Aerial Photogrammetry and Object-based Image Analysis for Bridge

Mapping: A Case Study on Bintan Bridge, Riau Islands, Indonesia

Husnul Kausarian

, Muhammad Zainuddin Lubis

, Primawati

, Dewandra Bagus Eka Putra

, Adi

Suryadi

and Batara

Department of Geological Engineering , Universitas Islam Riau, Pekanbaru, Indonesia

Department of Informatics Engineering, Politeknik Negeri Batam, Batam-Kepulauan Riau, Indonesia

Mechanical Engineering, Engineering Faculty, Universitas Negeri Padang, Padang-West Sumatra, Indonesia

adisuryadi}@eng.uir.ac.id, batarabtr@gmail.com

Keywords:

Photogrammetry, Unmanned Aerial Vehicle (UAV), Bintan Bridge, Structure, Specify Second Distance.

Abstract:

Photogrammetry is a good method for determining the geometric properties of an object from the images. The

geometry of the object obtained from two or more drawings that are overlaid. It is completely autonomous,

ultra-lightweight so-called Unmanned Aerial Vehicle (UAV) which has been commercially available at very

economical prices in the community or researchers, and photogrammetric applications. The study area was

located at Bintan Island, Riau Islands Province, Indonesia, collecting data on 8th may, 2017 (1

◦

3’45.98”N

- 104

◦

27’49.22”E), with DJI phantom 4 including control range small format air photography (SFAP) which

is a low-cost, cost-effective solution for obtaining bridge surface imagery and can also be proposed as a

long-distance bridge inspection technique to complement the current bridge visual inspection in Indonesia.

Some examples of evaluations on bridges using SFAP are presented to provide remote sensing information

and capabilities that serve as an essential tool for monitoring and assessing the construction of the bridge.

The measurement of Bintan Bridge is 193 m, the photos were taken from the airplanes around 70 meters and

providing top-down views. Bintan Bridge’s structure have specify second distance in left wide is 1.057 <

1560, and specify second distance in right wide is 0.9981 < 1570.

1 INTRODUCTION

Modelling on object information with building in-

stances becomes a famous technology for some in-

frastructure such as bridges, road systems, tunnels,

dams, water, and sewage networks. In the Riau Is-

lands, Bintan Island does not have much informa-

tion on mapping air information using remote sensing

techniques on building any object, and buildings mod-

elling information is very limited. The latest map-

ping information on the Riau islands is still domi-

nantly small, and still in the general mapping of ob-

jects such as sea grass beds, settlements, seaﬂoor (Lu-

bis and Daya, 2017; Lubis et al., 2017; Farizki and

Anurogo, 2017; Kausarian et al., 2016b; Kausarian

et al., 2016a; Kausarian et al., 2017; Kausarian et al.,

2018; Kausarian et al., 2019).

In terms of the image analysis, the contempo-

rary and existing construction is one of the signiﬁ-

cant problem (Agapiou et al., 2015; Patraucean et al.,

2015; Tang et al., 2010; Volk et al., 2014; Cuca

et al., 2014; Kausarian et al., 2017). The mapping

on the current geomorphology ﬁeld relies more and

more on automatic techniques that serve to classify an

image from the results of remote sensing techniques

and a digital elevation model (DEMs) (Lardeux et al.,

2016). Parameters are seen in the morphometric sec-

tion, such as the slope or curvature of the region or

the inherited object for the result of characterization

of the shape and result of the geomorphological pro-

cess. Arithmetic in the process of operation with the

drawing band can clarify the class on a particular ob-

ject. The vegetation index is often also used to clas-

sify vegetation and separate objects from other classes

in a remote sensing data. In the object image of the

entity on the basic technique in the drawing (in our

case is the bridge), in each group of objects in the im-

age, the pixel results consist of the same digital value,

and has a relationship to the intrinsic size and shape,

and the intrinsic ecology real-world scene component

is a model (Hay et al., 2001). In the results of a re-

cent study informed by the American Civil of Society

Kausarian, H., Lubis, M., Primawati, ., Putra, D., Suryadi, A. and Batara, .

Aerial Photogrammetry and Object-based Image Analysis for Bridge Mapping: A Case Study on Bintan Bridge, Riau Islands, Indonesia.

DOI: 10.5220/0009185802370242

In Proceedings of the Second International Conference on Science, Engineering and Technology (ICoSET 2019), pages 237-242

ISBN: 978-989-758-463-3

237

(Chen et al., 2009) indicate that conditions improve

on all systems in the current infrastructure, with ob-

jects such as roads and bridges.

UAVs have been used successfully in a research,

for mapping (Hardin and Jackson, 2005; Kausarian

et al., 2018), for the nitrogen and biomass measure-

ment in a plant object (Izumi et al., 2018; Izumi et al.,

2019; Widodo et al., 2018; Widodo et al., 2019),

for document the crop at the value of water pressure

(Berni et al., 2009), and for results on rangeland veg-

etation mapping. In this study, we were interested in

the utilization of aerial mapping object-based image

(Bintan Bridge) for structure and position analyst in

Kepri islands, Indonesia

2 DATA AND METHODS

2.1 Study Location

Our study site was located on Bintan Island, Riau Is-

lands, Indonesia, collecting data on 8th May 2017 (

◦

3’45.98”N - 104

◦

27’49.22”E) (Figure 1). The area

over which imagery was acquired was located within

1.5 km of is Bintan Bridge.

2.2 Study Location

Agisoft Photoscan Professional version 1.2.7 build

3100 64 bit was used to extract all mosaic from quad-

copter DJI phantom 4 (Figure 2) and the tiles of

point cloud data converted into photogrammetry im-

ages. The 1-m resolution DTM used for normalizing

the terrain model on the object based images (Bintan

Bridge) height, and photo captured of Bintan Bridge

with altitude 70 meters can be seen in Figure 2.

