Comprehensive approach for building outline extraction from LiDAR data with accent to a sparse laser scanning point cloud


Comprehensive approach for building outline
extraction from LiDAR data with accent to a sparse
laser scanning point cloud
Petr Hofman and Markéta Potůčková∗

Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles
University, Czech Republic

∗Corresponding author: marketa.potuckova@natur.cuni.cz

Abstract. The method of building outline extraction based on segmentation of airborne
laser scanning data is proposed and tested on a dataset comprising 1,400 buildings typical for
residential and industrial urban areas. The algorithm starts with setting a special threshold
to separate roof points from bare earth points and low objects. Next, local planes are fitted
to each point using RANSAC and further refined by least squares adjustment. A normal
vector is assigned to each point. Similarities among normal vectors are evaluated in order to
assemble planar or curved roof segments. Finally, building outlines are formed from detected
segments using the α-shapes algorithm and further regularized. The extracted outlines were
compared with reference polygons manually derived from the processed laser scanning point
cloud and orthoimages. Area-based evaluation of accuracy of the proposed method revealed
completeness and correctness of 87 % and 97 %, respectively, for the test dataset. The influ-
ence of parameters like number of points per roof segment, complexity of the roof structure,
roof type, and overlap with vegetation on accuracy is evaluated and discussed. The emphasis
is on point clouds with the density of 1 or 2 points/m2.

Keywords: airborne laser scanning, building outline, low point density.

1. Introduction

Increasing demand on 3D information and variety of its applications ranging from architec-
ture, engineering, real estate, telecommunication or tourism and development in technologies
like airborne laser scanning (ALS) and multi image processing have triggered research on
algorithms for derivation of 3D building models from point clouds and on related issues such
as accuracy assessment, transferability of the algorithms to different datasets, fusion of Li-
DAR and image data. A comprehensive review of about 100 papers dealing with building
extraction from ALS data published in the last two decades, challenges and possible research
trends can be found in [17]. Point density belongs to the issues discussed.

Increasing point density increases also the accuracy of extracted building outlines [17]. The
extraction algorithm mentioned in [16] requires conversion of an original point cloud to a
raster that is further processed in the eCognition software (object based image analysis, [18]);
densities lower than 5 points/m2 show very low accuracy in the presented case. On the other
hand, sparser LiDAR point clouds are acquired for nationwide mapping and therefore their
use for building extraction and modelling has been investigated (e.g. [14, 4, 2]).

This article focuses on automated process of building outline extraction applicable on a sparse
LiDAR point cloud with the point density about 1–2 points/m2. Inputs include an original
irregular point cloud containing only 3D coordinates of collected points plus digital terrain
model (DTM) of the entire area. The algorithm is based on segmentation in the parameter

Geoinformatics FCE CTU 16(1), 2017, doi:10.14311/gi.16.1.6 91

http://orcid.org/0000-0002-8760-790X
https://doi.org/10.14311/gi.16.1.6
http://creativecommons.org/licenses/by/4.0/


P. Hofman and M. Potůčková: Building outline extraction from LiDAR data

domain (the categories of approaches for the segmentation of surface features can be found
e.g. in [9]). To derive building roof surfaces, similar approach as shown in [6] is used; however,
random sample consensus (RANSAC) algorithm [3] and similarity between point attributes
(normal vectors) in local neighbourhood are utilized. In such a way, the proposed algorithm is
applicable to lower point densities and non-planar surfaces. The influence of parameters
like number of points per roof segment, complexity of the roof structure, roof type, and overlap
with vegetation on accuracy is evaluated and discussed. Development of the algorithm was
initiated by the Czech Office for Surveying, Mapping, and Cadastre.

2. Data and test sites

The laser scanning point cloud was acquired using the LMS Q680 scanner of the RIEGL Laser
Measurement Systems GmbH [12]. The overlap of the scanned lines was 50 %. The average
point cloud density was 1.5 points/m2 and the estimated accuracy in the point elevation was
0.1 m.

