Key words: Laser Scanning, LIDAR, Surveying and Mapping, Point Cloud, Geospatial software

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 1 D. Asenso-Gyambibi, 2 Y. Issaka and J. Oteng, 3 R. Arkoh 1 CSIR-Building and Road Research Institute, Ghana 2 University of Mines and Technology Dept. of Engineering, Ghana 3 Rabotech 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 2015, IJARCSSE All Rights Reserved Page 175

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 Golden Software Inc. $ 849 Version (8.06) Esri Arc.GIS ESRI $1,500 Gemcom Surpac Gemcom $600- $950 Fusion Silviculture and Forest Models Team at Open source the U.S. Forest Service s Pacific Northwest Research Station 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. 2015, IJARCSSE All Rights Reserved Page 176

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 2015, IJARCSSE All Rights Reserved Page 177

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 2015, IJARCSSE All Rights Reserved Page 178

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 2015, IJARCSSE All Rights Reserved Page 181

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. 2015, IJARCSSE All Rights Reserved Page 182

[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 24 th, 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, 22 nd CIPA Symposium, October 11-15, Kyoto, Japan 1pp., 2009. 2015, IJARCSSE All Rights Reserved Page 183