© 2015, IJARCSSE All Rights Reserved Page | 175
Volume 5, Issue 5, May 2015 ISSN: 2277 128X
International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com
Comparative Study of Selected Data Processing Software Using
Massive Point Cloud Data 1D. Asenso-Gyambibi,
2Y. Issaka and J. Oteng,
3R. Arkoh
1CSIR-Building and Road Research Institute, Ghana
2University of Mines and Technology Dept. of Engineering, Ghana
3Rabotech Ghana Limited, Ghana
Abstract- Modern geographic data acquisition technologies such as Light Detection and Ranging (LIDAR),
Photogrammetry and Remote Sensing generate point clouds in the range of billions of elevation points. Point clouds
have become important geo-spatial data for modern science applications like large scale mine survey, environmental
impact assessment, flood analysis, large scale engineering surveys especially in areas where site accessibility is
difficult. The application of laser scanners for capturing point cloud data efficiently demands computers with
enormous storage space, computing power, display capabilities and special technical skills. This study therefore
investigates the abilities, and makes comparative analysis of various I.T. (Information Technology) software platforms
in handling massive point cloud data. Various software were used to manage the same volume of data and analysis
carried out. It became clear from the results that each software platform has its own strength and weakness in
application for point cloud data processing. However, good planning and database design is also critical.
Key words: Laser Scanning, LIDAR, Surveying and Mapping, Point Cloud, Geospatial software
I. INTRODUCTION
Point cloud is 3-dimensional positions, possibly associated with additional information such as colours and normal
and can be considered sampling of a continuous surface [Zhiqiang and Qiaoxiong, (2009)]. The term “Cloud” reflects the
unorganized nature of the set and its spatial coherence, however, with an unsharp boundary. A geo-referenced point
cloud is given in an earth-fixed coordinate system; e.g. Earth-centred system, like WGS 84 (World Geodetic System,
1984) or in a map projection with a specified reference ellipsoid, e.g. UTM (universal Transverse Mercator).
Each point "P" has three co-ordinates (x,y,z) and may have additional attributes [Otepka et al, (2013)]. Demand for
high resolution geo-spatial data with immense attributes is on the rise. The use of traditional methods in acquiring such
data is not as efficient as using modern technologies such as LIDAR, Remote Sensing and Photogrammetry [Carter et al,
(2012)]. Lidar however produces timely, accurate and high quality data that address a number of applications
(Richardson, K. (2013]. Such technologies have found applications in the following:
Terrestrial Surveys: Supporting large scale construction projects, exploration and development of oil and gas
and mineral resources, dimensional control, structural monitoring, as-built surveys, ecological assessment
surveys, etc.
Hydrographic surveys:
Supporting coastal and marine studies using airborne LIDAR bathymetry or echosounder.
Aerial Mapping: Supporting natural resources management, urban planning, economic planning, defense and
emergency response.
Satellite mapping and Geographic Information System applications.
The application of applied research, such as lasers in surveying and mapping in support of planning, designing and
rehabilitation is essential throughout the project delivery process in view of project remote locations and challenges in
terrains. The application of electronic data collection therefore addresses the critical elements of cost and schedule.
Laser scanners are used to obtain point clouds. Laser range scanning provides an efficient way to actively acquire
accurate and dense 3D point clouds of object surfaces or environment (Elseberg et al, 2012). This paper presents a
comparative study of (six) geospatial software in handling and analyzing massive point cloud data. This will enable the
selection of the most appropriate software depending on user requirements.
II. OBJECTIVES OF STUDY
The objectives of the study are:
1. To determine the challenges associated with handling point cloud data
2. To investigate the abilities and make a comparative analysis of ArcGIS, Surpac, Golden software Surfer, Fusion,
Fugro Viewer and ALDPAT in:
Visualization
Gyambibi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),
May- 2015, pp. 175-183
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Measurements
Generating Cross-sections
Gridding
Contouring
Hill shading
Generating Digital Elevation models
Classifications
3- D generation
Creating water sheds and
Slopes
III. METHODOLOGY
3.1 Materials
Computer: The computer required for data processing must have minimum specification as below:
Type: AMD Athlon M. Dual Core Processor 2.10 GHz
RAM: 2.00GB 64 Bit OS Processor
Disk Space: 124.31 GB + 94.47 GB
Free Space: 72.32GB + 41.65 GB
Graphics Adaptor: ATI Mobility Random HD 4200 Series
Available Graphics Memory: 893 MB
Dedicated Video Memory: 256MB
Resolution 1366 x 768
Software
Table 1: Geospatial software used in analyzing the data
Software Developer Cost
Golden Software Surfer
Version (8.06)
Golden Software Inc. $ 849
Esri Arc.GIS ESRI $1,500
Gemcom Surpac Gemcom $600- $950
Fusion Silviculture and Forest Models Team at
the U.S. Forest Service’s Pacific
Northwest Research Station
Open source
Fugro Viewer Fugro Geospatial Services Open source
ALDPAT International Hurricane Research
Center,Florida International University
Open source
Sources of primary data
The primary data was obtained from the following sources:
The Institut Verkhr-Und Raum, Germany
Council for Scientific and Industrial Research-Building and Road Research Institute (CSIR-BRRI, Kumasi-
Ghana): Data was obtained through consultancy projects from the mining sector in Ghana.
