Generalized Data

I n the last Random Points arlkk, I discussed the overview concepts of

data and sensor fusion. I promised

at the conclusion to continue with a

fusion technique I call "Just in Time" (JIT) product generation. However, I thought it would a useful exercise to first

discuss data generalization.

In the mid-1980's 1 had the privilege of participating in a massive Defense Mapping Agency (DMA) development

called the Mark 90 program. TI1e DMA was producing a wide range of mapping

products (e.g. Nautical Charts, Harbor and Approach Charts, Topographic

Line Maps and more) essentially as one-off products. Data were collected

from a wide variety of sources to

create thematic map separates. These separates were transferred to film at a one-to-one {relative to the printed map

size) scale and used in printing physical maps; there were virtually no digital

maps at this time. You can imagine

how difficult and time consuming this process could be. Additionally, the

DMA had to stockpile literally pallets of maps in warehouses for dissemination

to the user community. If a change was deemed necessary, the map had to either be reprinted or a "red-line" separate

produced for overprinting of the map. The DMA embarked on a huge series

of modernization programs aimed at creating a "map factory'' that could

produce maps in a fiT fash ion. The idea

was to build a world-wide database of

base information and use this database

Product Request

1! 11 Mop P100Ud GeoeaUOil

in a series of software processes to create map products. The database

was called the Mapping, Charting and Geodesy (MC&G) database, reflecting

its wide purpose. Imagine the machinery of such a

system. Given a multitude of data

sources, the mapping algorithms must pull together those sources needed to

create a specific product, accurately

combine them, create labels, render

color schemes, and so forth. Just a few of the technologies involved include

conflation, conflict detection and resolu-tion, label placements, names resolution

and generalization. The general idea is depicted in Figure 1.

1his is an immensely complex task that even w this day (nearly 30 years later) has

Products

not been fully accomplished. However,

the various attempts (beginning with the DMA programs) have created thousands

of algorithms that take us ever closer to

the generalized solution. All of this is directly related to some

difficult problems in representing the real world in point clouds. \Y/e

are patticularly interested in the

"generalization" problem-that is, how

do we accurately represent data as we "zoom out" on that data? Consider a

Large scale map where a highway runs

very close to a parallel railroad. As we zoom out on the map, the road and

railroad would normally merge into a

single line. However, if the desired map product specified that these two featwes

must remain distinct, a cartographic

2013 VOL. 3 NO. 4 UcAR 61

displacement must occur as smaller

scale products are produced- this is the

process of generalization.

Consider the point doud "zoom out"

problem. To represent features in a point

cloud, the data must be at least twice

as dense as tht! smaUt!St d t!sired feature

(this is the Nyquist criteria to which

T have referred in p1'evious articles). Fot example, if you want to be able to measure a 4 em diameter walk button

at a crosswalk, the LIDAR data point

spacing must be 2 em o1closer. As one

zooms out on a view, more and more

data must be accessed to render the view.

For example, if the view volume con tains an area 5 meters high, 20 meters wide

and 10 meters deep, the view volume encompassed is 1,000 m3! At a point den-

sity of 1 point per 8 cm1 (one point per

cube measuring 2 em on a side), we have

125.000 points per cubic meter. Thus we would need to access 125 million points

to render the scene of our view volume.

For t his viewing to scale, the data must

be decimated. We routinely do this in

image processing by low pass filtering the data and then decimating. This is a

natural way of decimating images since

this is what happenS in an optical system

such as o ur eyes.

However, we have adopted a system

of tagging points within a point cloud,

anointing each point with "i ntelligent"

content. For example, a single point may

be tagged as a "Vertical Obstruction"

such as the top of a radio tower. lf we were to simply treat the point data as we

do an image. low pass filtering and deci

mating, we most likely would lose this

vert ical obstruction point. Tn most uses

of the data, this would be completely

unacceptable. Enter Cartographic

Generalization (or a similar technique

that we apply to point clouds).

62 UDAR 2013 VOL. 3 NO. 4

FiSh- 2: Tw n ultl \J I ur 1 rr

A search of the literatwe will reveal

very tittle on the topic of point cloud

generalization. 11ms we seem to be at

the forefront of this technology that

will prove critical as point douds begin

to replace other data sources for base

intelligen ce sources.

There has been some initial work on

a very specific case of generalization -terrain thinning. Consider a ground

model comprising point cloud data

with a nominal point spacing (NPS) of

50 em. This would yield an average of 8

triangles per m~ when the point cloud

of the ground surface were rendered

as a triangulated irregular network

(TIN)- see Figure2. Most CAD software would be

overwhelmed ifit were faced with load-

ing a TIN of such high density. For this

reason, thinning algorithms are included

with most LIDAR processing software.

'Ibis thinning operation for ground data

is typically called a "Model Key Point"

algorithm. Ln fact. the LAS point doud

specification includes a special bit flag to mark points as "MKP" points.

This thinning method functions as a

simple error bracketing algorithm. 1l1e

user specifies the maximum vertical

error that is permissible for the model

(treating the original data as "truth").

