







































37 

GeoPlanning 
Journal of Geomatics and Planning                                                                                                                 Vol. 9, No. 1, 2022  

 

Original Research 

3D Modelling of Boscha Observatory with TLS 

and UAV Integration Data  

Gusti A. J. Kartini1*, Naura D. Saputri1 

1. Department of Geodetic Engineering, Faculty of Civil Engineering and Planning, Institut 

Teknologi Nasional Bandung, Indonesia 

 

DOI: 10.14710/geoplanning.9.1.37-46 

Abstract 

The Bosscha Observatory is Southeast Asia's first modern astronomical observatory. This observatory is located exactly on 

the Lembang Fault in West Java, Indonesia. Its existence on the fault line makes Bosscha Observatory very vulnerable to 

disasters, which in the future will cause severe damage to the cultural heritage building. One way to protect the preservation 

of cultural heritage buildings is through 3D digital documentation. With 3D shapes, we can obtain precise visual and 

geometric data that can be used to monitor the building's condition. There are two technologies will be used in this study, 

terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) photogrammetry. TLS systems can capture millions of 

points representing 3-D coordinates at extremely high spatial densities on complex, multidimensional surfaces within 

minutes. UAV photogrammetry can generate 3D point cloud in centimeter-level precision. The results of data integration 

between TLS and UAV have been implemented successfully and can be used as one of the measurement techniques 

supporting 3D modeling and compensating for the shortcomings of each tool. This three-dimensional model can be used to 

create a cylindrical portion of a building and the roof of a hemispherical building; the texture and color of the building's 

details, such as windows, doors, and stairs, can be produced with an RMSE error value of 0.025 meters. 

Copyright © 2022 GJGP-Undip 

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license 

1. Introduction  

The Bosscha Observatory is Southeast Asia's first modern astronomical observatory, having been 

designated as a National Cultural Heritage site in 2004 and a National Vital Object in 2008. This observatory is 

located exactly on the Lembang Fault in West Java, Indonesia (Figure 1). According to research, this fault can 

produce earthquakes with a magnitude of 6.5–7.0 on the Richter scale (Daryono et al., 2019). Its existence on the 

fault line makes Bosscha Observatory very vulnerable to disasters, which in the future will cause severe damage 

to the cultural heritage building. Because of the importance of cultural heritage buildings for the future, 

protection is needed to prevent damage and destruction (Batur et al., 2020; Chmutina et al., 2020). One way to 

protect the preservation of cultural heritage buildings is through digital documentation.  

Typically, image-based technology or lasers have been utilized in the documentation of cultural heritage 

structures. With the aid of this technology, the documentation results are not only 2D but also 3D. The 

researcher was able to accurately document their subjects using 3D digital data formats (Dostal & Yamafune, 

2018). With 3D shapes, we can obtain precise visual and geometric data that can be used to monitor the building's 

condition (Kushwaha et al., 2020). The high precision of measurement makes it possible to investigate the 

deformations and damages of historic objects (Kwoczynska et al., 2016).   

Wirnajaya et al. (2019) conducted 3D mapping at the Bosscha Observatory using Terrestrial Laser 

Scanner (TLS) technology to produce the majority of 3D point cloud shapes. The absence of point cloud data on 

the roof of the Bosscha Observatory is a limitation of their research. In other studies, to obtain the top or difficult-

e-ISSN: 2355-6544 
 
Received: 24 September 2021; 
Accepted: 22 November 2022; 
Published: 29 November 2022.  
 
Keywords:  
Terrestrial Laser Scanner, 

Unmanned Aerial Vehicle, Data 

Integration, Point Cloud, 3D 

Model 

*Corresponding author(s)  
email: ayujessy@itenas.ac.id  
 
 

 

https://doi.org/10.14710/geoplanning.9.1.37-46
mailto:ayujessy@itenas.ac.id


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38 

to-scan areas of an object, additional tools such as cranes are required (Büyüksalih et al., 2020). TLS scanning is 

relatively more expensive in terms of cost, but it has many advantages, including the ability to map quickly and 

in large quantities, to provide position, intensity, and RGB information, and to produce relatively precise 

measurements (Wu et al., 2021). TLS systems can capture millions of points representing 3-D coordinates at 

extremely high spatial densities on complex, multidimensional surfaces within minutes (Gallay et al., 2015). The 

technology can quickly determine the precise coordinates of points that represent the surface of an object (Klapa 

et al., 2017).  

