1. SURVEYING ENGINEERING TECHNOLOGY
POINT CLOUD FILTERING
ALGORITHMS: NEW VS. OLD
By: Brian Mitchell
Technical Writing II
SUNY College of Technology – Alfred State
Kevin Cassell

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ABSTRACT
The material that follows deals with the technological advances that are constantly occurring in
the surveying engineering field. It also explains how the changing technology is causing a
change in how the analysis of a surveyors recorded data. This article provides a background on
the process that is used for collecting elevation data, information about the algorithms that are
used to filter the point clouds of data, which are then used to create digital models of the terrain
observed by surveyor. It also explains the differences between older and newer algorithms, and
describes why the newer ones are needed in order to keep up with the changing technology.
Technical images are used to demonstrate, visually, what is described and all of the information
and data comes from personal knowledge of the topics or scholarly articles.

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TABLE OF CONTENTS
BACKGROUND ............................................................................................................................ 1
THE ALGORITHM........................................................................................................................ 2
NEW VS. OLD METHODS........................................................................................................... 2
CONCLUSION............................................................................................................................... 3
REFERENCES ............................................................................................................................... 3
LIST OF FIGURES
Figure 1: Model of Airborne LiDAR.............................................................................................. 1
Figure 2: Drone LiDAR Setup........................................................................................................ 1
Figure 3: Digital Elevation Model form LiDAR Point Cloud........................................................ 1
Figure 4: Point Cloud Created Through Airborne LiDAR ............................................................. 1
Figure 5: Three Views of Same Terrain Before and After Filtering............................................... 2
Figure 6: Comparison of New Algorithm Vs. Three Older Algorithms......................................... 3
Figure 7: Models of the Compared Algorithms from Figure 6....................................................... 3

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BACKGROUND
In the field of surveying, advances in technology, that
have occurred over time, have had significant
impacts on the daily tasks of a surveyor. In the past, it
used to be more difficult to analyze and record the
elevations and patterns of earths terrain, but in todays
practice of land surveying there are easier and more
technical methods of obtaining this information.
One example of how technological advances have
impacted the surveying field is in the process of map
making. With the use of computer software
programs, such as AutoCAD and Carlson, that are
being implemented into the everyday work of
surveyors, surveying is becoming more digital.
Because these programs allow one to easily draw and
save projects while working from a basic computer
screen and not hand drawn like they were previously
done, maps are now able to take to new shapes.
While traditional maps are only two dimensional,
newer technologies are allowing terrain to be viewed
and recorded in three dimensions. Technology like
LiDAR, also referred to as terrestrial laser scanning,
has played a huge role in making this possible.
LiDAR is an abbreviation that stands for light
detection and ranging, but is more commonly
referred to as light radar. This technology is used in
surveying to measure the distances from the point
that the light is emitted, on the instrument, to the
point that the user wants to know the distance to.
LiDAR can be used either while stationary or while
in motion. Because it has the ability to record data
while moving, surveyors began affixing these laser
scanners to the under carriage of airplanes, as seen in
Figure 1. As the airplane travels in its flight
direction, the laser scans over the terrain in a
sweeping motion measuring the distance between the
plane and the ground. The planes altitude is
monitored by the ground reference station, and from
the altitude the elevation of the ground is calculated.
This process began with the use of airplanes to fly
over the terrain, but now with even more technology
advancements, remote controlled drones are also
starting to be implemented in doing this as well.
Because it is some much more cost effect, this
technique is easier for smaller surveying companies
to operate and allows them keep up with the
changing technology, as well as bigger companies.
The drone LiDAR works by being operated from the
ground with a controller or laptop, with the drone
relaying information that is the recorded by the base
station, which is also on the ground, as shown in
Figure 2.
Figure 1: Model of Airborne LiDAR
Figure 2: Drone LiDAR Setup
Figure 2: Point Cloud Created Through Airborne LiDAR
Figure 1: Digital Elevation Model form LiDAR Point Cloud

