Skip to main content
SpringerLink
Log in

D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network algorithms.This study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. https://sdd.nc.gov.

  2. https://sdd.nc.gov.

References

  1. Tarboton DG. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resour Res. 1997;33(2):309–19.

    Article  Google Scholar 

  2. Li J, Wong DWS. Effects of DEM sources on hydrologic applications. Comput Environ Urban Syst. 2010;34.3:251–61.

    Article  Google Scholar 

  3. Priestnall G, Jaafar J, Duncan A. Extracting urban features from LiDAR digital surface models. Comput Environ Urban Syst. 2000;24(2):65–78.

    Article  Google Scholar 

  4. Florinsky I. Digital terrain analysis in soil science and geology. Cambridge: Academic Press; 2016.

    Google Scholar 

  5. Fisher PF, Tate NJ. Causes and consequences of error in digital elevation models. ProgPhysGeogr. 2006;30(4):467–89.

    Google Scholar 

  6. Liu X. Airborne LiDAR for DEM generation: some critical issues. ProgPhysGeogr. 2008;32(1):31–49.

    Google Scholar 

  7. Woodrow K, Lindsay JB, Berg AA. Evaluating DEM conditioning techniques, elevation source data, and grid resolution for field-scale hydrological parameter extraction. J Hydrol. 2016;540:1022–9.

    Article  Google Scholar 

  8. Heritage GL, et al. Influence of survey strategy and interpolation model on DEM quality. Geomorphology. 2009;112.3-4:334–44.

    Article  Google Scholar 

  9. Sørensen R, Seibert J. Effects of DEM resolution on the calculation of topographical indices: TWI and its components. J Hydrol. 2007;347(1–2):79–89.

    Article  Google Scholar 

  10. Chaubey I, et al. Effect of DEM data resolution on SWAT output uncertainty. Hydrol Process Int J. 2005;19(3):621–8.

    Article  Google Scholar 

  11. Sanders BF. Evaluation of on-line DEMs for flood inundation modeling. Adv Water Resour. 2007;30(8):1831–1843. https://doi.org/10.1016/j.advwatres.2007.02.005.

    Article  Google Scholar 

  12. Li Z, Mount J, Demir I. Evaluation of model parameters of HAND model for real-time flood inundation mapping: iowa case study. EarthArXiv. 2020.

  13. Zhang W, Montgomery DR. Digital elevation model grid size, landscape representation, and hydrologic simulations. Water Resour Res. 1994;30(4):1019–28.

    Article  Google Scholar 

  14. Claessens L, et al. DEM resolution effects on shallow landslide hazard and soil redistribution modelling. Earth Surface Process Landf. 2005;30(4):461–77.

    Article  Google Scholar 

  15. Sit M, Demir I. Decentralized flood forecasting using deep neural networks. 2019; arXiv preprint http://arxiv.org/abs/1902.02308.

  16. Xiang Z, Yan J, Demir I. A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning. Water Resour Res. 2019;56(1):e2019WR025326. https://doi.org/10.1029/2019WR025326.

    Article  Google Scholar 

  17. Xiang Z, Demir I. Distributed long-term hourly streamflow predictions using deep learning–A case study for State of Iowa. Environ ModelSoftw. 2020;131:104761. https://doi.org/10.1016/j.envsoft.2020.104761.

    Article  Google Scholar 

  18. Vaze J, Teng J, Spencer G. Impact of DEM accuracy and resolution on topographic indices. Environ ModelSoftw. 2010;25(10):1086–98.

    Google Scholar 

  19. Sit M, Sermet Y, Demir I. Optimized watershed delineation library for server-side and client-side web applications. Open Geospat Data, Softw Stand. 2019;4(1):8.

    Article  Google Scholar 

  20. Demir I, Szczepanek R. Optimization of river network representation data models for web-based systems. Earth Space Sci. 2017;4(6):336–47.