 

 
Source: Daryono et al., 2018 

Figure 1. The location of the Bosscha Observatory (6°49'28.97" S 107°37'01.59" E) is on the Lembang Fault 

trajectory. The Lembang Fault is illustrated with a black line that stretches for 29 km 

In addition to TLS, Maharani et al. (2020) have used Unmanned Aerial Vehicle (UAV) photogrammetry 

to document the Bosscha Observatory and produce its full 3D shape. The resulting photographs were then 

combined and converted into a point cloud shape using Agisoft Metashape in this study. In other studies, 

photography-based technologies are commonly used to document heritage building (Febro, 2020; Manajitprasert 

et al., 2019; Themistocleous, 2020). This is because UAV photogrammetry is relatively inexpensive, in addition 

to being simple to operate and capable of producing high-quality 3D models (Manajitprasert et al., 2019; A 

Murtiyoso et al., 2019). 3D point clouds can be reconstructed from UAV images with satisfactory accuracy; these 

images can generate centimeter-level precision  (Arnadi Murtiyoso & Grussenmeyer, 2017; Pan et al., 2019).  

In a separate study, a combination of TLS and UAV was utilized to create 3D documentation of historical 

buildings. Ulvi (2021) combines UAV and TLS data because TLS is incapable of obtaining roof and tower area. 

According to the findings of Ulvi (2021) it is known that the two techniques can complement each other. Hu et 

al. (2016) merged point cloud data from multiple technologies on the Liyang Yi Temple building, Wudang 

Mountain, Shiyan, Hubei Province, Central China, to determine the building's complete shape. Complex 

architectural structures may be restored using a combination of different technologies (AĞCA et al., 2020; Li et 

al., 2021; Liang et al., 2018).  

The integration of TLS and UAV is possible based on previous research. The Bosscha Observatory has 

point cloud data and photographs, but there is no research on creating 3D models of the building, so this study 

will attempt to combine the two data sets. The results of this study will be compared with data from Wirnajaya 

et al. (2019) to determine if the results of this integration are better to those of previous studies. 

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39 

2. Data and Methods 

We utilized secondary data from previous studies for this investigation. In March of 2019, scans were 

performed using a Topcon GLS-2000 TLS. This results of the study conducted ten scans in the.e57 data format 

(Figure 2a). In April 2019, scanning with a DJI Mavic 2 Pro UAV and ground control point (GCP) coordinate 

measurements were performed. The scan produced 334 images in .JPEG format and 31 coordinate points in the 

WGS 84/UTM Zone 48S system (Figure 2b). 

Afterward, data processing is performed on each data set. The cloud-to-cloud method is used to perform 

a registration process for TLS data processing in Cyclone 2020, after which the accuracy of the values generated 

by the registration process is evaluated. Then, a filtering process is applied to eliminate noise from building 

objects that are no longer required or will be removed in order to concentrate on the desired area. 

 

  
(a) 

 

 
(b) 

 
Source: Analysis, 2022 

Figure 2. (a) TLS scan result and (b) UAV photogrammetry scan result. 

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40 

Furthermore, photogrammetric data processing is carried out on Agisoft Metashape. The first step in 

UAV data processing is the align photos procedure, which is used to identify the image's points. This method 

matches points from two or more photographs. This process will generate a useful initial three-dimensional 

model in the form of a sparse point cloud for the subsequent stage. Following the process of aligning photos, the 

georeferencing stage is performed to provide a three-dimensional X, Y, and Z coordinate reference for the aligned 

photos. This phase also includes the marking of photographs, which is used to identify GCP points on the 

photograph. The location of the marker is determined by the point measured in the field using an object that is 

readily identifiable. Then, the process of optimizing cameras is executed, which aims to realign the photos from 

the preceding process (marking photos and geofencing), which are adjusted to the precision of the camera's 

position from the selected coordinate system. In addition, the process of forming dense point clouds produces 

point clouds with a greater density than sparse point clouds. 

The integration of TLS and UAV data is the subsequent step, which is performed on CloudCompare. 

Generally, processing is performed using the method depicted in Figure 3. The entered data are already in the 

same coordinate system, WGS 84/UTM Zone 48S, because the research area is in Lembang, West Java. The 

data format generated by the TLS data processing is a point cloud in e57 format, while the data format generated 

by the UAV data processing is a dense point cloud in e57 format. The e57 format was selected because it is a 

compact and vendor-neutral file format used for storing and exchanging three-dimensional (3D) imaging data, 

including point clouds, images, and metadata. Numerous applications support the e57 data format. 

