CONVOLUTIONAL NEURAL NETWORK BASED DEM SUPER RESOLUTION

31 citations as recorded by crossref.

1. T2-cGAN: A new modeling paradigm for joint DEM spatial interpolation and super-resolution reconstruction Z. Huo et al. https://doi.org/10.1016/j.neucom.2025.131181
2. DDMAFN: A Progressive Dual-Domain Super-Resolution Network for Digital Elevation Model Based on Multi-Scale Feature Fusion B. He et al. https://doi.org/10.3390/electronics13204078
3. Super-resolution reconstruction of the 1 arc-second Australian coastal DEM dataset B. Zhang et al. https://doi.org/10.1080/10095020.2025.2487143
4. Sliding Windows Method Based on Terrain Self-Similarity for Higher DEM Resolution in Flood Simulating Modeling Q. Yin et al. https://doi.org/10.3390/rs13183604
5. JSPSR: Joint Spatial Propagation Super-Resolution Networks for Enhancement of Bare-Earth Digital Elevation Models from Global Data X. Cai & M. Wilson https://doi.org/10.3390/rs17213591
6. LIE-DSM: Leveraging Single Remote Sensing Imagery to Enhance Digital Surface Model Resolution S. Zhang et al. https://doi.org/10.1109/TGRS.2024.3408793
7. Multi-modal DEM super-resolution using relative depth: A new benchmark and beyond W. Huang et al. https://doi.org/10.1016/j.jag.2025.104865
8. TTSR: A Transformer-Based Topography Neural Network for Digital Elevation Model Super-Resolution Y. Wang et al. https://doi.org/10.1109/TGRS.2024.3360489
9. Terrain Self-Similarity-Based Transformer for Generating Super Resolution DEMs X. Zheng et al. https://doi.org/10.3390/rs15071954
10. D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks B. Demiray et al. https://doi.org/10.1007/s42979-020-00442-2
11. Research on High-Resolution Modeling of Satellite-Derived Marine Environmental Parameters Based on Adaptive Global Attention R. Cui et al. https://doi.org/10.3390/rs17040709
12. GKTLF: a geographical knowledge-guided transfer learning framework for cross-regional DEM super-resolution X. Han et al. https://doi.org/10.1080/13658816.2026.2704882
13. An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs A. Zhou et al. https://doi.org/10.3390/rs13163089
14. Unmixing frequency features for DEM super resolution Z. Wen et al. https://doi.org/10.1016/j.isprsjprs.2025.07.039
15. A Topography-Structure-Aware and Similarity-Enhanced Network for DEM Super-Resolution K. Hu et al. https://doi.org/10.1109/JSTARS.2026.3732317
16. A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning X. Lin et al. https://doi.org/10.3390/rs14092181
17. An ensemble learning framework for generating high-resolution regional DEMs considering geographical zoning X. Han et al. https://doi.org/10.1016/j.isprsjprs.2025.02.007
18. Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data Y. Cai et al. https://doi.org/10.3390/rs15051359
19. Super-Resolution Remote Sensing Datasets for Application to Caral–Supe Archeological Sites Employing SAR and DEMs J. Kim & R. Singh https://doi.org/10.3390/rs18060854
20. Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks Y. Zhang & W. Yu https://doi.org/10.3390/s22030745
21. DEM super-resolution framework based on deep learning: decomposing terrain trends and residuals H. Wang et al. https://doi.org/10.1080/17538947.2024.2356121
22. Deep learning methods applied to digital elevation models: state of the art J. Ruiz-Lendínez et al. https://doi.org/10.1080/10106049.2023.2252389
23. A Global-Information-Constrained Deep Learning Network for Digital Elevation Model Super-Resolution X. Han et al. https://doi.org/10.3390/rs15020305
24. Super-Resolution Reconstruction of Seafloor Topography Using ViT-GAN Model and 3-Arcsecond Multibeam Bathymetric Data Z. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3716897
25. A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs X. Zheng et al. https://doi.org/10.3390/rs16193676
26. Real-World DEM Super-Resolution Based on Generative Adversarial Networks for Improving InSAR Topographic Phase Simulation Z. Wu et al. https://doi.org/10.1109/JSTARS.2021.3105123
27. Exploring the integration of auxiliary modal information from remote sensing images for DEM super-resolution B. Gao et al. https://doi.org/10.1080/13658816.2025.2506123
28. Super-resolution reconstruction of a digital elevation model based on a deep residual network D. Jiao et al. https://doi.org/10.1515/geo-2020-0207
29. An Enhanced Residual Feature Fusion Network Integrated with a Terrain Weight Module for Digital Elevation Model Super-Resolution G. Chen et al. https://doi.org/10.3390/rs15041038
30. A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling W. Tan et al. https://doi.org/10.1016/j.rse.2023.113956
31. A Seabed Terrain Feature Extraction Transformer for the Super-Resolution of the Digital Bathymetric Model W. Cai et al. https://doi.org/10.3390/rs15204906