A Review of Digital Elevation Model Super Resolution

References

[1] Airbus. (2020). Copernicus DEM Copernicus Digital Elevation Model Validation Report.

[2] Argudo, O., Chica, A., & Andujar, C. (2018). Terrain super-resolution through aerial imagery and fully convolutional networks. Computer Graphics Forum, 37(2), 101–110. https://doi.org/10.1111/cgf.13345

[3] Chaplot, V., Darboux, F., Bourennane, H., Leguédois, S., Silvera, N., & Phachomphon, K. (2006). Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology, 77(1–2), 126–141.

[4] Chen, Z., Wang, X., Xu, Z., & Hou, W. (2016). Convolutional neural network based dem super resolution. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 41, 247–250. https://doi.org/10.5194/isprsarchives-XLI-B3-247-2016

[5] Clabaut, É., Lemelin, M., Germain, M., Bouroubi, Y., & St-Pierre, T. (2021). Model specialization for the use of esrgan on satellite and airborne imagery. Remote Sensing, 13(20). https://doi.org/10.3390/rs13204044

[6] Demiray, B. Z., Muhammed Sit, ·, & Demir, · Ibrahim. (2021). D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks. SN Computer Science, 2(48). https://doi.org/10.1007/s42979-020-00442-2

[7] Demiray, B. Z., Sit, M., & Demir, I. (2020). D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks. http://arxiv.org/abs/2004.04788

[8] Dong, C., Loy, C. C., He, K., & Tang, X. (2014). Learning a deep convolutional network for image super-resolution. In D. Fleet (Ed.), ECCV 2014: Vol. 8692 LNCS (Issue PART 4, pp. 184–199). Springer international Publishing .

[9] Fischer, M. M. (1997). Computational neural networks: a new paradigm for spatial analysis. Environment and Planning A, 30, 1873–1891.

[10] Garcia, G. P. B., & Grohmann, C. H. (2019). DEM-based geomorphological mapping and landforms characterization of a tropical karst environment in southeastern Brazil. Journal of South American Earth Sciences, 93, 14–22.

[11] García, S., Luengo, J., & Herrera, F. (2015). Data Preprocessing in Data Mining. Springer International Publishing . http://www.springer.com/series/8578

[12] Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. http://www.github.com/goodfeli/adversarial

[13] Guo, H., Goodchild, M. F., & Annoni, A. (2020). Manual of Digital Earth.

[14] GURIN, J., MANLEY, L., & PETROV, O. (2015, August 26). World Bank Blogs. New Discussion Paper: How Open Data Can Drive Sustainable Development. https://blogs.worldbank.org/digital-development/new-discussion-paper-how-open-data-can-drive-sustainable-development

[15] He, Y., Song, Z., & Liu, Z. (2017). Updating highway asset inventory using airborne LiDAR. Measurement, 104, 132–141. https://doi.org/10.1016/J.MEASUREMENT.2017.03.026

[16] Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 79(8), 2554–2558. https://doi.org/10.1073/PNAS.79.8.2554

[17] Huang, S., Sun, J., Yang, Y., Fang, Y., & Lin, P. (2017). Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field. IEEE Access, 5, 21669–21683. https://doi.org/10.1109/ACCESS.2017.2757239

[18] Jiang, L., Hu, Y., Xia, X., Liang, Q., Soltoggio, A., & Kabir, S. R. (2020). A multi-scale mapping approach based on a deep learning CNN model for reconstructing high-resolution urban DEMs. Water (Switzerland), 12(5). https://doi.org/10.3390/w12051369

[19] Jiao, D., Wang, D., Lv, H., & Peng, Y. (2020). Super-resolution reconstruction of a digital elevation model based on a deep residual network. Open Geosciences, 12(1), 1369–1382. https://doi.org/10.1515/geo-2020-0207

[20] Kubade, A. A., Sharma, A., & Rajan, K. S. (2020). Feedback Neural Network Based Super-Resolution of DEM for Generating High Fidelity Features. International Geoscience and Remote Sensing Symposium (IGARSS), 1671–1674. https://doi.org/10.1109/IGARSS39084.2020.9323310

[21] Lai, W. S., Huang, J. bin, Ahuja, N., & Yang, M. H. (2019). Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(11), 2599–2613. https://doi.org/10.1109/TPAMI.2018.2865304

[22] Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature 2015 521:7553, 521(7553), 436–444. https://doi.org/10.1038/nature14539

[23] Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., & Shi, W. (2017). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (pp. 4681–4690).

