Abstract

In this work, we describe multi-layered analyses of a high-resolution broad-area LADAR data set in support of expeditionary activities. High-level features are extracted from the LADAR data, such as the presence and location of buildings and cars, and then these features are used to populate a GIS (geographic information system) tool. We also apply line-of-sight (LOS) analysis to develop a path-planning module. Finally, visualization is addressed and enhanced with a gesture-based control system that allows the user to navigate through the enhanced data set in a virtual immersive experience. This work has operational applications including military, security, disaster relief, and task-based robotic path planning.

© 2013 OSA

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References

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  1. A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
    [CrossRef]
  2. The display of Fig. 1 was generated with the Eyeglass software, developed by Ross Anderson, MIT Lincoln Laboratory.
  3. A. Vasile, F. R. Waugh, D. Greisokh, and R. M. Heinrichs, “Automatic alignment of color imagery onto 3D laser radar data,” 35th Applied Imagery and Pattern Recognition Workshop (2006); doi:
    [CrossRef]
  4. Matlab is a product of MathWorks, http://www.mathworks.com
  5. P. Cho, “3D organization of 2D urban imagery,” IEEE 2008 Geosci. Remote Sensing Symp., 2 (2008).
  6. R. Madhavan and T. Hong, “Robust detection and recognition of buildings in urban environments from LADAR data,” 33rd Applied Imagery Pattern Recognition Workshop (2004) doi:
    [CrossRef]
  7. Q. Wang, L. Wang, and J. Sun, “Rotation-invariant target recognition in LADAR range imagery using model matching approach,” Opt. Express18(15), 15349–15360 (2010).
    [CrossRef] [PubMed]
  8. N. Rackliffe, H. A. Yanco, and J. Casper, “Using geographic information systems (GIS) for UAV landings and UGV navigation,” Technologies for Practical Robot Applications (TEPRA) (2001).
  9. J. B. Campbell, “GloVis as a resource for teaching geographic content and concepts,” J. Geog.106, 6 (2007)..
  10. D. G. Bell, F. Kuehnel, C. Maxwell, R. Kim, K. Kasraie, T. Gaskins, P. Hogan, and J. Coughlan, “NASA World Wind: opensource GIS for mission operations,” 2007 IEEE Aero. Conf., (2007).
  11. L. Breiman, “Random forests,” Mach. Learn.45(1), 5–32 (2001).
    [CrossRef]
  12. MATLAB interface by Abhishek Jaiantilal ( http://code.google.com/p/randomforest-matlab/ ), C code by Andy Liaw and Matthew Wiener, based on FORTRAN code by Leo Breiman and Adele Cutler.
  13. R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
    [CrossRef]
  14. OpenStreetMaps, http://www.openstreetmaps.org
  15. P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and CyberneticsSSC4, 4(2) (1968).
  16. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, 2009)
  17. Microsoft Kinect, http://www.xbox.com/en-US/KINECT
  18. Breckel Kinect- Tools for Kinect, http://www.breckel.com
  19. FAAST- Flexible Action and Articulated Skeleton Toolkit, http://projects.ict.usc.edu/mxr/faast/
  20. R. Kehl and L. Van Gool, “Real-time pointing gesture recognition for an immersive environment,” Proc. Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition, (2004).
    [CrossRef]
  21. M. R. Fetterman, T. Hughes, N. Armstrong-Crews, C. Barbu, K. Cole, R. Freking, K. Hood, J. Lacirignola, M. McLarney, A. Myne, S. Relyea, T. Vian, S. Vogl, and Z. Weber, “Distributed multi-modal sensor system for searching a foliage-covered region,” IEEE Technologies for Practical Robot Applications (TEPRA), (2011).

2010 (2)

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

Q. Wang, L. Wang, and J. Sun, “Rotation-invariant target recognition in LADAR range imagery using model matching approach,” Opt. Express18(15), 15349–15360 (2010).
[CrossRef] [PubMed]

2007 (1)

J. B. Campbell, “GloVis as a resource for teaching geographic content and concepts,” J. Geog.106, 6 (2007)..

2005 (1)

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

2001 (1)

L. Breiman, “Random forests,” Mach. Learn.45(1), 5–32 (2001).
[CrossRef]

1968 (1)

P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and CyberneticsSSC4, 4(2) (1968).

Breiman, L.

L. Breiman, “Random forests,” Mach. Learn.45(1), 5–32 (2001).
[CrossRef]

Burnside, J. W.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Campbell, J. B.

J. B. Campbell, “GloVis as a resource for teaching geographic content and concepts,” J. Geog.106, 6 (2007)..

Cannata, R.

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

Crawford, M. M.

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

Davis, W. R.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Fried, D.

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

Greisokh, D.

