stanbul 2004 
DIGITAL TERRAIN AND AUTOMATED MAPPING SYSTEM DEVELOPMENT USING 
UNIFIED MODELLING LANGUAGE (UML) 
Alias Abdul-Rahman' and Jae-Hong Yom” 
' Institute for Geospatial Science and Technology 
Universiti Teknologi Malaysia 
81310 Skudai, Johor, 
Malaysia 
aliac(z7n 2 
alias @fksg.utm.my 
“Department of Geoinformation Engineering 
Sejong University 
98 Gunja-Dong, Gwangjin-Gu, Seoul, 
South Korea 
jhyom(Zsejong.ac.kr 
ABSTRACT: 
Automated mapping and digital terrain system could be developed by using several approaches where an object-oriented technique is one 
ofthem. The technique has been considered by many software engineers and developers as one of the most efficient approaches. This 
paper discusses the design and development of digital terrain and automated mapping system using the Unified Modelling Language 
(UML). The design and the development aspects will be highlighted and forms major discussion of the paper. The digital terrain 
modeling component is based on Triangular Irregular Network (TIN) data structure. We will demonstrate the system by using 
photogrammetrically captured data. The application of the system will be demonstrated and finally we will highlight the system's 
outlook. 
KEY WORDS: Modelling, Design, Automation, Software, DEM/DTM, GIS 
1. INTRODUCTION 
UML is a notation set for building object models. It is the 
standard for communicating the meaning of object modeling 
diagrams throughout the industry. UML is a language for 
designing a conceptual schema using object-oriented 
programming (OO) concepts, and enhancement of the widely 
used Entity Relationship Model (ER Model). It is currently 
one of the most common languages for conceptual data 
modeling and system development. We could utilize this tool 
to model any GIS spatial modeling tasks as well as for 
mapping and digital terrain problems. Basic concept of the 
UML modeling is not discuss here but rather a discussion how 
to use and implement a system based on the language. Readers 
may refer to several texts on the basic of UML such as Wood 
(2002) and Laurini (2001). Here we focus on the design of the 
conceptual data model, the first step in the modeling process. 
This paper addresses the development of automated mapping 
in Section 2 and digital terrain using the modeling language 
(UML) in Section 3. Finally, we conclude the experiments and 
the system outlook. 
2. UML BASED AUTOMATED MAPPING SYSTEM 
An experiment was done at the Glasgow University for the 
development of an automated mapping system by utilizing the 
UML tool (Yom, 2000). 
An automated mapping system is defined as a composition 
of subsystems namely; the image acquisition subsystem, 
positioning subsystem, triangulation subsystem, object 
297 
extraction subsystem and the visualisation subsystem. A 
practical level of automation is assumed where human 
interaction is still required at many stages. 
2.1 Image acquisition subsystem 
The image acquisition subsystem of the automated mapping 
system, involves a human operated platform, such as a fixed 
wing airplane or a helicopter. The imaging sensors are a key 
component of the imaging subsystem. Some examples of 
imaging sensors are CCD digital cameras, video cameras, 
linear array scanners, laser profiling scanners and multi- 
spectral scanners. Use case diagram of the image acquisition 
subsystem is as shown in Figure 1. 
2.2 Positioning subsystem 
In an automated system the imaging sensors of the image 
acquisition subsystem (e.g. CCD cameras) are synchronized 
with the positioning sensors to compute the attitude and the 
position at the instant of the image capture. Some of the most 
popular positioning sensors in use today are GPS (Global 
Positioning System) receivers and IMU (Inertial Measurement 
Units). The GPS receivers provide the positional coordinates 
of the exposure stations whereas the IMU provides the 
attitudes of the images at the instant the exposure. Figure 2 
shows the use case diagram of the positioning subsystem. 
2.3 Triangulation subsystem 
Triangulation is defined as the task of locating the ground 
position of any points seen on the image by using their 
observed image coordinates and the values obtained from the