Visual Analysis and Semantic Exploration
of
Urban LIDAR Change Detection
Thomas Butkiewicz, Remco Chang,
Zachary Wartell, and William Ribarsky
• Project Goals:
– Develop change detection algorithm
– Urban areas (new buildings, construction, etc)
– County-wide, annual LIDAR scans
– Interactive exploration
Target Users:
• Urban planners
• Historians
• Tax Enforcement
Airborne LIDAR:
Laser Rangefinder + GPS/Inertial Navigation
Sample Points (x,y,z) w or w/o classification
Tens of thousands of point/second
Previous Work
Many previous approaches interpolate to rasters:
Interpolation
3D point cloud
2D grid of heights
Reduces accuracy
Points no longer measurements
Fails to exploit areas of higher density
Vu T., Matsuoka M., Yamazaki F.
Lidar-based change detection of buildings in dense urban areas.
In Proceedings of IEEE Geoscience and Remote Sensing Symposium, 2004. vol. 5, pp. 3413–3416.
Previous Work
Grids/Rasters than subtracted
Scan 1
-
Scan 2
=
Difference
Change Extraction done on the 2D grid of height differences
Thresholding
Vu T., Matsuoka M., Yamazaki F.
Lidar-based change detection of buildings in dense urban areas.
In Proceedings of IEEE Geoscience and Remote Sensing Symposium, 2004. vol. 5, pp. 3413–3416.
Previous Work
Filtering of changes done with image processing
Original
Opened
Opened then Closed
Coarse control of filtering due to granularity of kernel sizes
Creates false silhouettes/footprints
Loss of detail
3x3
5x5
7x7
Change Detection
Differences in our technique:
Points tested individually (No interpolation)
Accuracy preserved
Variable density exploited
Reliable measurements
3D, shape-based filtering
Finer granularity
More options (size, height, area, shape, semantical)
Change Detection
Input:
X,Y,Z points
Decision: Does this point fit the existing model?
Change due to measurement? or
Change in physical environment?
Output:
Only the X,Y,Z points that represent changes
(To change aggregator)
Change Detection
All possible surfaces
Sampled Points
Triangle Network
"Analyzing Sampled Terrain Volumetrically with Regard to Error and Geologic Variation“
Thomas Butkiewicz, Remco Chang, Zachary Wartell, William Ribarsky
Proc. SPIE Visualization and Data Analysis 2007, San Jose, CA
Change Aggregation
Input:
X,Y,Z Change points (From change detector)
Task:
Attempt to assemble nearby points into
cohesive models.
Output:
Change Models
(Exported for the Interactive Application)
Change Aggregation
Unvisited marked (as changed) vertices:
Has marked neighobrs?
No – discard (lone point  street sign, power line, etc)
Yes – Add incident faces to model, visit neighbors
Change Aggregation
Input:
Change Points
Output:
Change Models
Interactive Exploration
Raster based methods:
Results are 2D images
This is boring and offers no insight or interactivity!
Murakami H., Nakagawa K., Hasegawa H., Shibata T., Iwanami E.:
Change detection of buildings using an airborne laser scanner.
Journal of Photogrammetry and Remote Sensing 54 (July 1999), 148–152.
Vu T., Matsuoka M., Yamazaki F.
Lidar-based change detection of buildings in dense urban areas.
In Proceedings of IEEE Geoscience and Remote Sensing Symposium, 2004.
vol. 5, pp. 3413–3416.
Interactive Exploration
Features:
3D GIS Environment
GIS Database Integration
Interactive Filtering Tools
Analytical Tools
Interactive Exploration
Interactive Exploration
GIS Integration
Other Data:
Vector Data:
Roads, Building Footprints, etc
Building permits, Tax Database, etc
Non-Realistic Level-of-Detail
Traditionally:
Regions of Changes
Popping or fade out
Zooming out…
Buildings  1 pixel  disappear
No popping
Seamless transition
Distance away
NR LOD Solution:
Groups of Changes
Cognitively correct presentation:
“What abstraction makes sense to
show at each extent?”
Individual Changes
Full Detail Level
Development Level
Development Level
Regional Level
Legible Simplification of Textured Urban Models
Remco Chang, Thomas Butkiewicz, Caroline Ziemkiewicz, Zachary Wartell, Nancy Pollard, William Ribarsky
IEEE Computer Graphics and Applications (CG&A) Issue on Procedural Methods for Urban Modeling.
Heat Map + Filtering
Heat Map + Filtering
Heat Map + Filtering
Discussion and Future Work
Additional uses:
FEMA floodplain mapping
Live battlefield change detection*
Future enhancements:
LIDAR classification data
Aerial photos + obliques
SAR (penetrability)
Automatic target recognition
* Visual Analysis for Live LIDAR Battlefield Change Detection.
Thomas Butkiewicz, Remco Chang, Zachary Wartell, William Ribarsky.
SPIE Defense and Security Symposium 2008.
Conclusions
We have developed:
A method for comparing LIDAR data that:
Preserves accuracy
Results in useful 3D change models
An interactive application for exploring the detected changes, which has:
Intuitive presentation of changes
Non-realistic LOD for scale-appropriate data abstraction
Analytical and Filtering tools
Questions?