Open Journal of Civil Engineering, 2013, 3, 219-227
Published Online December 2013 (http://www.scirp.org/journal/ojce)
http://dx.doi.org/10.4236/ojce.2013.34026
Open Access OJCE
Cross-Verification of As-Built Point Cloud and
GIS-Related Map Data
Naai-Jung Shih, Shih-Cheng Tzen, Tzu-Ying Chan, Chia-Yu Lee
Department of Architecture, National Taiwan University of Science and Technology, Taipei, Taiwan
Received October 3, 2013; revised November 3, 2013; accepted November 10, 2013
Copyright © 2013 Naai-Jung Shih et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
This study cross-validates existing urban maps using point cloud models to update GIS related data. The model, as-built
3D data, is created to integrate with maps in an architectural CAD platform. The clouds are referred by existing vector
maps to verify inconsistency and to update 3D spatial relationships between subjects and environment. The cloud model
shows its top reference hierarchy as the updated data for topographic-derived urban maps.
Keywords: 3D Scan; As-Built 3D Models; Urban Infrastructure; GIS; Topographic Data
1. Introduction
Urban-related survey data have become increasingly
versatile, and are owned or contributed to different
government departments. Most local facility-related maps
are created based on topographic maps. Due to update
lag, inconsistency occurs among the maps and results in
different levels of errors. This inconsistency, which ori-
ginates from obsolete source maps, significantly reduces
the accuracy of each individual map, and causes subse-
quent mutual interferences between maps for accurate
comprehension.
Virtual 3D city models are becoming more widely
implemented by governments and city planning services,
of which highly detailed 3D models that reflect the
complexity of city objects and the interrelations are
required [1,2]. Nowadays, city modeling has reached a
new paradigm in which 3D point cloud models have been
treated with rich geometric properties and rich details,
which enable the clouds to integrate other city model
types [3]. Since the cloud models are as-built data, the
integration with old environmental data leads to a
specific application in showing most current status of
environment or in contrasting the changes.
Technology, policy and institutional barriers are usual-
ly faced in integrating data from multiple state-based
sources [4]. Same situation can occur in departments of a
local government for spatial-referenced multiple land
information databases. It’s important that data from all
platforms need to be exchangeable for the best efficiency
[5]. Based on shared data, system integration can be
achieved to support decision-making of planning and
facility management after construction. The concept of
cross-sourcing virtual cities [6] should be promoted
further to as-built city data, as to reflect the real content
of an environment. 2D registration processes should be
extended to cover 3D property registration [7], like the
integration of topographic map and as-built 3D city
models. The concept of rich geometric data should be
extended to GIS field being capable of integrating with
existing 2D vector drawing for update purposes.
Monitoring the development of city infrastructure is an
important task. Geospatial technique is used to monitor
city infrastructure networks by, for example, mobile laser
scanning [8]. The issues to be taken care of include the
representation, identification, and segmentation of 3D
urban objects. Although CityGML is a common informa-
tion model for the representation of 3D urban objects,
such as buildings, traffic infrastructure, water bodies [9],
the presence of these subject needs to be verified by as-
built model prior to evaluation or simulation. In addition
to the convincing visualization for spatial traversing,
scan details enable structuring and interpretation of
clouds for object segmentation, description, and classifi-
cation in a cluttered environment. 65% of recognition is
achieved [10]. Similar approach was also applied to steel
structure [11]. High-complexity point clouds have been
collected from terrestrial LiDAR 3D for city modeling,
based on boundary tracing of the planar components [12].

N.-J. SHIH ET AL. 220
Although airborne LiDAR point clouds have been ach-
ieved with large percentage of success in area-wide roof
plane segmentation [13], segmentation is feasible for
typical planar parts of building components. This ap-
proach may not be feasible for other urban facilities like
lamp poles which usually have circular sections or come
with different diameters from bottom to top. In addition,
most of the urban facilities are presented in a much
smaller size and ground projection that needs further
study in segmentation.
In order to correlate various survey data to as-built
spatial information, this study collaborates data types and
checks consistency using a point cloud platform. The
study integrates 3D urban scans and local maps to
analyze the differences between them. The development
and application of model structure can be seen in Figure
1. Clouds differ among various maps, as the former
represent the as-built data and the latter are vector
drawings. 3D scans are usually considered more precise
than the maps. If scans are used in an urban survey,
spatial information can be recorded more accurately and
more thoroughly. Near 20 departments and related maps
were used in this analysis.
3D scans create full-scale as-built point cloud data
which can be measured directly. 3D cloud models, which
can be viewed in different orientations, serve as a plat-
form for communication or inspection. With the related
maps overlapped, map inconsistency can be prevented by
referring to the as-built data for the most updated in-
formation. With the correct topographic data being
referred to as a base map, the demands for cross-
department collaboration and cross-map integration can
be achieved.