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Digital elevation models can be created by using this
airborne LiDAR technology (airplanes/drones). By
scanning over the specific terrain area, and recording
the changes in elevations, a point cloud can be
created (Figure 3). A point cloud is simply a set of
data points in a specific coordinate system. Once
there is a set of recorded elevations, the point cloud,
collected through laser scanning, the cloud is then
filtered using one or more algorithms to create the
three dimensional digital elevation model, as shown
in Figure 4. These figures show the elevations, using
a set of different colors, which helps better define the
different elevations. The colors work down from top
to bottom with the highest elevations displayed as red
and the lowest as dark blue.
THE ALGORITHM
It might seem unclear as to how an algorithm can
produce a three dimensional visual of terrain that has
been observed using an airborne LiDAR unit, but
what the algorithm is actually doing is “removing
unwanted measurements,” from the data set that was
collected (Sithole and Vosselman 2004).
An algorithm is a self-contained, step-by-step set of
operations that are performed in order to make
calculations, process data, or automate reasoning. In
this process the algorithms used to filter the point
cloud data sift throw the points and remove ones that
are erroneous, or don’t seem to fit. The
measurements that are being removed from the point
cloud data are points, or even objects, that don’t
make sense in comparison with the rest. The
unwanted measurements are usually characterized by
one of three things, noise, outliers, or errors. This
process of removing the unwanted points is known as
filtering.
Once the algorithm determines which point do not
correlate with the actual terrain, the data can then be
analyzed again, with other algorithms, that are
designed to more focused. There are several different
types of algorithms that deal with different types of
terrain. Because some terrains could be heavily
wooded or consist of a very urban area, which
contain a large number of buildings, specific
algorithms can either record the elevations at the top
of the objects, as well as the ground elevation, or they
can just remove the unnecessary objects all together.
This can be seen in Figure 5. Image (a) shows the
terrain as it is when the recorded point cloud only is
filtered with a basic algorithm to create the model,
while image (b) removes the buildings, and image (c)
strengthens some of the features from the scanned
data.
NEW VS. OLD METHODS
While the technology used in the field of surveying
has rapidly been evolving over time, the methods that
are used to analyze the data gathered by these new
technologies has been forced to change as well. Due
to current algorithms, and ones that were used prior
to now, becoming outdated due to the technology
being able to get more precise measurements, the
algorithms that are used to filter the point clouds, are
now becoming more advanced.
It has been said that, “current algorithms for filtering
point clouds assume the Earth’s surface to be
continuous in all directions” (Sitholea and
Vosselman, 2012). With this being said, the digital
elevation models that are being produced using these
types of algorithms are not very accurate. While they
are able to show the general terrain, “most traditional
morphology-based algorithms have difficulties in
preserving abrupt terrain features” (Hui et al. 2016).
Because the algorithms that have been highly used in
the past, and are still somewhat used now are proving
to be very inaccurate, newer types of algorithms have
been in development. These newer algorithms are
based on many different ideas. One of these ideas is
segmentation.
Segmentation breaks up the point cloud into
segments that may still have several height
discontinuities, which are measurable. The idea
behind this is that by do this it effectively smooths
out the point cloud, by removing small objects, that
are irrelevant, in order to see the bigger ones more
Figure 3: Three Views of Same Terrain Before and After Filtering.

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clearly. This in turn allows for the terrain models to
become more accurate, due to the discontinuities
being more noticeable, as shown in Figure 7. Figure
6 and Figure 7 show comparisons between a newer
algorithm, based off of the segmentation idea, and
three other, current or even older, algorithms. The bar
graph in Figure 6 shows the amount of errors that
each algorithm produces when filtering the point
cloud. The black bar represents the errors that occur
when the new algorithm is used, while the other three
are represented by the three different gray bars. It can
be seen that the newest algorithm has the least
amount of errors. This can also be seen in Figure 7,
where an area of terrain, is filtered with the same
algorithms compared in Figure 6. The dark areas of
each image represent the inaccuracies at the ground
level, while the white shows incorrect objects. Also
the correct areas of the image are shown by two
different tones of gray. When compared, it can be
seen, once again, that the new algorithm, or
Developed Alg. as shown in Figure 7, is the most
accurate representation of the area that was scanned.
CONCLUSION
In this report the differences between traditional and
newer filtering algorithms for point cloud analysis are
discussed. As my research has shown, the newer
types of algorithms are more accurate than the older
ones. Because of this, it is becoming more important
for the use of these newer algorithms being
implemented into the work in the surveying field. By
using these types of algorithms, as opposed to the
older ones, maps and three dimensional models, that
are produced from the data, will be able to show
more accurate depictions of the areas that are being
surveyed. Also, since abrupt terrain features and
dramatic elevation changes are now able to be shown
more accurately, users of the maps and models will
be able to have a better, more accurate, understanding
of the terrain, before actually seeing the terrain for
themselves.
REFERENCES
[1] Axelsson, P. (1999). “Processing of laser scanner
data – algorithms and applications.” Photogrammetry
and Remote Sensing, 54, 138-147.
[2] Hui, Z., Hu, Y., Yevenyo, Y. Z., Yu, X. (2016).
“An Improved Morphological Algorithm for Filtering
Airborne LiDAR Point Cloud Based on Multi-Level
Kriging Interpolation.” Remote Sensing, 8(35), 1-16.
[3] Sithole, G. and Vosselman, G. (2004).
“Experimental comparison of filter algorithms for
bare-Earth extraction from airborne laser scanning
point clouds.” Photogrammetry and Remote Sensing,
59, 89-91.
[4] Sithole, G. and Vosselman, G. (2012). “Filtering
of Airborne Laser Scanner Data Based on Segmented
Point Clouds.” Research Gate, 66-71.
[5] Yong, L., Bin, Y., Huayi, W., Ru, A. and Hanwei,
X. (2014). “An Improved Top-Hat Filter with Sloped
Brim for Extracting Ground Points from Airborne
Point Clouds.” Remote Sensing, 6, 12885-12095.
Figure 7: Comparison of New Algorithm Vs. Three Older
Algorithms.
Figure 6: Models of the Compared Algorithms from Figure
7.