[24] Liu, S., Wang, M., Huang, Q., & Liu, X. (2021). Robust multi-frame super-resolution based on adaptive half-quadratic function and local structure tensor weighted BTV. Sensors, 21(16). https://doi.org/10.3390/s21165533

[25] Mandic, D. P., & Chambers, J. A. (2001). Recurrent neural networks for prediction : learning algorithms, architectures, and stability. John Wiley.

[26] Nguyen, Q. M., Nguyen, T. T. H., La, P. H., Lewis, H. G., & Atkinson, P. M. (2019). Downscaling Gridded DEMs Using the Hopfield Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(11), 4426–4437. https://doi.org/10.1109/JSTARS.2019.2953515

[27] Salvador, Jordi. (2017). Example-based Super Resolution. Academic Press.

[28] Sankey, T. T., McVay, J., Swetnam, T. L., McClaran, M. P., Heilman, P., & Nichols, M. (2018). UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sensing in Ecology and Conservation, 4(1), 20–33. https://doi.org/10.1002/RSE2.44

[29] Sharma, S., Sharma, S., & Athaiya, A. (2020). ACTIVATION FUNCTIONS IN NEURAL NETWORKS. International Journal of Engineering Applied Sciences and Technology, 4, 310–316. http://www.ijeast.com

[30] Shin, D., & Spittle, S. (2019). LoGSRN: Deep Super Resolution Network for Digital Elevation Model. https://doi.org/10.0/Linux-x86_64

[31] Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1874–1883.

[32] Shi, W. Z., Li, Q. Q., & Zhu, C. Q. (2005). Estimating the propagation error of DEM from higher-order interpolation algorithms. International Journal of Remote Sensing, 26(14), 3069–3084. https://doi.org/10.1080/01431160500057905

[33] Sørensen, R., & Seibert, J. (2007). Effects of DEM resolution on the calculation of topographical indices: TWI and its components. Journal of Hydrology, 347(1–2), 79–89. https://doi.org/10.1016/j.jhydrol.2007.09.001

[34] Tsai, R., & Huang, T. S. (1984). Multiframe image restoration and registration. Advance Computer Visual and Image Processing, 317–339.

[35] Vanthof, V., & Kelly, R. (2019). Water storage estimation in ungauged small reservoirs with the TanDEM-X DEM and multi-source satellite observations. Remote Sensing of Environment, 235, 111437. https://doi.org/10.1016/J.RSE.2019.111437

[36] Wu, Z., & Ma, P. (2020). ESRGAN-based DEM super-resolution for enhanced slope deformation monitoring in lantau island of Hong Kong. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43(B3), 351–356. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-351-2020

[37] Wu, Z., Zhao, Z., Ma, P., & Huang, B. (2021). Real-World DEM Super-Resolution Based on Generative Adversarial Networks for Improving InSAR Topographic Phase Simulation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 8373–8385. https://doi.org/10.1109/JSTARS.2021.3105123

[38] Xu, Z., Chen, Z., Yi, W., Gui, Q., Hou, W., & Ding, M. (2019). Deep gradient prior network for DEM super-resolution: Transfer learning from image to DEM. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 80–90. https://doi.org/10.1016/j.isprsjprs.2019.02.008

[39] Xu, Z., Wang, X., Chen, Z., Xiong, D., Ding, M., & Hou, W. (2015). Nonlocal similarity based DEM super resolution. ISPRS Journal of Photogrammetry and Remote Sensing, 110, 48–54. https://doi.org/10.1016/j.isprsjprs.2015.10.009