In order to cover the most common roof types in the Czech Republic, two different urban areas,
municipalities of Ctiněves (50°22’29” N, 14°18’26” E) and Pardubice-Polabiny (50°03’05” N,
15°45’40” E), were chosen (Figure 1). Ctiněves is a small village in the Ústí nad Labem
Region featuring typical rural architecture. Smaller buildings with mostly gabled roofs are
often partially overshadowed by vegetation. Polabiny form part of regional capital Pardubice.
There are large blocks of flats and industrial buildings with flat roofs as well as residential
areas comprising detached houses with more complicated roof constructions. In total, 1400
buildings and building complexes were processed.

(a) (b)

Figure 1: Subsets of point clouds from the test sites (a) Ctiněves and (b) Polabiny. For
the purpose of visualization the point clouds were automatically classified in SCOP++ (dark
green – ground, light green – vegetation, red – roofs, white – not classified).

3. Methodology

The proposed solution of building outline extraction is based on processing of irregular point
cloud representing roof structures. The extracted outlines therefore correspond to roof out-
lines and not to groundplans. Moreover, it is assumed that roofs consist of planar (flat, gabled,
hipped roofs) or curved (spherical, cone roof) segments and that any roof can be assembled

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from such segments. A data-driven approach is applied; no pre-defined building models are
used. Figure 2 shows a general workflow of a developed methodology. First, points on bare
earth and points with low height are filtered out. Second, a plane fitting to its neighbourhood
and a corresponding normal vector are assigned to each point on artificial and natural objects.
Based on similarity of normal vectors, points are divided to segments. Segments boundaries
are geometrically linked together to form a roof outline. Finally, the outlines obtained are
regularised. A detailed description of each processing step follows. The proposed method-
ology and its implementation require setting of several parameters. Their values are in the
current implementation set automatically depending on the point cloud density and type of
urban area. The values were determined empirically and tested so that the algorithm was
transferable among datasets with different densities and types of buildings.

Figure 2: General workflow of the proposed building (roof) outline extraction algorithm.

3.1. Pre-processing

Prior to the roof outline extraction, a dataset representing only bare earth points (DTM) is
required as an input. Existing filtering algorithms, e.g. [7, 1], are nowadays used operationally
(e.g. software packages TerraScan [15], SCOP++ [20], LasTools [11]) and will not be discussed
further in this text. The program SCOP++ [20] was applied for filtering of the test data.
Any DTM derived by other means could be utilised provided its resolution and accuracy will
not worsen the accuracy of derived roof outlines.

3.2. Object detection

Building extraction from an original irregular point cloud can be a demanding computational
operation. Thus, reducing the dataset to building candidates is helpful. With the use of
DTM, the height (above ground) of each point is calculated. Points with height less than 2 m
are excluded from further calculations. Remaining point clusters are delineated with closed,
in general non-convex polygons by means of the α-shape algorithm [8]. The next processing
step aims at eliminating clusters that do not represent buildings but high vegetation, pylons,
cars or their combination.

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3.3. Plane fitting

Points belonging to one plane roof segment show the same or very similar direction of normal
vectors. In the case of curved surfaces, e.g. conical or cylindrical ones, the direction of normal
vectors continuously change. Based on these conditions, points not corresponding to buildings
can be filtered out. On the other hand, the accuracy of determination of plane parameters
and its vectors is essential for successful extraction of buildings.

In our approach, a plane is fitted to surrounding of each point. To decrease the computational
time, surrounding is defined with a distance threshold that depends on an average point
density in the selected area. In the case of the tested dataset, a bounding box with a side
of 5 metres was used. Such an approach brings 30 to 40 points at the start of plane fitting.
The chosen size of the bounding box was sufficient for a reliable definition of a plane even at
the corners of the roof segments where in average 5–10 points laying at the same plane were
found. First, plane parameters are calculated using RANSAC [3] and subsequently refined
by means of least squares adjustment.