The purposes of this study are:
 to improve GIS data consistency;
 to achieve data cross-referencing;
 to address the importance of as-built cloud model;
Figure 1. The development and application of model struc-
ture.
 to explore a reversed data update approach.
2. Map Sources and As-Built References
Domestic maps are created by different departments,
such as the central government, as well as in cities and
townships. Take Taipei City for example: the topogra-
phic maps, urban planning pile map, cadastral maps and
public facility maps are all created by different depart-
ments within the city with different levels of resolution
and precision. Although topographic maps are commonly
referenced, most of the maps do not share the same
update procedures. The urban-related maps and informa-
tion, more than 80 categories, are later collected by the
Taipei City Government and integrated into a single
platform. The maps and information are now allocated in
the central databases originated by corresponding de-
partments. Although the data are integrated with the
Taipei digital topographic maps, there are differences
between the databases caused by survey or inconsistency
errors.
In order to update those map sources, an unified
source of reference is required. The reference should
have rich geometric information so different chara-
teristics of map subjects can be referred to. Whenever
renovations or demolishes occur, data retrieved only has
to be made from the source and update related data
automatically or manually. The consistency among re-
lated maps can be maintained.
The source data of this study are the as-built point
cloud models of both sides street facing façade of an
entire street of 6.5 km. The 3D model not only is used to
visualize urban environment, but also to add new
contents to all street-related urban data. The emerging
fields of elaboration are potentially unlimited.
3. Cross-Verification between Point Clouds
and 2D Vector Drawings
Scan data, which inherit 3D information, can provide
data on altitude and shape for 2D maps of street lamps,
parking facilities, excavations and underground utility
piping. The point clouds are overlapped with maps for
cross-verification. The model, as-built 3D data, is created
to integrate point cloud models and maps in an AutoCAD
Revit™ platform. The import problem occurred to large
data sets is solved by the imbed link between scan
software, which is capable of handling data with the
original level of accuracy, and the PC CAD platform,
which is widely used in architectural practice with the
capability of accepting different graphic formats. The
clouds are linked from Cyclone scan software to
AutoCAD™ through Cloudworx™. A Leica HDS
3000™ long-range laser scanner was used at ground and
roof levels.
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221
3.1. As-Built Point Cloud and Related Maps for
Streetlight
Scans were made to a street of 6.5 km in which six
blocks of maps were inspected. This comparison is made
by overlapping 3D point clouds and the Street Lamp Map
created by The Parks & Street Lights Office, the Public
Works Department of Taipei City Government [14].
Each street lamp type is represented by a specific symbol.
The original lamp map is quite simple, and only marks
the locations. The map has to be overlapped with a to-
pographic map to illustrate a lamp’s location on a street
or its relation to buildings.
Map number 4044B exemplifies overlapping the point
cloud with the lamp map and the topographic map (Fig-
ure 2), in which distance error is numerically indexed by
square symbols, and consistency error is indexed by
round symbols. The distance between the center of the
pole point cloud and the mark is illustrated in Figure 3.
Increasing need has been shown in generate real world
facilities in virtual environment, involving different level
of balance between human and computer effort [15].
With the balance in mind, after the environmental data
are retrieved, the human effort is still needed especially
in identifying the different between heterogeneous rep-
resentations among source maps. For example, human
effort with field photograph records may be needed to
specify subject types with higher success rate and to
identify the demolished subject which is usually difficult
to classify.
Figure 2. The street lamp map [14], point cloud, and topographic map of 4044B (left & top) and the indications of errors over
overlapped maps.

N.-J. SHIH ET AL. 222
Figure 3. Detailed illustration of the distance error and inconsistency error between the point cloud (4044B), the street lamp
map and the topographic map (top). The garden lamp (), which was marked in lamp map, was actually removed (bottom).
Based on the comparison between the point cloud, the
topographic map and the lamp map, errors are shown:
 Distance error: 33 errors with a distance larger than
1.88 m are identified in the street lamp map. The
minimum distance is 0.7 m, and the maximum dis-
tance is 4.59 m. 14 errors with a distance larger than
2.62 m are identified in the topographic map. The
minimum distance is 0.4 m, and the maximum dis-
tance is 4.86 m.
 Consistency error: The location and type of lamps can
be identified by the point cloud. 26 errors were found
in street lamp map, where ground garden lamps had
been demolished. 21 errors where different types of
lamps should be marked were found in the topo-
graphic map.
In correcting distance and consistency errors, it was
found that the topographic and lamp maps also contained
inconsistencies regarding building boundaries, open
spaces and lamp locations. Scans made on ground level
can retrieve more details than the plane-loaded scanners
or aerial photographs can achieve. Maps were found in-
consistent to cloud model and to current urban scenes.