Methods
Today’s sensors produce data whose volume may overrun the processing and storage capacities of Data Base
Management Systems (DBMS) [Bayaari S. (2013)]. As a result, the full potential of point cloud data remains
unexploited. Various methods and software are derived to handle data; such as conversion of point cloud data into raster
and use JPEG compression just as is done for imagery, lowering the number of digits for the x, y, z and segmentation of
the point cloud [Lemens, M. (2013)].
According to Fernandez et al, it is impossible to describe all the software on the market that is designed to process
point cloud data. The choice of software depends on budget, user needs, requirement and experience, activities to be
performed, volume of data, computing power and expected results (Fernandez et al, 2007).
New methods are also being proposed for Visualization using Commodity PC (Zhiqiang, Du). There are various
platforms used to handle point data. Each of these platforms has their advantages and disadvantages. The study makes a
comprehensive analysis of six (6) of the most popular and sophisticated software available; but more importantly whose
cost is within the reach of many prospective users of point cloud data and its applications. These software platforms are
used to process the same primary data to generate outcomes relevant for their applications.
Data preparation
Creating working Directory: Data was organized into folders according to data formats and purpose.
Data Conversion: Data were converted from across formats to analyze the size of each format and suitability for
purpose.
Gyambibi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),
May- 2015, pp. 175-183
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Data processing
The point cloud data was processed with the selected Geo-spatial data to create:
Digital Terrain Models (DTM)/Digital Elevation Models (DEM)
Contours
Watershed charts
Cross-sections
IV. RESULTS
The results of the study revealed the strength and weaknesses of the six(6) geospatial software analyzed with the point
cloud data. Depending on user needs, it is useful to evaluate the capabilities of geospatial software before procuring them
to achieve value for money; for example Surpac from the study has been seen to be useful for engineering works though
ArcGIS has the most capability (Table 3) and ALDPAT is least capable.
Challenges with data processing
Software crash/froze on loading primary data in its original format (ECW, BIN, ASCII). Fig. 1 and 2.
Fig. 1 Screen Capture of Crashed Surpac
Fig 2:Screen Capture of Surpac Freezing
Fig.3 Freezing and Crashing of ArcGIS
Gyambibi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),
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Table 2: File formatting
Original Format Size Converted Format Size
GRD 0.99 GB DXF 336 KB
LDA 19.8MB LDI 260KB
ECW 4.55MB ASCII 9.41MB
XYZ 149MB ASCII 9.41MB
ASCII 9.41MB STR 4.00KB
ASCII 9.41MB GRD 1.34MB
BIN 79.7MB ASCII 9.41MB
Fig 4: Contour shade of Surfer
Fig 5: Watershed created with Surfer
Fig. 6: DTM created with Surpac
Gyambibi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),
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Fig 7: Contour created with surpac
Fig 8: Visualising using Fusion
Fig. 9: Visualising using Fugro Viewer
Fig. 10 3D Visualisation using FugroViewer
Gyambibi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),
May- 2015, pp. 175-183
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Fig. 11: DTM created using Fusion/LVD
Fig. 12: Surface modeling with Fusion
Fig. 13 Contour shade using ArcGIS
Fig. 14: Hill shade using ArcGIS
Gyambibi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),
May- 2015, pp. 175-183
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Measurements
The following measurements were made using the software:
1. Length of segments
2. Perimeter
3. Area
Figures 15 and 16 show measurements made on the data
Fig. 15 Selected Point Measurement in Fusion (Lidar Distance Viewer)
Fig. 16: Measurement using Surfer
Gridding
The software used several algorithms to grid and generates contours. Grid lines were also displayed when a
command was issued to display grids.
Generating Cross sections
Cross sections were generated to ascertain the profile along some portions of the data. The results of software with
the capability of generating cross-sections is shown in Table 3. The figures below show cross sections generated with
some of the software under review.
Fig. 17: Cross Sections Generated by Fugro Viewer
Gyambibi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),
May- 2015, pp. 175-183
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Fig. 18: Cross sections generated by Surpac
V. ANALYSIS OF GEOSPATIAL CAPABILITIES
From the results in Table 3, the following observations were made about the geospatial software:
ALDAT had the least capabilities for the processes investigated.
Surfer, Surpac, Fugro, Fusion, ArcGis performed most of the process, as displayed in table 3
Surfer and ArcGis had the most interoperability
Surpac had the best 3D rendering and manipulation
It was also determined that Computers with specs less than what was used for this study could not or took longer hours to
process what could have been completed in few minutes.
Good file management promoted ease of handling data. Data duplication was controlled, thus the storage space
was effectively used.
For studies and minor works, the open source software (Fugro, ALDAT, and FUSION) would be the best to be
employed.
Where all these software are available, Surfer must be used to process, compress and export the data into
standard formats, preferably Drawing Exchange Format (DXF), usable by other CAD software and GIS
software
For mining and engineering works Surpac must be employed due to its strong 3D rendering and manipulation.
Table 3: Comparative capability of the selected geospatial software
SURFER SURPAC ArcGIS FUSION
FUGRO
ALDPAT
VISUALIZATION
Hillshade √ √
Watershed √
Slope √ √ √ √
DTM √ √ √ √
3D (DEM) √ √ √ √ √
Classification √ √ √
Contour √ √ √ √ √
GRIDDING √ √ √ √
MEASUREMENTS √ √ √ √ √ √
CROSS SECTIONS √ √ √ √
VI. CONCLUSION
The study revealed the challenges in managing point cloud data if not planned and managed properly. It also
revealed the capabilities of the 6 software. Data processing of point cloud data therefore requires skill and knowledge of
software applications. It is critical to plan for the processing and applications of point cloud data to derive its full
potentials and benefits.
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