[40] Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J. H., & Liao, Q. (2019). Deep Learning for Single Image Super-Resolution: A Brief Review. In IEEE Transactions on Multimedia (Vol. 21, Issue 12, pp. 3106–3121). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/TMM.2019.2919431

[41] Yan, L., Tang, X., & Zhang, Y. (2021). High accuracy interpolation of dem using generative adversarial network. Remote Sensing, 13(4), 1–14. https://doi.org/10.3390/rs13040676

[42] Yue, L., Shen, H., Yuan, Q., & Zhang, L. (2015). Fusion of multi-scale DEMs using a regularized super-resolution method. International Journal of Geographical Information Science, 29(12), 2095–2120. https://doi.org/10.1080/13658816.2015.1063639

[43] Yue, L., Shen, H., Zhang, L., Zheng, X., Zhang, F., & Yuan, Q. (2017). High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations. ISPRS Journal of Photogrammetry and Remote Sensing, 123, 20–34. https://doi.org/10.1016/j.isprsjprs.2016.11.002

[44] Zhang, R., Bian, S., & Li, H. (2021). Rspcn: Super-resolution of digital elevation model based on recursive sub-pixel convolutional neural networks. ISPRS International Journal of Geo-Information, 10(8). https://doi.org/10.3390/ijgi10080501

[45] Zhang, Y., Zheng, Z., Luo, Y., Zhang, Y., Wu, J., & Peng, Z. (2019). A CNN-based subpixel level DSM generation approach via single image super-resolution. Photogrammetric Engineering and Remote Sensing, 85(10), 765–775. https://doi.org/10.14358/PERS.85.10.765

[46] Zhen, L., Jinglei, Y., Zheng, L., Xiaomin, Y., Gwanggil, J., & Wei, W. (2019). Feedback Network for Image Super-Resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3867–3876. https://openaccess.thecvf.com/content_CVPR_2019/html/Li_Feedback_Network_for_Image_Super-Resolution_CVPR_2019_paper.html

[47] Zhou, A., Chen, Y., Wilson, J. P., Su, H., Xiong, Z., & Cheng, Q. (2021). An enhanced double‐filter deep residual neural network for generating super resolution dems. Remote Sensing, 13(16). https://doi.org/10.3390/rs13163089

[48] Zhu, D., Cheng, X., Zhang, F., Yao, X., Gao, Y., & Liu, Y. (2019). Spatial interpolation using conditional generative adversarial neural networks. Https://Doi.Org/10.1080/13658816.2019.1599122, 34(4), 735–758. https://doi.org/10.1080/13658816.2019.1599122

@article{1712040,
      author = {Musa Muhammad Chindo},
      title = {A Review of Digital Elevation Model Super Resolution},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {861-879},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712040.pdf},
      abstract = {High-resolution (HR) digital elevation models (DEMs) have been found to be critical for many applications, as they provide accurate basic geodata, as well as more information and accurate results.  However, despite the importance of HR DEM, many areas across the world, particularly in developing countries, lack access to them.  Thus, researchers inspired by the success of super resolution (SR) on image enhancement, especially the use of deep learning (DL) approaches, instead of using high-precision equipment to obtain HR DEMs, have recently presented and are discussing the concept of DEM SR.  This paper provides a review of such a DEM SR technique.  It first explains the basic idea of SR, then describes DEM SR, and finally, a review of DEM SR algorithms proposed in the literature is presented, describing the main approaches and some of the shortcomings.  This review shall provide the geoscientific community with information on an emerging alternative technique for acquiring HR DEM that is more cost-effective and can contribute to open data, which is widely recognised as the key engine for achieving the Sustainable Development Goals (SDGs).},
      keywords = {DEM, High Resolution, Low Resolution, Super Resolution, Machine Learning, Deep Learning.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1712040}
  }