1. Introduction

1.1 Background
The last decade has seen manifold growth in the use of aerial survey LiDAR (Light Detection and Ranging) technology. Due to the main advantage of measuring topography through highly dense and accurate data points which are captured at high speed, the LiDAR technology has found several interesting applications (Lohani, 2001; Queija, et al., 2005).

1.2 Object-oriented software engineering
Software engineering has traditionally been an expensive and time-intensive process. Object-oriented analysis and design is the principal industry-proven methodology that answers the call for a more cost-effective, faster way to develop software and systems. Object-oriented technology cuts development time and overheads, giving time to market and thus significant competitive advantage, enabling software engineers to produce more flexible and easily maintainable applications.

1.3 Characteristics of LiDAR simulator
A LiDAR simulator is aimed at faithfully emulating the LiDAR data capture process with the use of mathematical models under a computational environment. Basically, data generated by simulator should exhibit all characteristics of data acquired by an actual LiDAR sensor. Literature reveals that only a few attempts have been made by researchers to develop a simulator for LiDAR. These efforts are limited in their scope as either these consider the effect of only single parameter on one kind of object (Holmgren et al., 2003) or inaccurate scanning pattern (Beinat and Crosilla, 2002). Another attempt is made (Kukko and Hyyppa, 2007) using MATLAB however the simulator has limitations as it is designed only for test purpose and does not offer flexibility and completeness. More focussed and comprehensive efforts have been made to simulate the return waveform from a footprint (Sun and Ranson, 2000; Tulldahl and Steinvall, 1999).

1.4 Requirement of LiDAR simulator software
Considering the significance of LiDAR, there is a need to introduce LiDAR technology to students at undergraduate and postgraduate level. LiDAR instrument and data are costly. Collecting LiDAR data with varied specifications, as are desired for classroom teaching and laboratory exercises, may not be viable considering the cost and availability of the instrument. A simulator can generate various kinds of data, as and when desired, at minimal or no cost. This data could be very useful for conducting laboratory exercises. User control over the entire data generation process in simulator can also help students in understanding the functioning and limitation of LiDAR instrument. Further, error sources and their effect on LiDAR data can be understood. In any laboratory exercise, availability of ground truth is fundamental.