2.3 Flight Planning

The take-off and landing operations are strongly

linked to the vehicle and the level of characteristics

employed, but can usually be controlled from the

ground by the pilot (e.g. with a remote controller).

Research mission (aviation and data acquisition) is

planned in laboratory with special software, starting

from the area of interest (AOI), the distance of re-

quired soil sample (GSD) or path, and knowing what

is the intrinsic parameter of camera digital installed

from DJI Phantom 4. During the ﬂight process, the

platform is usually observed with a control station

showing real-time ﬂight data such as position, speed,

stance and distance, GNSS observations, battery or

fuel status, rotor speed, altitude, etc.

3 RESULT AND DISCUSSION

Nearly 325 images (Figure 4) obtained through pho-

togrammetry and object-based image analysis (Bintan

Bridge) in Figure 3. 4-rotor quadcopter DJI phantom

4 with GNSS addition system equipped with 12 MP

pixel camera, GPS & GLONASS satellite system, -

◦

to + 30

◦

Pitch Gimbal Control Range. The mea-

surement of the Bintan Bridge is 193 m, measured by

Agisoft software, in solid mode and in shaded mode

as shown in Figure 4.

Image objects for each individual on the bridge

are simultaneously archived for viewing in a geo-

graphic information system (GIS) (Ellenberg et al.,

2016; Yeum and Dyke, 2015; Reagan et al., 2018).

The technique used in the results is ”manually match-

ing features” with mosaic images already available

from ArcMap 10.2 and Agisoft Photoscan Profes-

sional. The classiﬁcation process is used to extract the

information performed by clarifying different colours

to each object class to distinguish them by rapid iden-

tiﬁcation. Figure 4 shows the results of the object

detection of the bridge structure shown as a feature

with darker pixels than surrounding adjacent images

and may be marked as possible identiﬁcation objects.

By viewing the surface with visuals and other surface

conditions may also appear like cracks seen in bridge

structures and ﬁgures. The same problem will arise

if the colour identiﬁcation technique used on the sur-

face of the concrete. The ﬂight crew must function

after the process of airing and ﬂying near the point of

the camera direction that has been set for each bridge.

Accurate communication of the cockpit and air trafﬁc

control ofﬁcer is essential in completing the research

safely. Identifying the large and wide bridge objects

on the surface is relatively easy to observe. Analy-

sis of the bridge object and counting the width using

the method of air photography effectively identiﬁes

the width which is 6 m and signed as a yellow colour

in Figure 5 which is also shows the condition of the

bridge connection and expansion.

The length of Bintan Bridge is 193 m from image

processing using Agisoft Software (Figure 4). Exten-

sive recapitulation of the entire procedure for generat-

ing the same 3D cloud point as the results of this study

can be found on applying the Scale Invariant Feature

Transform (SIFT) to perform button detection as in-

troduced (Lowe, 2004). The existence of ﬁeld process

that occurs in trafﬁc on the highway is always outside

the control of the ﬂight pilot to take the photos. The

area of the sweep by the pilot in the process of trafﬁc

can be such that large areas on the road surface are

not blocked by car objects. When performing aerial

photography analysis for this study, 2 (two) cars were

ICoSET 2019 - The Second International Conference on Science, Engineering and Technology

238

Figure 1: The Map of Research location and Object Based (Modiﬁed from Google Earth).

Figure 2: The image of Bintan Bridge, captured by Quadcopter DJI Phantom 4.

Figure 3: The point cloud computed with PhotoScan.

Figure 4: Automated image perspective 30 0 results for the UAV, left: with solid mode, right: with shaded mode.

present at the bridge statically. Taking vehicles from aerial photography is required (Figure 6). Structure

Aerial Photogrammetry and Object-based Image Analysis for Bridge Mapping: A Case Study on Bintan Bridge, Riau Islands, Indonesia

239

Figure 5: Aerial track object identiﬁcation, left: original image, right: zoomed image.

Figure 6: Detection of two vehicles, left: original image, right: zoomed image.

Figure 7: Above: Structure of Bintan Bridge, left below: Specify Second Distance in Left Wide (1.057 < 1560), right below:

Specify Second Distance in Right Wide (0.9981 < 1570).

of Bintan Bridge can be seen in Figure 7 that have

specify second distance in left wide in range 1.057

< 1560, and specify second distance in right wide is

0.9981 < 1570.

4 CONCLUSIONS

In this study, remote sensing is used as a tool in or-

der to see the structure of Bintan Bridge with high-

resolution air photography. The procedures of image

processing and data collection as a detailed process

are described. The results of this study indicate that

ICoSET 2019 - The Second International Conference on Science, Engineering and Technology

240

the technology of remote sensing is able to detect very

clear objects and bridge structures. Accurate posi-

tioning and tracking results through bridges are done

without a robust test and the use of GPS units included

in the Instruments used on the correct scale to suit the

needs. It is necessary for pilots to remember that the

wide area augmented system (WAAS) enables GPS

accuracy of up to 6-8 inches/pixels. In this paper, we

discuss the process of making the whole drawing on

the object of the bridge by separating other objects,

including plants, etc. to keep in view the focus of the

Bintan bridge structure results, and the vehicles of the

aerial photogrammetr.

ACKNOWLEDGEMENTS

This study is supported by P2M, Geomatics Engi-

neering Program, Batam State Polytechnic, Kepu-

lauan Riau, Indonesia, and Engineering Geological

Program, Universitas Islam Riau.

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