Volume 5, Issue 5, May 2015 ISSN: 2277 128X...

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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

http://www.ijarcsse.com/

Gyambibi et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(5),

May- 2015, pp. 175-183


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.


May- 2015, pp. 175-183


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


May- 2015, pp. 175-183


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


May- 2015, pp. 175-183


Fig 7: Contour created with surpac

Fig 8: Visualising using Fusion

Fig. 9: Visualising using Fugro Viewer

Fig. 10 3D Visualisation using FugroViewer


May- 2015, pp. 175-183


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


May- 2015, pp. 175-183


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


May- 2015, pp. 175-183


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.

REFERENCES

[1] Anon, “Systems overview and Application”, Advanced Surveying and Mapping Technologies, U.S.

Department of Transportation, Federal Highway Administration. Publication No. FHWAICFL/TD-08-002, pp.

1-8,2008.


May- 2015, pp. 175-183


[2] Anon, “Fugro Viewer Software”, http://www.fugro.com., 2009, Accessed October 12, 2013 pp. 1-2

[3] S. Bayari, “Supporting Lidar and other Data in Petrel,” 8th Ocean Development Framework Users Meeting,

June 24th

, Madrid, Spain, 2008.

[4] J. Carter, K. Schmid, K. Waters, L. Betzhold, B. Hadley, R. Mataosky and J. Halleran, “An Introduction to

Lidour Technology, Data and Applications. National Oceanic and Atmospheric Administration (NOAA)

Coastal Services Centre, pp. 3-9., 2012.

[5] C. Crosby, C. “Introduction to Lidar Point Cloud Data using Fusion Software Package”.

http://wwwcloud.sdsc.edu, 2009, Accessed December, 2013.

[6] J. Elseberg, D. Borrman and A. Niichter, “One billion points in the Cloud – an Octree for efficient processing of

3-D Laser Scans”, ISPRS Journal of Photogrammetry and Remote Sensing. Pp. 76-78, 2013.

[7] Fermandez, J.C., Singhania, A. Caceres, J. Slatton, K.C. Starek, M. and Kumar R. (2007). “An Overview of

Lidar Point Cloud Processing Software”. Geosensing Engineering and Mapping (GEM). Civil and Coastal

Engineering Department, University of Florida, pp. 1-27.

[8] M. Lemens, “Massive Point Clouds”. GIM International Vol. 27 No.2. wwwgim-international.com/idl , 2013.

[9] J. R. McGaughey, “Software for Lidar Analysis and Visualization”. Otepka J., Ghuttar, S., Waldhauser, C.,

Hochseiter, R. and Pfesfer, N. (2013), “Geo reference Point Clouds: A survey of features and Point Cloud

Management. ISPRS International Journal of Geo-information. Pp. 1038 – 1040, 2013.

[10] K. Richardson, “Scaling Lidar.” Lidar Magazine, Vol.3 No.5., pp. 50-54, 2013.

[11] K. Williams, M.J. Osleen, Roe V. Gene and C. Glennie, “Synthesis of Transportation Applications of Mobile

Lidar”. 2 pp., 2013.

[12] K. Zhang , “Airborne Lidar Processing and Analysis Tools”, American Geo-physical Union, Fall meeting 2007,

Abstract # H52E-01 http://lidarihrc.flu.edu/

[13] D. Zhiqiang, and L. QiaoXiong, “New Method of storage and Visualization for Massive Point Cloud Dataset’,

22nd

CIPA Symposium, October 11-15, Kyoto, Japan 1pp., 2009.

http://www.fugro.com/

http://wwwcloud.sdsc.edu/

http://lidarihrc.flu.edu/

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