Fitting a plane to points lying in the middle of a planar roof segment without additional
objects (e. g. chimneys, dormers) is trivial. Nevertheless, there are often other roof construc-
tions or overlapping vegetation or a point falls on the edge of two or more surfaces. Thus,
in order to find a plane fitting to most of the points in a given surrounding and to exclude
outliers, RANSAC is applied. In addition to the evaluated point, two random points are iter-
atively selected, plane parameters are calculated, and distances of all points from this plane
are evaluated. Assuming that at least one fourth of points in the evaluated point surrounding
falls into a searched plane, the number of iterations is determined so that the probability of
selecting three initial points from this plane is not lower than 99.9 %. In the case of tested
dataset, the number of iteration was set to 110 according to the Formula 1 [3]. The used
parameter values are in parentheses.

k =
log (1 − p)

log (1 − wn)
(1)

k number of iterations (according to the parameters below equal to 107; set to 110)
p expected probability of fitting a correct plane (99.9 %)
w minimal expected number of inliers in the evaluated sample of points (0.25)
n number of sought points (2, the third point was the evaluated one)

Only points with a distance smaller than a set threshold (in our case 0.1 m, i. e. the accuracy
in height of the dataset) are accepted. The solution which features the highest number of
points (the highest score) falling into the distance threshold is taken as the final one. Plane
parameters and the normal vector assigned to the evaluated point are recalculated by the
least squares adjustment using all points fulfilling the threshold condition. The plain fitting
process is shown in Figure 3.

3.4. Roof face segmentation

Points that match the criteria mentioned above can be further segmented and grouped into
planar roof faces based on similarity in the direction of their normal vectors – points belong-
ing to one plane are attributed with nearly parallel normal vectors corresponding to local

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(a) (b) (c) (d)

Figure 3: Local plane fitting and refinement: (a) 2D section of an original point cloud with
an evaluated point in green, (b) selected point surroundings, (c) fitting plane after using
the RANSAC algorithm with a distance threshold, (d) final plane refined by means of least
squares adjustment.

planes formed in the point neighbourhood (Figure 4). Due to the presence of curved sur-
faces with continuously changing slope and exposition, the search for similar normal vectors
is not performed at once for the whole building but only in the close proximity of the point.
Thus, also points with even opposite directions of the normal vectors can form one segment
provided there is not any discontinuity, i.e. a difference in the angle between the normal vec-
tors exceeding the given threshold. Such local determination in similarity of normal vectors
also allows for discriminating roof planes having the same slope and orientation but being
physically separated.

Segments that include a low number of points to form a plane in a reliable way (less than 5 in
our case) are excluded from the further processing. Thus, after this step, only points forming
roof surfaces remain.

(a) (b) (c)

Figure 4: Point segmentation based on similarity of normal vectors in close proximity: (a)
2D section of an original point cloud and direction of normal vectors assigned to individual
points, (b) clustering based on parallelism of normal vectors – points belonging to the left
(red) and right (blue) planes, and two points (black) which direction of normal vectors exceed
the threshold with respect to their neighbouring points, (c) result of segmentation – points
belonging to two roof faces as an input for the final building outline extraction.

3.5. Outline extraction and regularization

The method of building outline extraction published in [8] is used in the next step. First,
the building outlines are derived by means of the α-shapes algorithm that enables extracting
also non-convex shapes and holes inside polygons. Next, irregular shapes are simplified using
the adopted sleeve-fitting algorithm which preserves critical points. Finally, the outlines are
modified to the most common rectangular building shapes. The dominant building direction

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is calculated from the existing outlines. If the orientation of a single line does not signifi-
cantly differ from the dominant direction (or the direction perpendicular to that), the line is
transformed to that required direction (see also [8] or [21]). Figure 5 shows an example of the
outline extraction and regularization result.

(a) (b)

(c) (b)

Figure 5: Outline extraction and regularization. (a) Outline of a reference building (red poly-
gon) derived by manual editing. (b) Outline of a cluster after applying 2 m height threshold
(green polygon). (c) Outline after the roof segmentation (blue polygon). (d) Final regularized
building outline (cyan polygon).

3.6. Evaluation approach

The automatically extracted building outlines obtained by the above described approach were
compared with outlines derived manually from colour orthoimages (0.25 cm ground sampled
distance) and a surface model calculated from the test point cloud. Thus, the stated accuracy
and reliability values of outline extraction express only quality of the applied method and do
not reflect absolute errors in the data. In order to evaluate the quality of outline extraction, an
automated area-based approach is applied. Its advantages in comparison with other evaluation
methods are discussed in detail in [10].

The outline of each reference building is overlaid with extracted building outlines. Overlaying
areas are divided into three groups (see also Figure 6):

• True positive (TP): Areas of the reference building that are correctly detected by the
automated process.

