Internationalof Computational
Intelligence
Systems
Journal ofJournal
Robotics,
Networking andArtificial Life
Vol.6(2);
XX(Z);
Month (Year),Vol.
September(2019),pp.
pp.xx–yy
84–88
DOI: 10.1080/XXXXXXXXXXXXXX; ISSN
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DOI: https://doi.org/10.2991/jrnal.k.190828.003;
ISSNXXXX–XXXX
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Research Article

Feasibility Study of UAV Implementation in Route Surveying
Hazry Desa1, Muhammad Azizi bin Azizan1, Mohamad Syafiq Abdul Khadir1,*, Muhammad Safwan Suhaimi1,Noor Zulaiha Ramli1, Zainudin Hat2
Centre of Excellence for Unmanned Aerial System (COEUAS), Universiti Malaysia Perlis (UniMAP), Kangar, Perlis, Malaysia
IP Fokus Sdn Bhd, Bandar Baru Bangi, Selangor, Malaysia

12

ARTICLE INFO
Article History
Received 21 November 2018
Accepted 02 December 2018
Keywords
UAVengineering work
land survey
drone

ABSTRACT
Unmanned Aerial Vehicle (UAV) are widely used in numerous field and the technology keeps growing. Generally, conventional
method used in data collection for engineering work is tedious and requires a lot of manpower. This research focuses on theinvestigation of the suitability of UAV for route survey. Three stages involved: data collection, data processing and data analysis.
By using Kama Beta, the data was collected. For data processing, Pix4D Mapper is used for point cloud and AutoCAD 2014 forthe analysis stage. The result is mainly focused on extracting the road profile and test the point cloud data using Root Mean
Square Error (RMSE). The result from both methods, conventional and UAV from the point cloud data using RMSE show only
the small difference, with only 6.67% from total 60 points out of tolerance.
© 2019 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
Engineering survey is a survey undertaken for the purpose ofobtaining information essential to the planning of an engineering
project. Engineering survey provides accurate and reliable dimension data through the construction process from the base topographical survey, cross-section and ground modelling information
to cut and fill calculation, dimensional control to setting-out on-site
and finished as-built drawing [1].
Route surveying is comprised of all survey operation required fordesigning and construction of engineering works such as highways,pipelines, canals or railroads. Survey operations in route survey
include topographic survey, cross-section and longitudinal section [2].
Unmanned Aerial Vehicles (UAVs), also known as “drones”, areamong the most important technological advances that have been
introduced to the land surveying industry in quite some time. It canbe piloted by remote and can take detailed survey information while
simultaneously transmitting that data back to the head office. UAVs
create a highly accurate map and provide valuable data to companies
and individuals who are considering major projects on large areasof land. Research about the reliability of these techniques to be used
in topographic mapping was conducted in land survey method [3].

2. METHODOLOGY
2.1. Planning and Preparation
Planning and preparation are needed before the data collection.
Planning and preparation cover the scope of work such as planning
Corresponding author. Email: msyafiq@unimap.edu.my*for the area of the survey and the preparation for the instrument andsoftware used in this project. Good planning and preparation enable
to ensure efficiency and maintaining a high quality of the data collection in this study. Furthermore, proper planning and preparation help
in saving time and cost as time increase, the cost will increase as well.

2.2. Data Collection
Data collection for this study is focused on two methods. The
first method of data is used as a conventional method by totalstation. Besides that, the second method for this study used a
new method by UAV.

2.3. Data Processing
Data processing for this study refers to process the laser and theconventional method. Data processing for a conventional method
uses the Civil Design & Survey software. Besides that, for the UAV
processing used Pix4D Mapper. Both use AutoCAD as data analysis
processing.

3. RESULTS AND ANALYSIS
3.1. Camera Calibration
The camera calibration is done before doing the flight planning.
The purpose of camera calibration is to determine the focal length,principal point, and lens distortion from the camera. This process
was carried out to figure out the unstable element in the camera
like interior orientation and lens distortion parameters [4].

H. Desa et al. / Journal of Robotics, Networking and Artificial Life 6(2) 84–88

In this project, the camera calibration is processed using Photo­
Modeler Software. This software is an automatic lens calibration,
which uses A1 paper sheet as a calibration target. It supports estimations of the full camera calibration matrix, including non-linear
distortion coefficient.
To check the accuracy of the camera calibration results, the total
final error must be checked. In both methods, the data sets are not
the same. For lab calibration, the data set is the grid of the pattern and for filed calibration are the 67 targets points. According to
PhotoModeler tutorial, a value <1.0 pixel indicates a good calibration and very good calibrations can have a final total error smaller
than 0.4 pixels (www.photomodeler.com). In this case, the lab calibration has a final error of 1.940 pixels [7].
The total error was a bit higher than the recommended. The fieldcalibration has a total error of 0.282 pixels, which is assumed to bea very good calibration project. Also checking the marking residuals is a good way to test the calibration quality [4]. PhotoModeler
tutorial recommended having the largest marking residual <1.0pixel [3]. In both cases, the largest marking residuals are <1.0 pixel.
The lab calibration has 0.723 pixels and the field calibration 0.700
pixels (Table 1).

