Automated Segmentation of the Walkable Area from Aerial Images for Evacuation Simulation

Fabian Schenk, Matthias Rüther, Horst Bischof

2016

Abstract

Computer-aided evacuation simulation is a very import preliminary step when planning safety measures for major public events. We propose a novel, efficient and fast method to extract the walkable area from highresolution aerial images for the purpose of evacuation simulation. In contrast to previous work, where the authors only extracted streets and roads or worked on indoor scenarios, we present an approach to accurately segment the walkable area of large outdoor areas. For this task we use a sophisticated seeded region growing (SRG) algorithm incorporating the information of digital surface models, true-orthophotos and inclination maps calculated from aerial images. Further, we introduce a new annotation and evaluation scheme especially designed for assessing the segmentation quality of evacuation maps. An extensive qualitative and quantitative evaluation, where we study various combinations of SRG methods and parameter settings by the example of different real-world scenarios, shows the feasibility of our approach.

References

  1. Adams, R. and Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6):641-647.
  2. Chambolle, A. and Pock, T. (2011). A first-order primaldual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision, 40(1):120-145.
  3. Cheriyadat, A. M. (2014). Unsupervised feature learning for aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 52(1):439-451.
  4. Collins, R. T. (1996). A space-sweep approach to true multi-image matching. In Computer Vision and Pattern Recognition, pages 358-363. IEEE.
  5. Dal Poz, A. P., Gallis, R. A., da Silva, J. F., and Martins, Ó. F. (2012). Object-space road extraction in rural areas using stereoscopic aerial images. Geoscience and Remote Sensing Letters, IEEE, 9(4):654-658.
  6. Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3):297-302.
  7. Galea, E. R. (2002). Simulating evacuation and circulation in planes, trains, buildings and ships using the exodus software. Pedestrian and Evacuation Dynamics. Springer, pages 203-225.
  8. Han, J., Zhang, D., Cheng, G., Guo, L., and Ren, J. (2015). Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. Transactions on Geoscience and Remote Sensing, 53(6):3325-3337.
  9. Holz, D., Holzer, S., Rusu, R. B., and Behnke, S. (2012). Real-time plane segmentation using rgb-d cameras. In RoboCup 2011: Robot Soccer World Cup XV, pages 306-317. Springer.
  10. Hsu, E. B. and Burkle, F. M. (2012). Cambodian bon om touk stampede highlights preventable tragedy. Prehospital and disaster medicine, 27(05):481-482.
  11. Hu, J., Razdan, A., Femiani, J. C., Cui, M., and Wonka, P. (2007). Road network extraction and intersection detection from aerial images by tracking road footprints. IEEE Transactions on Geoscience and Remote Sensing, 45(12):4144-4157.
  12. Huth, J., Kuenzer, C., Wehrmann, T., Gebhardt, S., Tuan, V. Q., and Dech, S. (2012). Land cover and land use classification with twopac: Towards automated processing for pixel-and object-based image classification. Remote Sensing, 4(9):2530-2553.
  13. Irschara, A., Rumpler, M., Meixner, P., Pock, T., and Bischof, H. (2012). Efficient and globally optimal multi view dense matching for aerial images. ISPRS annals of photogrammetry, remote sensing and spatial information sciences, 1:227-232.
  14. Jaccard, P. (1908). Nouvelles recherches sur la distribution florale .
  15. Johnson, C. W. (2008). Using evacuation simulations for contingency planning to enhance the security and safety of the 2012 olympic venues. Safety science, 46(2):302-322.
  16. Kl üpfel, H. (2006). The simulation of crowd dynamics at very large events. Traffic and Granular Flow'05 , 5.
  17. Krausz, B. and Bauckhage, C. (2012). Loveparade 2010: Automatic video analysis of a crowd disaster. Computer Vision and Image Understanding, 116(3):307- 319.
  18. Lämmel, G., Grether, D., and Nagel, K. (2010). The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations. Transportation Research Part C: Emerging Technologies, 18(1):84-98.
  19. Lin, Y. and Saripalli, S. (2012). Road detection and tracking from aerial desert imagery. Journal of Intelligent & Robotic Systems, 65(1-4):345-359.
  20. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  21. Mas, E., Adriano, B., and Koshimura, S. (2013). An integrated simulation of tsunami hazard and human evacuation in la punta, peru. Journal of Disaster Research, 8(2):285-295.
  22. Rudin, L., Osher, S., and Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60(1):259-268.
  23. Rumpler, M., Wendel, A., and Bischof, H. (2013). Probabilistic range image integration for dsm and trueorthophoto generation. In Image Analysis, pages 533- 544. Springer.
  24. Schneider, V. and K önnecke, R. (2001). Simulating evacuation processes with aseri. Pedestrian and Evacuation Dynamics, pages 301-313.
  25. Shi, C., Zhong, M., Nong, X., He, L., Shi, J., and Feng, G. (2012). Modeling and safety strategy of passenger evacuation in a metro station in china. Safety Science, 50(5):1319-1332.
  26. Tang, F. and Ren, A. (2012). Gis-based 3d evacuation simulation for indoor fire. Building and Environment, 49:193-202.
  27. Taubenb öck, H., Post, J., Kiefl, R., Roth, A., Ismail, F. A., Strunz, G., and Dech, S. (2009). Risk and vulnerability assessment to tsunami hazard using very high resolution satellite data: The case study of padang, indonesia. EARSeL eProceedings, 8(1):53-63.
  28. Triggs, B., McLauchlan, P. F., Hartley, R. I., and Fitzgibbon, A. W. (2000). Bundle adjustmenta modern synthesis. In Vision Algorithms: Theory and Practice, pages 298-372. Springer.
  29. Tsai, J., Fridman, N., Bowring, E., Brown, M., Epstein, S., Kaminka, G., Marsella, S., Ogden, A., Rika, I., Sheel, A., et al. (2011). Escapes: evacuation simulation with children, authorities, parents, emotions, and social comparison. pages 457-464. AAMAS.
  30. Zhou, H., Kong, H., Wei, L., Creighton, D., and Nahavandi, S. (2015). Efficient road detection and tracking for unmanned aerial vehicle. IEEE Transactions on Intelligent Transportation Systems, 16(1):297-309.
Download


Paper Citation


in Harvard Style

Schenk F., Rüther M. and Bischof H. (2016). Automated Segmentation of the Walkable Area from Aerial Images for Evacuation Simulation . In Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-188-5, pages 125-135. DOI: 10.5220/0005868601250135


in Bibtex Style

@conference{gistam16,
author={Fabian Schenk and Matthias Rüther and Horst Bischof},
title={Automated Segmentation of the Walkable Area from Aerial Images for Evacuation Simulation},
booktitle={Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2016},
pages={125-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005868601250135},
isbn={978-989-758-188-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Automated Segmentation of the Walkable Area from Aerial Images for Evacuation Simulation
SN - 978-989-758-188-5
AU - Schenk F.
AU - Rüther M.
AU - Bischof H.
PY - 2016
SP - 125
EP - 135
DO - 10.5220/0005868601250135