• False negative (FN): Areas of the reference building that were not detected by the
automated process.

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• False positives (FP): Areas detected by the automated process that do not match any
reference building.

(a) (b)

(c) (b)

Figure 6: Definition of relation between (a) reference building (red polygon) and detected
building area (cyan), (b) the true positive (TP) area (blue), (c) the false positive (FP) area
(green) and (d) the false negative (FN) area (magenta) (after [10]).

Based on these areal values, two quality measures are computed:

• Completeness: Comp = TP/(TP + FN), i.e. ratio of correctly detected building area
to the area of the reference building

• Correctness: Corr = TP/(TP + FP), i.e. ratio of correctly detected building area to
the total detected building area

In addition to the area-based method, the object-based evaluation with a mutual overlap
threshold of 70 % and weighting by building area [13] is also applied.

In addition to the overall quality, also influence of different parameters of an input point
cloud, buildings, and surrounding conditions were evaluated, namely number of points per

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roof segment, level of noise in the data, building size, type and complexity of the roof structure,
and presence of vegetation. Only the area-based method was used for this evaluation.

4. Results and discussion

4.1. Overall quality

The quality of building outline extraction achieved by application of the algorithm described
on 1,400 buildings or building blocks is summarised in Table 1. The object-based approach
only shows whether the building was roughly detected; compared to the area-based evaluation,
however, it does not express the geometric similarity between the reference and extracted
building outlines. While the object-based completeness shows that 94 % of reference buildings
were successfully detected and 99 % of all extracted building outlines match the reference
buildings, the area-based quality measures express that 87 % of building area were extracted
correctly and only 3 % of building area do not overlay reference buildings.

Table 1: Correctness and completeness of area- and object-based evaluation methods for
building extraction. Results of the above mentioned building outline extraction algorithm
applied on 1,400 buildings in the Ctiněves and Polabiny test areas.

Evaluation method Completeness Correctness
Object-based 0.94 0.99
Area-based 0.87 0.97

Considering that only spatial coordinates of laser points were available (without additional
information, e.g. multiple echoes or intensity) and the point density was rather low, the
building outline extraction of the tested dataset was successful.

4.2. Quality in relation to building and point cloud parameters

Success rate of the extraction chiefly depends on the building size, specifically on the size
of roof faces. Figure 7a demonstrates the relation between area-based correctness and com-
pleteness values and an average number of points per roof segment. Similarly, Figure 7b
shows the relation between the same quality parameters and the size of reference building.
First, relation of completeness and observed parameters will be discussed. Correctness will
be analysed separately at the end of this section.

It is obvious that the completeness much depends on the number of points per roof segment.
This number is influenced by the density of the original point cloud, building size, and com-
plexity of the roof deck. The role of building size works similarly, it is practically a subset
of the first parameter studied. The trends observed are not surprising due to the fact that
the proposed approach is data-driven and detection of single roof segments is crucial in the
whole extraction process. In the case of small buildings, reliability of an automated decision
(whether a point cluster creates a smooth and continuous surface) is very low. With the in-
creasing number of points per roof segment the success rate increases rapidly. Starting from
30 points per roof segment, the building outline extraction can be considered satisfactory;
completeness exceeds 65 %.

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0.0 

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10 20 30 40 50 75 100 200 10000 

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Building size dependency 

Corr 

Comp 

(a) (b)

Figure 7: Dependency of correctness and completeness accuracy measures (a) on the number
of points per roof segment and (b) on the size of the building.

The quality of building extraction is highly dependent on the number of points per roof
segment and on the building size, respectively. Thus, further parameters were studied on
buildings larger than 100 m2.

Noise in the dataset can be described as σ0 (standard deviation of the unit weight) resulting
from least squares estimation of a best fitting tilted plane in a point neighbourhood. The
software package OPALS was used for calculating σ0 values [19].

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Noise dependency (building area > 100 m2) 

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Vegetation  

Vegetation dependency (building area > 100 m2) 

Corr 

Comp 

(a) (b)

Figure 8: Dependency of correctness and completeness accuracy measures (a) on the noise
level and (b) on overlapping vegetation.