A. Vasile, F. R. Waugh, D. Greisokh, and R. M. Heinrichs, “Automatic alignment of color imagery onto 3D laser radar data,” 35th Applied Imagery and Pattern Recognition Workshop (2006); doi:
[CrossRef]

Hart, P. E.

P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and CyberneticsSSC4, 4(2) (1968).

Hatch, R. E.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Heinrichs, R.

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

Heinrichs, R. M.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

A. Vasile, F. R. Waugh, D. Greisokh, and R. M. Heinrichs, “Automatic alignment of color imagery onto 3D laser radar data,” 35th Applied Imagery and Pattern Recognition Workshop (2006); doi:
[CrossRef]

Hong, T.

R. Madhavan and T. Hong, “Robust detection and recognition of buildings in urban environments from LADAR data,” 33rd Applied Imagery Pattern Recognition Workshop (2004) doi:
[CrossRef]

Kehl, R.

R. Kehl and L. Van Gool, “Real-time pointing gesture recognition for an immersive environment,” Proc. Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition, (2004).
[CrossRef]

Knowlton, R.

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

Lee, E. I.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Madhavan, R.

R. Madhavan and T. Hong, “Robust detection and recognition of buildings in urban environments from LADAR data,” 33rd Applied Imagery Pattern Recognition Workshop (2004) doi:
[CrossRef]

Magruder, L. A.

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

Marino, R. M.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

McLaughlin, J. L.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Neuenschwander, A. L.

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

Nilsson, N. J.

P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and CyberneticsSSC4, 4(2) (1968).

O’Brien, M.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Raphael, B.

P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and CyberneticsSSC4, 4(2) (1968).

Rich, G. C.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Rowe, G. S.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Skelly, L. J.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Square, T. E.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Stanley, B. M.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Sun, J.

Van Gool, L.

R. Kehl and L. Van Gool, “Real-time pointing gesture recognition for an immersive environment,” Proc. Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition, (2004).
[CrossRef]

Vasile, A.

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

A. Vasile, F. R. Waugh, D. Greisokh, and R. M. Heinrichs, “Automatic alignment of color imagery onto 3D laser radar data,” 35th Applied Imagery and Pattern Recognition Workshop (2006); doi:
[CrossRef]

Wang, L.

Wang, Q.

Waugh, F. R.

A. Vasile, F. R. Waugh, D. Greisokh, and R. M. Heinrichs, “Automatic alignment of color imagery onto 3D laser radar data,” 35th Applied Imagery and Pattern Recognition Workshop (2006); doi:
[CrossRef]

Weed, C. A.

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

A formal basis for the heuristic determination of minimum cost paths (1)

P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and CyberneticsSSC4, 4(2) (1968).

J. Geog. (1)

J. B. Campbell, “GloVis as a resource for teaching geographic content and concepts,” J. Geog.106, 6 (2007)..

Laser Radar Technology and Applications (1)

R. M. Marino, W. R. Davis, G. C. Rich, J. L. McLaughlin, E. I. Lee, B. M. Stanley, J. W. Burnside, G. S. Rowe, R. E. Hatch, T. E. Square, L. J. Skelly, M. O’Brien, A. Vasile, and R. M. Heinrichs, “High-resolution 3D imaging laser radar flight test experiments,” Proc. SPIE 5791, Laser Radar Technology and ApplicationsX, (2005), doi:.
[CrossRef]

Mach. Learn. (1)

L. Breiman, “Random forests,” Mach. Learn.45(1), 5–32 (2001).
[CrossRef]

Opt. Express (1)

Proc. SPIE (1)

A. L. Neuenschwander, M. M. Crawford, L. A. Magruder, C. A. Weed, R. Cannata, D. Fried, R. Knowlton, and R. Heinrichs, “Terrain classification of LADAR data over Haitian urban environments using a lower envelope follower and adaptive gradient operator,” Proc. SPIE7684, 768408, 768408-9 (2010).
[CrossRef]

Other (15)

The display of Fig. 1 was generated with the Eyeglass software, developed by Ross Anderson, MIT Lincoln Laboratory.

A. Vasile, F. R. Waugh, D. Greisokh, and R. M. Heinrichs, “Automatic alignment of color imagery onto 3D laser radar data,” 35th Applied Imagery and Pattern Recognition Workshop (2006); doi:
[CrossRef]

Matlab is a product of MathWorks, http://www.mathworks.com

P. Cho, “3D organization of 2D urban imagery,” IEEE 2008 Geosci. Remote Sensing Symp., 2 (2008).

R. Madhavan and T. Hong, “Robust detection and recognition of buildings in urban environments from LADAR data,” 33rd Applied Imagery Pattern Recognition Workshop (2004) doi:
[CrossRef]

N. Rackliffe, H. A. Yanco, and J. Casper, “Using geographic information systems (GIS) for UAV landings and UGV navigation,” Technologies for Practical Robot Applications (TEPRA) (2001).