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3.3. Digital Surface Model
The aerial image was taken from above with 14 min flight duration.
To achieve this project the overlap is 80%, sidelap is 70% and thealtitude is 110 m. This overlapping and flight altitude is required
to avoid potential missing areas of coverage and to ensure goodcoverage. Based on the data that has been processed using Pix4D
software, produced an Orthophoto and Digital Surface Model(DSM) in raster as shown in Figures 1 and 2.
Figure 3 shows the sample result road profile of UAV and TS data
for CH 1 to CH 10, which is located in Chuping, Perlis, Malaysia.
Based on Figure 3, the red color refers to the highest elevation while
the soft blue color refers to the lowest elevation [5]. Therefore, it
can clearly be seen that the UAV data gives a greater number ofpoint compared with TS data. Besides that, the profile generated
by using UAV is denser than the TS data due to the great numberof point [6].

The accuracy of field calibration was also checked to compare theGlobal Positioning System (GPS) coordinates of the targets with thecoordinates obtained with PhotoModeler. This software only needs
three control points to change from relative to absolute coordinates.
For this process, the targets number one, two and four were used. The
planimetric and the altimetric Root Mean Square (RMS) were calculated. The planimetric RMS was 0.028 m and the altimetric 0.026 m,
which is really small and indicates the accuracy level of the project [7].

3.2. Georeferencing
Ground Control Point (GCP) and Verification Point (VP) arevery important data that are used to involve in aerial triangulation
phase, where it can be identified on the ground features and can begeoreferenced with the aerial image used in geometric correctionof the distorted image [3]. Therefore, pre-marking for GCP’s andVP’s must be carried out before a flight mission. This is because theprocessing of the tie point image could be more efficient and accurate. The coordinates of x, y and z-value that were observed usinglevelling method (land survey).

Figure 1 | Location of GCP in UAV images in Chuping, Perlis, Malaysia(Orthophoto).

Figure 1 shows the strategic location of placement six (6) GCP’s. The
location of GCP was in Chuping, Perlis, Malaysia. Each of GCP isobserved by GPS equipment (Topcon – GR5) using Static Method.
The reason of this method selection is because this method is themost widely used differential technique for control and geodetic
surveying, involved 1 h of observation to resolve the integer ambiguities between the satellite and the receiver. By using this method,
the accuracies in the sub-centimeter range can be obtained.
Table 1 | Total final error and residuals of the camera

Final total error (pixel)
Largest marking residual (pixel)
Overall RMS (pixel)

Labcalibration

Field calibration(50 m flight high)

1.940
0.723
0.245

0.282
0.700
0.341

Figure 2 | DSM of the project area (Location: Chuping, Perlis, Malaysia).

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H. Desa et al. / Journal of Robotics, Networking and Artificial Life 6(2) 84–88

Figure 3 | RMSE error verification points.

Figure 4 | RMSE error cross-section.

Figure 5 | Residual error XY.

3.4. Statistical Analysis
The road profiles are tested with RMS Error (RMSE) which is doneby analyzing the comparison of differences between and within thegroup. The road profile is statistically tested by using the RMSE test
at each chainage to ensure that the data of UAV and TS is significant to each other.

where Σ = summation of, (zfi – zoi)2 = (differenced, squared),
N = sample size.
There are three types of sample tested using RMSE formula, verification point, positioning and elevation. Values from the real measurement are compared with the value from the 3D modelling to
get the error of every sample.

Root mean square error is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression
line data points are; RMSE is a measure of how to spread out theseresiduals are. In other words, it tells you how concentrated the datais around the line of best fit. RMSE is commonly used in climatology,
forecasting, and regression analysis to verify experimental results [5].

Based on Figure 3, it can be seen that the graph values of RMSE VP arefor minimum and maximum errors. All the VPs are under tolerance
which is below ±0.10 m. Based on Figure 4, it can be seen that thegraph values of RMSE Cross Section are for minimum and maximum
errors are for Different Elevation (H). The best result is ±0.03 m (VP5).
VP9, VP10, and VP18 are out of tolerance which is above ±0.10 m.

The formula (1) is:

The values of RMSE XY for minimum and maximum errors canbe referred to Figure 5. All the XY are under tolerance which isbelow ±0.10 m except at VP19 with value ±0.125 m. These results
might be affected by image matching algorithm that was used inthe same software during image processing. The error was usually1 N (z f − z o )2  2iRMSE fo =  ∑ i 		 N i =1(1)H. Desa et al. / Journal of Robotics, Networking and Artificial Life 6(2) 84–88

caused by flying height during image acquisition, image matching
during image processing and motion movements such as w, p,and k [6].

4. CONCLUSION

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conference. High appreciation to UAS research team for excellent
contribution. Special gratitude to IP Fokus Sdn Bhd for full support
in completing this project.

