596.2011.623348"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-011-2352-8"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00444-8"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs13091854"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-021-08988-1"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs15041038"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2023.105482"},{"key":"e_1_2_11_9_1","first-page":"247","article-title":"Convolutional Neural Network Based Dem Super Resolution","volume":"41","author":"Chen Z.","year":"2016","journal-title":"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110068"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.07.031"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3130800.3130804"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3130191"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs16132283"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1029\/93WR00545"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2024.06.030"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10346-020-01353-2"},{"key":"e_1_2_11_21_1","unstructured":"Kingma D. P.2014.\u201cAdam: A Method for Stochastic Optimization.\u201darXiv preprint arXiv:1412.6980."},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs14051166"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58520-4_26"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00557"},{"key":"e_1_2_11_26_1","first-page":"2359","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Liu J.","year":"2020"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_2_11_28_1","first-page":"1","article-title":"Factseg: Foreground Activation\u2010Driven Small Object Semantic Segmentation in Large\u2010Scale Remote Sensing Imagery","volume":"60","author":"Ma A.","year":"2021","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3288296"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.70124"},{"key":"e_1_2_11_31_1","unstructured":"Mirza M.2014.\u201cConditional Generative Adversarial Nets.\u201darXiv preprint arXiv:1411.1784."},{"key":"e_1_2_11_32_1","first-page":"8162","volume-title":"International Conference on Machine Learning","author":"Nichol A. 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