MGT 7-1.indd - KMI Media Group

terized teri ri rize ze zed d by dra dramatic ra rama ma mati ti tic ch cchanges anges in eelevation.
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extract xt xtract fro from ro rom m LI LIDA LIDAR DA DAR R da data
ta tha than h n road roads. ad ads.
“There is a high level of discontinuity between the top of a
building and the ground,” McKeown explained. “Current LIDAR
extraction software is relatively mature when it comes to finding
buildings or treetops.”
But in the case of roads, the difference in elevation between
the th center t of f a road d and d the th edge d of f a road d is i much h smaller. ll
“Elevations over roads tend to be smooth and different from
elevations characteristic of other features,” said McKeown. “We
use LIDAR data to localize where to look in black and white or
color imagery for more information and for further support of
the visualization. Since we don’t expect to see roads running
across the tops of buildings but between buildings, we are able to
throw away a fair amount of imaging and data and concentrate
processing on where roads are likely to be.”
The LIDAR data that TerraSim is using as part of its
Army SBIR project work provides its RoadMAP tool with a
new piece of information from which to draft the measurements
of elevations. “It will help us do a better job of finding
roads and extracting road delineation information,” McKeown
commented.
TerraSim expects that the addition of LIDAR data will improve
the accuracy and utility of its RoadMAP product. “LIDAR is being
used to enhance and validate features extracted from other
media, such as black and white or color imagery,” said Wilson
Harvey, a senior computer vision scientist at TerraSim. “LIDAR
gives a direct measurement of elevation much more accurate
and reliable than trying to calculate elevations based on sets of
imagery. It also adds a new set of data for systems to look at to
ascertain road characteristics and to help augment what Road-
MAP knows about a road. We expect to obtain much more fidelity
in terms of the x-y-z position of the road, and this will have a
huge impact in the 3-D visualization extracted from the data.”
The TerraSim product will be organized to have independent
evaluation modules, which will deal separately with color imagery,
black and white imagery, and LIDAR data. “Each of these
will talk to a higher level part of program, which will figure out
where the roadbed lies and put the pieces together in a certain
way to generate the road center line,” said Harvey.
TerraSim’s SBIR is a two-year deal that started in October
2008. “We have various six-month points where we link up with
the contract administrators to make sure we all are on the same
page and on the right track,” said McKeown. “We are expected
to present evaluation versions in six-month intervals, which our
Army sponsors run in their environment and give us feedback on
the productivity and accuracy of what we are doing.
“As with many SBIRs, we benefit from a close working
relationship with the sponsor,” McKeown added. “They have
the source data set and know the problems associated with the
exploitation of that data. They have the expectation of succeeding
in extracting road data from BuckEye. They suspect that process
can be improved otherwise would not have let this contract.”
McKeown expects the Army to evaluate TerraSim’s design
of the LIDAR enhancement to its RoadMAP product and make a
determination whether to go forward with a second phase. If the
Army gives the green light, McKeown said, he believes that TerraSim
could have a LIDAR-enhanced product in the marketplace
within a year to 18 months.
FEATURE EXTRACTION
Perhaps the most important benefit from the incorporation
of LIDAR data into simulation and training products will be the
improved accuracy of feature extraction software. The continuing
impediment to the adoption of automated feature extraction
is its level of accuracy, according to McKeown.
“A human h can do d it manually ll with ith 99 percent t accuracy and d
it takes a day,” he explained. “A machine can do it with 80 percent
accuracy in an hour. This still presents an impediment to
using the machine solution if 20 percent of the data must be
revisited to get it up to human-level performance. The problem
is how much user correction you will tolerate before you say it is
cheaper to do this manually.”
But the LIDAR data allows significantly more accuracy to
automated feature extraction. “If we can privatize the use of
LIDAR,” said McKeown, “we can do two things. We are able to
tell people that we are using LIDAR as a primary source, and we
are able to present a commercial product that is going to provide
a cost-effective feature extraction solution.” ✯
Contact Editor Harrison Donnelly at
harrisond@kmimediagroup.com. For more information related to this subject,
search our archives at www.MGT-kmi.com.
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