D. G. Bell, F. Kuehnel, C. Maxwell, R. Kim, K. Kasraie, T. Gaskins, P. Hogan, and J. Coughlan, “NASA World Wind: opensource GIS for mission operations,” 2007 IEEE Aero. Conf., (2007).

MATLAB interface by Abhishek Jaiantilal ( http://code.google.com/p/randomforest-matlab/ ), C code by Andy Liaw and Matthew Wiener, based on FORTRAN code by Leo Breiman and Adele Cutler.

OpenStreetMaps, http://www.openstreetmaps.org

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, 2009)

Microsoft Kinect, http://www.xbox.com/en-US/KINECT

Breckel Kinect- Tools for Kinect, http://www.breckel.com

FAAST- Flexible Action and Articulated Skeleton Toolkit, http://projects.ict.usc.edu/mxr/faast/

R. Kehl and L. Van Gool, “Real-time pointing gesture recognition for an immersive environment,” Proc. Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition, (2004).
[CrossRef]

M. R. Fetterman, T. Hughes, N. Armstrong-Crews, C. Barbu, K. Cole, R. Freking, K. Hood, J. Lacirignola, M. McLarney, A. Myne, S. Relyea, T. Vian, S. Vogl, and Z. Weber, “Distributed multi-modal sensor system for searching a foliage-covered region,” IEEE Technologies for Practical Robot Applications (TEPRA), (2011).

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Figures (12)

Fig. 1
Fig. 1

(left) High-resolution LADAR data. Image displayed with Eyeglass software package [2]. (right) Satellite imagery of the same region as on the left. Satellite imagery credit: DigitalGlobe. Approved for public release 13-387.

Fig. 2
Fig. 2

The National Palace in Port-Au-Prince, Haiti was destroyed during the earthquake. (a) LADAR imagery with satellite imagery wrapped over it. Satellite imagery credit: DigitalGlobe (b) Aerial imagery. Image credit: Logan Abassi/UNDP, licensed under Creative Commons License. (c) Photograph from the ground. Image credit: Logan Abassi/UNDP, licensed under Creative Commons License. Approved for public release 13-387.

Fig. 3
Fig. 3

(left) The region is colored according to height. (right) The region has been segmented, and the colors indicate different objects. Analyses based on LADAR data. Approved for public release 13-387.

Fig. 4
Fig. 4

Snapshot from GeoFetch tool. Roads (cyan), buildings (blue), tall buildings (red) are shown in this image. Feature analysis based on LADAR data. Satellite imagery credit: DigitalGlobe. Approved for public release 13-387.

Fig. 5
Fig. 5

(left) The lower right of this image shows the Haiti National Penitentiary. (right) Another area in Port-au-Prince. Feature analysis based on LADAR data. Satellite imagery credit: DigitalGlobe. Approved for public release 13-387.

Fig. 6
Fig. 6

(left) Satellite imagery of a block in Haiti. (right) The automated classifier has identified buildings and overlaid them upon the satellite imagery from Fig. 6 (left). The buildings are randomly colored so that close buildings can be distinguished. Feature analysis based on LADAR data. Satellite imagery credit: DigitalGlobe. Approved for public release 13-387.

Fig. 7
Fig. 7

The green regions represent clusters of buildings, or neighborhoods. Feature analysis based on LADAR data. Approved for public release 13-387.

Fig. 8
Fig. 8

When the user clicks on a building or region in the GIS tool, a window pops up (lower right) with detailed information about the object that the user clicked on. Feature analysis based on LADAR data. Satellite imagery credit: DigitalGlobe. Approved for public release 13-387.

Fig. 9
Fig. 9

Traversability analysis. (left) Assumed walking/driving speeds as a function of terrain slope. (right) Calculated velocities and go/no-go regions over a small region. Feature analysis based on LADAR data. Approved for public release 13-387.

Fig. 10
Fig. 10

(left) Black line indicates the fastest path from A to B. Image colored according to height. (right) Points visible from Point C are shaded red. This figure also shows the fastest path, incorporating traversability and covertness from an observer located at point C. Analysis based on LADAR data. Approved for public release 13-387.

Fig. 11
Fig. 11

(left) Visibility map. This map assumes a hostile observer is equally likely to be anywhere in the region; we refer to this as the aggregated line-of-sight calculation. In this map, the red areas have the highest visibility rating, and the blue areas have the lowest visibility. (right) Fastest path from A to B, incorporating the aggregated LOS calculation. The image is colored according to height, which makes it easier to recognize features than in the visibility map image. Analysis based on LADAR data. Approved for public release 13-387.

Fig. 12
Fig. 12

GeoFetch with software for gesture control of the virtual globe. Feature analysis based on LADAR data. Satellite imagery credit: DigitalGlobe. Approved for public release 13-387.

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