The next step is data integration using merge points, so that the data generated by TLS and UAV are 

merged into a single set. To combine the two measurements' data, it is essential that the resulting point cloud 

data have the same coordinate system. Using the Point Pairs Picking registration method, the data integration 

procedure is carried out by selecting the elements of the most prominent object between the two datasets. On 

the edges (edges of the building) and corners of the building, it is possible to select object elements. There are a 

total of 10 points used in the registration process. This registration procedure results in an accuracy of 0.025 

meter. The total number of points resulting from this integration is 213,286,187.  

 
Source: Analysis, 2022 

Figure 3. TLS and UAV data integration process on Cloud Compare. 

 

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41 

Following the successful completion of the TLS and UAV data integration processes, the meshing process 

is the next step. The meshing procedure seeks to reconstruct the 3D model created by combining TLS and UAV 

data. This stage of meshing aims to also bind the data point cloud into a triangular shape and generate a three-

dimensional model's surface area. Plugins for Poison Surface Reconstruction are used to generate the mesh. This 

plugin has a dense point cloud density and works well with closed objects. The parameter used is the octree 

depth; the greater the value of the octree depth, the finer the mesh results; however, the greater the value used, 

the greater the time and memory requirements. In this study, a depth of 11 octrees was used to generate a shape 

that closely resembles the actual situation. 

3. Result and Discussion 

The build texture is the final step following the formation of the mesh. The objective of this step is to add 

color and texture to the three-dimensional (3D) model created in the previous step so that it closely resembles 

the appearance of the real object. The Portion of Visible Sky (qPCV) parameter is used to calculate the 

illumination from point clouds (or mesh nodes) to provide a texture, color, and light that closely resembles the 

actual situation on the ground. This parameter works well with objects that have closed shapes; otherwise, the 

produced light will reach points in the front and back, resulting in unreal (unlikely) results and a lack of contrast. 

More data will produce smoother results, but it will take longer and require more memory. Figure 4 is a visual 

representation of the registration, meshing, and texturing processes. 

   
(a) (b) (c) 

Source: Analysis, 2022 

 
Figure 4. (a) The results of the TLS and UAV registration processes, (b) meshing using octree depth 11, and (c) 

texturing objects. 

As the final phase of this processing, a solid three-dimensional model of the Bosscha Observatory building 

is created using the SketchUp Pro after the texture formation stage of TLS and UAV data integration has been 

completed. As a result of the TLS and UAV data integration process, a point cloud representing a solid 3D model 

of the building was produced as shown in Figure 5. 

The TLS measurement data obtained from Wirnajaya et al. (2019) proved to be insufficient, particularly 

on the building's roof. This is possible because the acquisition process is influenced by the tool's distance from 

the object, which impacts the angle at which the object is captured. According to  Reshetyuk (2009), if the 

distance between the instrument and the object is too near, the viewing angle will be reduced. This will have an 

effect on the roof structure of the Bosscha Observatory, which cannot be modeled accurately. On the other hand, 

the distance between the tool and the object is an essential planning parameter prior to TLS acquisition. 

According to Achille et al. (2015), as distance increases, so does resolution. To take measurements of relatively 

small objects, it is necessary to readjust the correct distance; in this case, the scanned object has a surface area of 

approximately 552 m2. Even though there are limitations on the measurement distance, the obtained RMSE 

results are relatively accurate, at 0.015 meters. 

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42 

  
(a) 

 

 
(b) 

Source: Analysis, 2022 

Figure 5. Bosscha Observatory 3D model (a) front view and (b) rear view created using SketchUp Pro. 

Compared to the Maharani et al. (2020), study, the UAV measurement data applied in this research is 
slightly different. In this study, there were only 334 photos and 31 GCP points, compared to 362 photos and 38 
GCP points in  Maharani et al. (2020). Due to the lack of overlapping images, the difference in the number of 
photographs that used will impact the alignment procedure. In  Maharani et al. (2020), GCP points were only 
distributed on the cylindrical portion of the building because the roof was constantly moving for observatories 
purposes. The variance in GCP points influences the GCP marking procedure. As shown in Figure 6, there is no 
GCP point available at the building's rear, so this component cannot be properly bonded during the 
georeferencing procedure. Due to these disparities in data, an RMSE of 0.3 meters was obtained in this study, 
which is significantly different from the RMSE generated by TLS. 

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Source: Analysis, 2022 

Figure 6. Distribution of GCP points at the Bosscha Observatory. The red circle shows areas that do not have 
GCP points due to differences in data between this study and Maharani et al. (2020). 