First, the maps were updated based on cloud model and
then use the new source map to update subject data. The
two inconsistency types, which occur to maps and the
marked subjects, were corrected.
 Topographic map update: This study updated the
topographic map by referring to the point cloud. The
point cloud was orthogonally projected to eliminate
the vanishing point distortion commonly found in
aerial photographs of cities. The registration of 360 ×
270 scan regions provided sufficient data to illustrate
the relationship between subjects and environments.
In contrast, current digital topographic map based on
aerial maps and field surveys suffer from the amount
of details.
The topographic map update (Figure 4) shows the
differences in building size, location and angle. More
specifically, region A has become a nine-story build-
ing, instead of an open space; region B has become a
construction site, instead of a residence complex; and
region C has become a Mass Rapid Transit (MRT)
construction site.
 Street lamp map update: In order to correct the errors
made by the topographic-based drawings and human
field survey, the street lamp map was updated by
referring to the point cloud as well. Figure 5 shows
that most of the current lamp locations are wrong.
Region A has become an MRT construction site and
lamps have been temporarily removed, with the lamp
map has not been updated; region B-E have ground
garden lamps been demolished, without the lamp map
being updated.
The cloud model represe ts valuable as-built data n
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N.-J. SHIH ET AL. 223
Figure 4. Topographic map update proc e ss.
Figure 5. Street lamp map [14] update process.
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N.-J. SHIH ET AL. 224
which are used to inspect the difference of lamp shape
and locations between old maps and reality.
 Distance tolerance: In total, 493 out of 640 lamps on
the street lamp map were marked in the wrong loca-
tion, and 48.74% of those tolerances were greater
than 3 m, as compared to 139 out of 597 lamps on the
topographic map, where 60.87% of the tolerances are
greater than 3 m. In general, both maps have a large
percentage of tolerance, with the street lamp map
being slightly better.
 Type inconsistency: There are three types of mark
error: a mark with no real lamp, a lamp with no mark,
and a mark that is inconsistent with the lamp type. In
total, 145 lamps were mismarked, with 80 unmarked,
63 being extra marks, and 2 were inconsistent. Of the
458 errors in the topographic map, 424 were un-
marked and 34 were extra marks. In general, both
maps have a large percentage of tolerance, with the
street lamp map (29%) being better than the topo-
graphic map (77%).
 Inconsistency between map symbol marks and real
lamps: Different marking strategies were applied for
the topographic map and the street lamp map. The
former used unified symbols for lamps on sidewalks
and streets, which makes it very difficult for users to
distinguish types. The latter clearly specifies types,
although the size and shapes may be different from
the real ones.
In addition to showing the size and shape of lamps, the
point cloud can be rotated for different views, which pro-
vides more detailed on-site information for identification.
The comparison between the point cloud, the topographic
map and the street lamp map is illustrated in Table 1.
The tolerance shown in overlaying the map and the
point cloud is caused by:
 Human error: survey error, trace error, or data input
error;
Table 1. The comparison of symbol marks between point
cloud, topographic map, and street lamp map.
Point cloud
Symbol for
street lamp
map
Symbol for
topographic
map
 Obsolete data: Maps were created by different gov-
ernment departments over various periods of time,
during which facilities were installed or demolished
by phases.
 Missing environmental information: Data were not
retrieved due to the interference of fences, ground
covers or construction curtains.
 Modification of ground facilities due to new MRT
construction: The parking lots, curbs or sidewalks
were redesigned on ground level.
The accurate recording and display of as-built environ-
ments by 3D scans enables the measurement of facilities
by details, shape, size, altitude, tilt or the offset to the
original designated map location. The relative location to
the surrounding buildings can also be measured. This
quantitative description is very helpful for future renova-
tion, displacement or new installations.
3.2. The Integration of Cloud Model and Parking
Facility Maps
A comparison is exemplified by the parking facility map
provided by the Parking Management and Development
Office, Taipei City Government [16]. The map shows
lots for large vehicles, small vehicles and motorcycles.
The map also marks the charging methods, restriction
regions, restriction marks, numbers, corresponding de-
partments and contact phone numbers with specific sym-
bols and colors.
During the general facilities scans, a lower scan angle
at ground level usually suffers from interference by
fixtures and moving objects (vehicles, pedestrians). The
scan location must be carefully chosen for optimal
visibility, or the resulting cloud model will be frag-
mented, and will require additional scans from different
orientations to make the model more complete. Scans
from a higher altitude are more likely to capture the
parking facilities and red/yellow curb lines (Figure 6).