As expected, the dependence of completeness on σ0 is relatively high (see Figure 8a). If
a normal vector was assigned to a point with an incorrect height, it would produce larger
deviations from the vectors in its surrounding. On the other hand, increasing the threshold
in the roof segmentation step would rise the number of false positives and it would decrease
the correctness value. Figure 8b documents that the results are not strongly influenced by
adjacent or overlapping vegetation due to the applied filtering approach using RANSAC that
effectively filters out high portion of outliers.

Figure 9a shows dependency of the completeness on the complexity of the roof expressed as the
number of roof planes/surfaces. No trend was observed in this case which corresponds with
the principle of the proposed algorithm. The local, data-driven approach does not consider

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any building as one unit in the detection phase and it is not limited with a pre-defined set of
models which is the case of the model-driven approach. Any roof is considered as a union of
an arbitrary number of either planar or curved surfaces.

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Complexity dependency (building area > 100 m2) 

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Roof type 

Roof type dependency (building area > 100 m2) 

Corr 

Comp 

(a) (b)

Figure 9: Dependency of correctness and completeness accuracy measures (a) on building
complexity expressed as a number of roof segments and (b) on roof type.

The type of the roof does not influence the extraction success rate as well (compare Figure
9b). The proposed method can detect traditional gable or (half-)hipped roofs as well as
modern flat and shed roofs. Due to local determination of similarity between normal vectors,
curved surfaces are detected with the same quality.

No strong relation was observed between correctness values and the studied parameters.
Excluding negligible inaccuracies at the edge of the buildings, false positives appeared mostly
on vegetation (close and distant), locally comprising clusters behaving as a continuous surface.
Such surfaces were mostly small and isolated. Thus, majority of them was excluded from
further calculations. If such cluster appeared in a close proximity of a building, vegetation
was considered as another segment of the roof. Therefore, correctness values slightly decreased
in the case of buildings with adjacent or overlapping vegetation (Figure 8b). This problem
could be minimised by utilising intensity information or by fusion with colour imagery.

4.3. Comparison to related work

Similar approach for roof plane detection was published in [6]. By means of linear regression
the authors fitted local planes to scanned points and determined local roughness and normal
vectors. The points on vegetation were filtered out based on roughness values and the building
was segmented to roof planes according to normal vectors. It was not possible to use this
approach on the test dataset due to much lower point density, 1.5 points/m2 compared to 17
points/m2, and higher percentage of outliers. Thus, linear regression was not sufficient in the
case of our dataset. Moreover, in [6] problem with missing breaklines between roof planes was
mentioned; a normal vector corresponding to a point on a breakline does not match normal
vectors of any adjacent roof planes. These problems were solved by utilizing the RANSAC
algorithm that is able to eliminate outliers and chooses only one roof surface/plane for points
on breaklines. Finally, the solution when the roof surface was not planar but generally curved
was not included in [6].

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5. Conclusion

The proposed methodology for building outline extraction shows promising results. It is fully
automatic and based only on geometric attributes of the laser point cloud, i.e. on spatial
coordinates of the points. Moreover, it is suited for datasets with a lower point density (1.5
points/m2 in the case of our test point clouds).

Completeness of 97 % and correctness of 87 % was achieved in two test areas comprising
rural, industrial, and urban types of buildings. The success rate was similar in the case of
all roof types studied regardless of their complexity. The influence of adjacent or overlapping
vegetation was low. The major influence on resulting extraction quality was observed for the
size of roof faces in relation to the point density.

In order to increase the number of detected small buildings, higher point cloud density is
required. On the other hand, increasing point density also brings higher level of noise in the
laser point cloud [5]. Thus, successful practical application of the proposed method requires
more tests that would be carried out on datasets with different point densities.

Acknowledgements

The airborne laser scanning data were provided by the Czech Office for Surveying, Mapping,
and Cadastre.

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	P. Hofman and M. Potůčková: Building outline extraction from LiDAR data
	Introduction
	Data and test sites
	Methodology
	Pre-processing
	Object detection
	Plane fitting
	Roof face segmentation
	Outline extraction and regularization
	Evaluation approach

	Results and discussion
	Overall quality
	Quality in relation to building and point cloud parameters
	Comparison to related work

	Conclusion