    Article  Google Scholar 

  21. Sermet Y, et al. Crowdsourced approaches for stage measurements at ungauged locations using smartphones. Hydrol Sci J. 2019;65(5):1–10. https://doi.org/10.1080/02626667.2019.1659508.

    Article  Google Scholar 

  22. Saksena S, Merwade V. Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. J Hydrol. 2015;530:180–94.

    Article  Google Scholar 

  23. Xu Z, et al. Nonlocal similarity based DEM super resolution. ISPRS J Photogramm Remote Sens. 2015;110:48–54.

    Article  Google Scholar 

  24. Sit, M, et al. A comprehensive review of deep learning applications in hydrology and water resources. Water Sci Technol. 2020.

  25. Dong C, et al. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;382:295–307.

    Article  Google Scholar 

  26. Chen, Z, Wang X, Xu Z. Convolutional neural network based DEM Super resolution. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2016;41:247–250. https://doi.org/10.5194/isprs-archives-XLI-B3-247-2016.

    Article  Google Scholar 

  27. Ledig C, et al. Photo-realistic single image super-resolution using a generative adversarial network. CVPR. 2017;2(3):105–114. https://doi.org/10.1109/CVPR.2017.19.

    Article  Google Scholar 

  28. Xu Z, et al. Deep gradient prior network for DEM super-resolution: transfer learning from image to DEM. ISPRS J Photogramm Remote Sens. 2019;150:80–90.

    Article  Google Scholar 

  29. Nasrollahi K, Moeslund TB. Super-resolution: a comprehensive survey. Mach Vis Appl. 2014;25.6:1423–68.

    Article  Google Scholar 

  30. Bevilacqua M. Algorithms for super-resolution of images and videos based on learning methods. Diss. 2014.

  31. Farsiu S, et al. Fast and robust multiframe super resolution. IEEE Trans Image Process. 2004;1310:1327–44.

    Article  Google Scholar 

  32. Irani M, Peleg S. Improving resolution by image registration. CVGIP Graph Models Image Process. 1991;533:231–9.

    Article  Google Scholar 

  33. Jung C, et al. Position-patch based face hallucination using convex optimization. IEEE Signal Process Lett. 2011;18.6:367–70.

    Article  Google Scholar 

  34. Yang, C-Y, Ma C, Yang M-H. Single-image super-resolution: A benchmark. In: European Conference on computer vision. Springer, Cham, 2014.

  35. Takeda H, Farsiu S, Milanfar P. Kernel regression for image processing and reconstruction. IEEE Trans Image Process. 2007;16(2):349–66.

    Article  MathSciNet  Google Scholar 

  36. Protter M, et al. Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans Image Process. 2008;18(1):36–51.

    Article  MathSciNet  Google Scholar 

  37. Lin Z, Shum H-Y. Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans Pattern Anal Mach Intell. 2004;26(1):83–97.

    Article  Google Scholar 

  38. Li Y, et al. Single image super-resolution reconstruction based on genetic algorithm and regularization prior model. Inform Sci. 2016;372:196–207.

    Article  Google Scholar 

  39. Chang H, Yeung D-Y, Xiong Y. Super-resolution through neighbor embedding. Computer Vision and Pattern Recognition, 2004. In: CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on. Vol. 1. IEEE, 2004.

  40. Yang J, et al. Image super-resolution via sparse representation. IEEE Trans Image Process. 2010;19.11:2861–73.

    Article  MathSciNet  Google Scholar 

  41. Ni KS, Nguyen TQ. Image superresolution using support vector regression. IEEE Trans Image Process. 2007;16(6):1596–610.

    Article  MathSciNet  Google Scholar 

  42. Kim KI, Kwon Y. Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell. 2010;6:1127–33.

    Google Scholar 

  43. Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. BiolCybern. 1980;36(4):193–202.

