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A Distributed Computing Workflow for Modelling Environmental Flows in Complex Terrain

  • Stuart R. Mead
  • Mahesh Prakash
  • Christina Magill
  • Matt Bolger
  • Jean-Claude Thouret
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 448)

Abstract

Numerical modelling of extreme environmental flows such as flash floods, avalanches and mudflows can be used to understand fundamental processes, predict outcomes and assess the loss potential of future events. These extreme flows can produce complicated and dynamic free surfaces as a result of interactions with the terrain and built environment. In order to resolve these features that may affect flows, high resolution, accurate terrain models are required. However, terrain models can be difficult and costly to acquire, and often lack detail of important flow steering structures such as bridges or debris. To overcome these issues we have developed a photogrammetry workflow for reconstructing high spatial resolution three dimensional terrain models. The workflow utilises parallel and distributed computing to provide inexpensive terrain models that can then be used in numerical simulations of environmental flows. A section of Quebrada San Lazaro within the city of Arequipa, Peru is used as a case study to demonstrate the construction and usage of the terrain models and applicability of the workflow for a flash flood scenario.

Keywords

Structure-from-Motion photogrammetry numerical modelling rapid mass flow natural hazards 

References

  1. 1.
    Manville, V., Cronin, S.J.: Breakout Lahar from New Zealand’s Crater Lake. Eos, Transactions American Geophysical Union 88, 441–442 (2007)CrossRefGoogle Scholar
  2. 2.
    Cleary, P.W., Prakash, M.: Discrete–element modelling and smoothed particle hydrodynamics: potential in the environmental sciences. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 362, 2003–2030 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Williams, R.D., Brasington, J., Hicks, M., Measures, R., Rennie, C.D., Vericat, D.: Hydraulic validation of two-dimensional simulations of braided river flow with spatially continuous aDcp data. Water Resources Research 49, 5183–5205 (2013)CrossRefGoogle Scholar
  4. 4.
    Legleiter, C.J., Kyriakidis, P.C., McDonald, R.R., Nelson, J.M.: Effects of uncertain topographic input data on two-dimensional flow modeling in a gravel-bed river. Water Resources Research 47, W03518, 3518 (2011)Google Scholar
  5. 5.
    Javernick, L., Brasington, J., Caruso, B.: Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry. Geomorphology 213, 166–182 (2014)CrossRefGoogle Scholar
  6. 6.
    Kreylos, O., Oskin, M., Cowgill, E., Gold, P., Elliott, A., Kellogg, L.: Point-based computing on scanned terrain with LidarViewer. Geosphere 9, 546–556 (2013)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Towards Internet-scale multi-view stereo. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1434–1441 (2010)Google Scholar
  9. 9.
    Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 519–528 (2006)Google Scholar
  10. 10.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. ACM Trans. Graph. 25, 835–846 (2006)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1152, pp. 1150–1157 (1999)Google Scholar
  12. 12.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: Binary Robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (2011)Google Scholar
  14. 14.
    Furukawa, Y., Ponce, J.: Accurate, Dense, and Robust Multiview Stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1362–1376 (2010)CrossRefGoogle Scholar
  15. 15.
    Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–4 (2011)Google Scholar
  16. 16.
    Calakli, F., Taubin, G.: SSD-C: Smooth Signed Distance Colored Surface Reconstruction. In: Dill, J., Earnshaw, R., Kasik, D., Vince, J., Wong, P.C. (eds.) Expanding the Frontiers of Visual Analytics and Visualization, pp. 323–338. Springer, London (2012)CrossRefGoogle Scholar
  17. 17.
    Thouret, J.-C., Enjolras, G., Martelli, K., Santoni, O., Luque, J., Nagata, M., Arguedas, A., Macedo, L.: Combining criteria for delineating lahar-and flash-flood-prone hazard and risk zones for the city of Arequipa, Peru. Natural Hazards and Earth System Sciences 13, 339–360 (2013)CrossRefGoogle Scholar
  18. 18.
    Wu, C.: Towards Linear-Time Incremental Structure from Motion. In: 2013 International Conference on 3D Vision (3DV), pp. 127–134 (2013)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Stuart R. Mead
    • 1
    • 2
  • Mahesh Prakash
    • 2
  • Christina Magill
    • 1
  • Matt Bolger
    • 2
  • Jean-Claude Thouret
    • 3
  1. 1.Risk Frontiers, Faculty of ScienceMacquarie UniversitySydneyAustralia
  2. 2.CSIRO Digital Productivity FlagshipMelbourneAustralia
  3. 3.Laboratoire Magmas et Volcans UMR6524 CNRS, IRD and OPGCUniversity Blaise PascalClermont-FerrandFrance

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