The o riginal data are m odeled as a TIN and points are iteratively rem oved. As

each point is removed, the modified

TIN is generated and compared (verti-cally) to the original TfN. lf the error i.s below the user specified threshold,

the point i s left out of the model and

the next point ls rem oved. If, on the other hand, the model deviates from

"truth" by more than the user specified

vertical limit, the test point is restored

to Lhe model.

This process repeats until no further

points can be removed from the model

without violating the accuracy criteria.

The remaining points are fl agged

as Model Key Points. It should be intuitively obvious that very few MKPs will be required in flat terrain whereas a fairly dense coUection of MKPs will be required where the terrain sharply changes. This is illustrated in figure 3 where the nodes of the TIN represent the model key points and all points (orange points and TIN nodes) represent the ground.

So now the question is "how do we extend this MKP idea to other features?" Can we flag important points (li terally) in the data set and preserve these point features as we generalize the data? Obviously this requires a new class of algorith m. Much can be learned from the old DMA

generalization efforts as well as other developments in these areas.

This is a very important preamble to the idea of JIT product generation from LIDAR data. Lfwe are to store LIDAR data at full resolution and then generate ad hoc products (using server-side processing, for example), we will have to develop intelligent (and fast!) algorithms that can perform the

requisite processing. We will pick up on this theme in the next edition of Random Point.~. D

Lewis Graham Is the President and CTO of GeoCue Corporation. GeoCue is North America's largest supplier of LIDAR production and workflow tools and consulting services for airborne and mobile laser scanning.

Figure 3: MCJdel Key Poln :; (nodes of TIN) Ground Points (noces of Tl"' ana all other points)

SmerzJ continued from page 64

TED Talks tfyou're not familiar with Ted Talks let me introduce you to one of the best video platforms on the planet for inspiring thought and action. lt's a col-lection of mostly common individuals having completely uncommon lectures on an incredibly wide variety of topics,

including our 30 technology. One of the most basic reasons this site is so immensely popular and videos are so often viewed is because th~y all talk about \XTHY. Each video has a message that is designed to somehow enrich the viewer and provide you with a message about WHY. And as such, their videos are viewed worldwide. [Check out Simon Sinek's liJ ] ~

Something is Missing Talking Heads

I 've been to a variety of conferences

lately, both within the 30 imaging

industry as well as other business evt:nts. I've had the privilege of listening

to some very bright minds and overaJI

successful people share their vision

and opinions on a variety of subjects. And aside from the predictable self-

aggrandizing that commonly occurs, especially in the world of 3d, there have

been some valuable messages. But I find

too often there is something missing.

ln conunon with the majority of these presentations is the lack of WHY in the

message. \.Vhy were they successful and

more importantly WHY should I care? So let me introduct: you to Simon Sinek who's

hook (Start With Why: How Great Leaders Inspire Everyone to Take Action {2000))

delves into what he says is a naturaJly

occurring pattern that explains why we

are inspired by some people, leaders, messages and organizations versus others.

And why some simply fail even though

they have a good plan -a benevolent objective. To quote Sinek, " ... people don't

buy what you do, they buy why you do it:' I strongly recommend his book

for a variety of reasons for anyone in business. One of the examples he uses

to illustrate how important the \VHY

is comes from the story of the fa mous explorer Edmund Shackleton who in

1914led an expedition to Antarctica

against truly death defying odds in a

64 UDAR 2013 VOL. 3 NO. 4

wooden sailing ship. The story tells of

the shipwrecked crew who together

survived months floating on ice caps. They were successful and nobody died

because of the composition of the crew

and their personalities. Shackleton ran

an ad in the paper before the mission to recruit the crew with the proper mind-

set. The ad specifically didn't say what the mission was, but stressed WHY they

wert: doing it. Tt read in part, " ... honour and recognition in case of succes..,:'

3D imaging world? J.n that last sentence substitute the word "measurement" in place of 'cancer treatment' and it's the

common speech you've heard.]

The doctor continues about how

frustrated he is because while they're one

of the leading organizations in the world, they fight desperately to compete for

medical research dollars and recognition

with the highly promoted Banner MD Anderson Cancer Center. Immediately

in my head l envision the signage

'' ... people don't buy what you do, they buy why you do it.''

Lost Doctor As an example, I'm sitting in a presenta-tion by a research scientLo;t from the

University of Arizona who spends

the next 37 minutes explaining to the

audience what they are doing to find a

cure for cancer. Incredibly knowledge-able, the doctor cites statistics and

shows case study data on their success

rate; discusses cutting edge technology; and paints a great vision for the future

with his methods that will revolutionize cancer treatment. [Sound a little like the

that Ba1mer has posted on bill boatds

throughout Arizona that shows their

logo, that has a giant red stripe through the word "cancer~ Banner is effectively

telling me WHY I should care and

support them even in their logo, while

this doctor is telling me how great he is at 1esearch. At no time during the pre-

sentation I sat through did the scientist indicate WHY 1 should care about his

cause. Think how much better it COltld

have been if he'd told me WHY I should support him over Banner Medical.

continued on page 63

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