    MATH  Google Scholar 

  44. LeCun, Y, et al. Object recognition with gradient-based learning. In: David AF, Joseph LM, Vito di Ges´u, Roberto Cipolla, editors. Shape, contour and grouping in computer vision. Berlin: Springer; 1999, p. 319–45. https://link-springer-com.proxy.lib.uiowa.edu/book/10.1007%2F3-540-46805-6#toc.

  45. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems. Curran Associates, Inc; 2012. p. 1097–1105. https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf.

  46. Lawrence S, et al. Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw. 1997;8.1:98–113.

    Article  Google Scholar 

  47. Dong C, Loy CC, Tang X. Accelerating the super-resolution convolutional neural network. In: European Conference on computer vision. Springer, Cham, 2016.

  48. Kim J, Lee JK, Lee KM. Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition. 2016.

  49. Kim J, Lee JK, Lee KM. Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on computer vision and pattern recognition. 2016.

  50. Shi, W, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.

  51. Lim B, et al. "nhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on computer vision and pattern recognition workshops. 2017.

  52. Zhang Y, et al. "Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on computer vision and pattern recognition. 2018.

  53. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ, editors. Advances in neural information processing systems. Curran Associates, Inc; 2014. p. 2672–2680. https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf.

  54. Isola P, et al. Image-to-image translation with conditional adversarial networks. 2017; arXiv preprint.

  55. Perarnau G, et al. Invertible conditional gans for image editing. 2016; arXiv preprint http://arxiv.org/abs/1611.06355.

  56. Wang Y, et al. A fully progressive approach to single-image super-resolution. 2018; arXiv preprint http://arxiv.org/abs/1804.02900.

  57. Mahapatra D. Retinal vasculature segmentation using local saliency maps and generative adversarial networks for image super resolution. 2017; arXiv preprint http://arxiv.org/abs/1710.04783

  58. Wang X, et al. Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV). 2018.

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

    Article  Google Scholar 

  60. Yue L, et al. "Fusion of multi-scale DEMs using a regularized super-resolution method. Int J GeographInfSci. 2015;29.12:2095–120.

    Google Scholar 

  61. Nair V, Hinton GE. "Rectified linear units improve restricted Boltzmann machines. In: ICML. 2010.

  62. He K, et al. Identity mappings in deep residual networks. In: European Conference on computer vision. Springer, Cham, 2016.

  63. Kingma DP, Ba J. Adam: A method for stochastic optimization. 2014; arXiv preprint http://arxiv.org/abs/1412.6980.

  64. Tang J, Pilesjö P. Estimating slope from raster data: a test of eight different algorithms in flat, undulating and steep terrain. River Basin Manag. 2011;VI:143–154. https://doi.org/10.2495/RM110131.

    Article  Google Scholar 

  65. Bolstad PV, Stowe T. An evaluation of DEM accuracy: elevation, slope, and aspect. PhotogrammEng Remote Sens. 1994;60(11):1327–32.

    Google Scholar 

  66. Burrough PA, et al. Principles of geographical information systems. Oxford: Oxford University Press; 2015.

    Google Scholar 

  67. Kingma DP, Welling W. Auto-encoding variational bayes. 2013. arXiv preprint http://arxiv.org/abs/1312.6114.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Iowa, Iowa City, USA

    Bekir Z. Demiray

  2. Interdisciplinary Graduate Program in Informatics, University of Iowa, Iowa City, USA

    Muhammed Sit

  3. Department of Civil and Environmental Engineering, University of Iowa, Iowa City, USA

    Ibrahim Demir

  4. Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USA

    Ibrahim Demir

Authors
  1. Bekir Z. Demiray
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammed Sit
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ibrahim Demir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bekir Z. Demiray.

Ethics declarations

Conflicts of Interest/Competing Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Demiray, B.Z., Sit, M. & Demir, I. D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks. SN COMPUT. SCI. 2, 48 (2021). https://doi.org/10.1007/s42979-020-00442-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-020-00442-2

Keywords

Search

Navigation