REFERENCES

As a conclusion, the three objectives of this study has been achieved.
The first objective is to process image capture from UAV. The mostimportant procedure for route survey work is data collection.
Planning and preparation in this study focusing on planning for anumber of GCP and flight planning. Number of GCP depends ona number of flight planning where need minimum visible from theaerial view and location of GCP. After that, the procedure for routesurvey by using UAV need to focus on processing the point clouddata. Starting with data registration point cloud data uses minimum of three images that visible each other. Then, the next process
is point cloud densification using PIX4D Mapper software. Then,the next process produces DSM and Orthophoto. In this research,
two methods were applied, land survey and UAV survey.
A land survey is used as a benchmark for this research. Point clouddata produce in 2D data with 3D coordinate which can use to generate road profile for route survey. The third objective for this study
is to analyse the accuracy between land survey data and UAV data.
Series of test and analysis have been conducted in terms of the reliability of UAV data in completing a route survey. The reliabilityof UAV data shows in quantitative assessment by comparing resultroad profile from UAV and TS method.

CONFLICTS OF INTERESTThe authors declare they have no conflict of interest.

ACKNOWLEDGMENTS
The author acknowledges International Conference on Artificial
Life and Robotics (ICAROB) for giving the opportunity to join this[1] A. Ahmad, K.A. Hashim, A.M. Samad, Aerial mapping using
high resolution digital camera and unmanned aerial vehicle
for Geogra­phical Information System, 2010 6th InternationalColloquium on Signal Processing & its Applications, IEEE,Mallaca City, Malaysia, 2010, pp. 1–6.
[2] H. Eisenbeiss, A mini unmanned aerial vehicle (UAV): system
overview and image acquisition. International workshop on processing and visualization using high-resolution imagery, institutefor geod­esy and photogrammetry, ETH, Hoenggerberg, Zurich,Switzerland; 2004.
[3] J. Everaerts, The use of unmanned aerial vehicles (UAVs) for
remote sensing and mapping. International Archives of The
Photogrammetry, Remote Sensing and Spatial Information
Sciences (ISPRS Archives), Beijing, 2008, pp. 1187–1191.
[4] V. Peterman, M. Mesarič, Land survey from unmanned aerial­vehicle. International archives of the photogrammetry, remotesen­sing and spatial information sciences, XXXIX-B1 (2012),447–451.
[5] M. Pérez, F. Agüera, F. Carvajal, Digital camera calibration using
images taken from an unmanned aerial vehicle. International
Archives of the Photogrammetry, Remote Sensing and SpatialInformation Sciences, XXXVIII-1/C22, Conference on Unmanned
Aerial Vehicle in Geomatics, Zurich, 2011, pp. 167–171.
[6] A.M. Samad, N. Kamarulzaman, M.A. Hamdani, T.A. Mastor,K.A. Hashim, The potential of Unmanned Aerial Vehicle (UAV)
for civilian and mapping application. 2013 IEEE 3rd International
Conference on System Engineering and Technology, IEEE, ShahAlam, Malaysia, 2013.
[7] H. Desa, M.A. Azizan, S. Suhaimi, Z. Ramli, M.S. Abdul Khadir,Z. Hat, Feasibility Study of UAV Implementation in Route
Surveying, The 2019 International Conference on Artificial Lifeand Robotics (ICAROB2019), 24 (2019), 712–716.

Authors Introduction
Prof. Ts. Dr. Hazry Desa

Mr. Ts. Muhammad Azizi bin Azizan
He is a Director of Center of
Excellence for Unmanned Aerial
System at University of Perlis(UniMAP). He has published many
publications and filled patents on theapplication of UAV for crop spraying and monitoring. He leads theeducational program and organized
several UAS related conference inMalaysia. He is a member of IEEE.

He received his MSc degree fromUni­­versity of Technology Mara(UiTM) in Integrated ProjectManage­ment. He is a Non-ExecutiveGroup Director, Aurum Group andVice President (Malaysia Global
Economic Chamber of Commerce).
Registered as CIOB UK, CIBSE UK,HRDF Malaysia and RISM Malaysia.
Nature of research are Property,Construction, Consultation, Manufacturing and Supply.

H. Desa et al. / Journal of Robotics, Networking and Artificial Life 6(2) 84–88

88

Mr. Mohamad Syafiq Abdul KhadirNoor Zulaiha Ramli
Graduated from UiTM Arau in 2014.
Works as Geospatial Engineer andResearch Assistant under Center of
Excellence for Unmanned Aerial
System (COEUAS) UniMAP.

Graduated in MSc in MechanicalEngineering,form
UniMAP.
Currently, works as a lecturer inFaculty of Technology Engineering,
Mechanical Department. Interested
in UAS, Heat Transfer, Automotive
and Green Technology.

Mr. Muhammad Safwan Suhaimi
Graduated from UiTM Arau in 2014.
Works as Geospatial Engineer andResearch Assistant under Center of
Excellence for Unmanned Aerial
System (COEUAS) UniMAP.

Mr. Zainudin Hat
The owner of IP Fokus Sdn Bhd
Actively involved in researchfor Unmanned Aerial System(UAS), Building surveying andConstruction.