In the process of integrating TLS and UAV data, the TLS data is used as a reference because its RMSE 

value is significantly better than that of the UAV data. However, because the data on the roof of the building 

cannot accurately represent the actual object, UAV data is utilized to compensate for the shortcomings of the 

TLS data on the roof. Using the 10 points that are identical in both sets of data, it is possible to integrate the 

data and obtain an RMSE value of 0.025 meters. Even though the RMSE UAV value is measured in centimeters, 

the TLS and UAV integration results are measured in millimeters. This is in accordance with the statement 

Mikrut et al. (2014) that the accuracy of the final object can be improved by combining laser scanning and 

photogrammetry. 

To determine the accuracy of the three-dimensional model derived from the integration of TLS and UAV 

data, the distance between the integrated model and the TLS registration results on objects visible in both 

models is compared. Comparing the distance between the two models yields an RMSE of 0.001 meters. The 

distances between the two models are compared in Table 1. 

Based on the differences in distance between the two models, a statistical method was used to assess if the 

TLS and UAV integration results differed significantly from the TLS data. Calculations for the statistical test 

were performed using the t-distribution with a 95% confidence interval. The results of statistical test calculations 

utilizing the t-distribution method indicate that all measurement results from the three-dimensional model of 

the Bosscha Observatory have been accepted, i.e., they are already within the interval of lower values and upper 

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values in comparison to the results of the comparison of distance sizes from registration measurements of data 

processing TLS. This indicates that there is no significant difference between the two data sets. 

Table 1. Distances between the two models, where Xi is the size of the average distance of the 3D model as a 
result of data integration obtained from three measurements; Xi-Yi is the size of the average distance from the 

registration results of TLS data processing 

No Objects TLS Data Integration Xi-Yi (m) (Xi-Yi)² (m) 

Xi (m) Yi (m) 

1 A01 1.844 1.845 -0.001 0.0000004 

2 A02 1.853 1.851 0.002 0.0000028 

3 A03 2.371 2.372 0.000 0.0000001 

4 A04 2.371 2.370 0.001 0.0000018 

5 B01 1.257 1.256 0.002 0.0000028 

6 B02 1.257 1.258 -0.001 0.0000010 

7 B03 1.594 1.593 0.001 0.0000010 

8 B04 1.593 1.592 0.001 0.0000010 

9 C01 1.479 1.478 0.000 0.0000001 

10 C02 0.938 0.940 -0.002 0.0000054 

11 C03 2.095 2.094 0.001 0.0000004 

12 C04 1.258 1.259 -0.001 0.0000010 

13 C05 1.255 1.253 0.002 0.0000054 

14 C06 1.255 1.254 0.001 0.0000018 

15 D01 1.598 1.597 0.000 0.0000001 

16 D02 1.599 1.598 0.001 0.0000010 

17 D03 1.255 1.253 0.001 0.0000018 

18 D04 1.256 1.256 0.001 0.0000004 

19 E01 1.599 1.598 0.001 0.0000018 

20 E02 1.600 1.600 0.000 0.0000000 

21 E03 1.685 1.685 0.000 0.0000001 

22 E04 1.684 1.683 0.001 0.0000018 

23 F01 2.292 2.293 -0.001 0.0000004 

24 F02 2.291 2.291 0.000 0.0000000 

25 F03 6.016 6.014 0.002 0.0000028 

26 F04 0.216 0.214 0.001 0.0000018 

27 G01 6.016 6.016 -0.001 0.0000004 

28 G02 0.212 0.209 0.003 0.0000071 

  1.59524E-06 

  RMSE 0.001263027 

Source: Analysis.2022 

4. Conclusion 

The results of data integration between TLS and UAV have been implemented successfully and can be 

used as one of the measurement techniques supporting 3D modeling and compensating for the shortcomings of 

each tool. The 3D model of the exterior of the Bosscha Observatory produced by the integration process and 

TLS and UAV data can be used to approximate actual conditions on the ground. This three-dimensional model 

can be used to create a cylindrical portion of a building and the roof of a hemispherical building; the texture and 

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color of the building's details, such as windows, doors, and stairs, can be produced with an RMSE error value of 

0.025 meters. There is no statistically significant difference between the comparison of the TLS distance size and 

the TLS and UAV data integration distance size, based on the results of statistical tests. More research is 

required to determine how to combine different technologies so that the complete shape of an object can be 

created by utilizing the strengths of each technology. 

 

5. Acknowledgments 

This work was supported by Kampus Merdeka Competition Program Research Grant 2021, Geodetic 

Engineering, Institut Teknologi Nasional Bandung. 

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