Cloud data are also subject to being blocked by obsta-
cles, scan angles and scan locations. The combination of
cloud models and maps can make up for this missing
data. Although multiple scan locations can complete the
model through registration, it is a time- and effort-con-
suming task, even for some interstitial spaces on side-
walks that are inaccessible for car-loaded scanners. In
order to increase the efficiency of data integration, in-
formation is added to the cloud model by overlaying the
parking facility map. The symbolic and notational 2D
information that used to be provided by vector drawings
is now broadened into a new framework, in which the
interrelationship between the symbolic notations and the
physical building environment can be quantitatively de-
fined. Thus, the surrounding environment of the parking
facility is clearly presented with missing information
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N.-J. SHIH ET AL. 225
Figure 6. Overlapping point cloud and parking facility map
[16] at different regions with the street views in point
clouds.
retrieved from the collaborative works, particularly in 3D
view. The additional spatial and subject information
identifiable around the parking facility include landscape
configuration, loading space, facility types, yellow lines
temporary parking zone, and nearby constructions.
3.3. Overlapping Underground Information and
Point Clouds
Most urban scans are made above ground level, and
underground as-built information is usually missing due
to the lack of the necessary scan technology in the case
of older installations. This study overlaps the map of
underground construction sites with cloud models, using
the information provided by The Public Works Depart-
ment of Taipei City Government [17].
The street excavation map provided by The Public
Works Department is based on a local digital topographic
map. Due to the limited as-built topographic information,
the map only provides location-based marks. The over-
lapping shows not only the exact excavation location and
street boundary, but also provides more environmental
information. The types of excavation can also be iden-
tified in the 3D perspective view as either utility piping
or an MRT station, which is useful for government
examination of construction types and locations. The 3D
view also provides a visual illustration for public inquiry,
enhancing public comprehension of traffic during con-
struction periods. Overlapping results can be seen in
Figure 7.
3.4. Overlapping of the Cloud Model with
Sections
Technologies for mapping the underworld (MTU) have
been applied to the condition assessment of underground
utilities of buried infrastructure [18]. The underground
scan not only presents the relationship with outside world,
but also comes with specific scan-related data application,
like rock engineering [19]. With semi-underground open-
ings available, the connection between inside and ex-
terior can be well-established with long-term measure-
ments [20]. The combination of ground scan and the
planning specification is useful in indicating relative
location and section diagrammatically.
Most government-provided information consists of 2D
data with location marked by specific symbols. Since the
scans could not be made during the occurrence of water,
natural gas, electricity, telecommunications, sewerage,
altitude-related information, such as sections and 3D data,
are usually missing. This study uses the city underground
utility piping specification of Public Construction Com-
mission Executive Yuan [21], which defines design
standards to combine information on the construction of
different piping types in order to avoid frequent con-
struction. By using 3D scans, sections can be overlapped
with.
After the underground piping map and the cloud mod-
els are overlapped, the interrelationship between the fa-
cilities above and underground can be established. For
example, the sewer is located 10 meters outside the
building projection lines (Figure 8). Sewers can be iden-
tified under the current excavation of the MRT station.
Traditional 2D maps, which only show locations, without
providing information on the surrounding environment,
can now be extended to 3D views for future maintenance
and excavation reference.
Figure 7. Taipei street excavation map [17] and the over-
lapping of the point cloud model with the street excavation
map.
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N.-J. SHIH ET AL. 226
Figure 8. The overlapping of the underground piping facil-
ity specification [21] with the cloud model.
4. The Limitation of 3D Data Model
The 3D as-built point cloud model has limitations in data
integration, update speed and retrieving process.
 A more seamless integration with building informa-
tion modeling (BIM) has to be achieved and should
be extended to a city scale.
 Intensive scans have to be managed in order to keep
up with the schedules of multiple construction sites
along the entire street in the same period of time.
 Interferences from pedestrians or vehicles occur oc-
casionally. Complete configuration scans have to be
made from different angles before being registered as
a whole.
5. Conclusion
This study presents a scan project for 3D as-built urban
data, offering a model that contains significantly im-
proved amounts of information. The cross-validation pro-
cess is both quantitative and qualitative made to GIS-
related data. Based on the corrected data, systematic in-
formation build-up is achieved, and multi-level cross-
validation is explored using topographic maps, street
lamp maps, parking facility map, street excavation map,
underground utility piping specification, etc., including
2D point-based location map, 2D vector drawings, and
3D point clouds. Thus, the importance of point clouds is
raised to a higher reference hierarchy for the validation
of possible related data.
6. Acknowledgements
This project was sponsored by the National Science
Council, Taiwan, the Republic of China, under the pro-
ject number 97-2221-E-011-162 and 98-2221-E-011-
123-MY3. The authors would like to thank the Council
for its support of scan works.
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