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5. FUTURE OUTLOOK We envision the possible future scenario that the archiving and distribution capabilities of the program will be on a decentralized and distributed computing infrastructure to reduce the risk of a single point of failure. This will also enable the automation of some of the process flows, like the update of the gridded map data to reflect the objects stored in Ceph. The use of Ceph allows the system to easily adapt to a distributed environment because of its compatibility with Amazon’s AWS and OpenStack. Meanwhile, computing processes that can be parallelized, such as indexing and querying of data, can make use of technologies such as Hadoop (The Apache Software Foundation, 2014), a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. 6. SUMMARY Advances in geospatial technologies have enabled the acquisition of high-density, highaccuracy data sets with significantly detailed surface information of terrain and surface objects within a short period of time (Al-Naami et al., 2014). This results into large data sets that need to be stored for archiving, retrieval, and distribution. However, conventional storage systems, namely native file systems and spatial databases, have problems in scaling to the sizes of the datasets produced by the current GIS acquisition technologies (Fox et al., 2013). In this paper, we presented the use of Ceph Object Storage coupled with customized GeoNode to store and manage LiDAR and LiDAR-derived data sets. The customized GeoNode serves as both the front-end interface and the content management platform, while Ceph stores the large data sets subdivided into 1 KM by 1 KM tiles. This setup paves the road ahead for migrating from a file-system based infrastructure into a distributed computing platform. 7. REFERENCES Ackermann, F., 1999. Airborne laser scanning—present status and future expectations. ISPRS Journal of Photogrammetry and Remote Sensing 54 (2-3), 64-67. Al-Naami, K.M., and S. Seker, L. Khan, 2014. GISQF: An Efficient Spatial Query Processing System. 2014 IEEE 7th International Conference on Cloud Computing (CLOUD), pp. 681 - 688. Amazon Web Services, 2015. AWS | Amazon Simple Storage Service (S3) - Online Cloud Storage for Data & Files. Retrieved July 2015, from https:// aws.amazon.com/s3/ Development Of Data Archiving And Distribution System For The Philippines' Lidar Program Using Object Storage Systems 88
Boundless, 2015. GeoExplorer — GeoExplorer. Retrieved July 2015, from http ://suite.opengeo.org/opengeo-docs/geoexplorer/ Chen, Q., 2007. Airborne lidar data processing and information extraction. Photogrammetric Engineering & Remote Sensing, 73(2), 91-95. Crosby, C.J., Arrowsmith, J R., Nandigam, and V., Baru, C., 2011. A Geoinformatics Approach To Online Access And Processing Of LIDAR Topography Data. In R. Keller and C. Baru, Eds., Geoinformatics: Cyberinfrastructure for the Solid Earth Sciences, pp. 251-265. London: Cambridge University Press. David, N., Mallet, C., and Bretar, F., 2008. Library concept and design for lidar data processing. GEOgraphic Object Based Image Analysis (GEOBIA) Conference, Calgary, Canada. Fox, A., Eichelberger, C., Hughes, J., and Lyon, S., 2013. Spatio-temporal indexing in nonrelational distributed databases. 2013 IEEE International Conference on Big Data, pp. 291– 299. GeoNode Development Team, 2013. About GeoNode — GeoNode 2.0 documentation. Retrieved July 2015, from http://docs.geonode.org/en/master/organizational/about.html#about Inktank Storage, Inc., 2015. Welcome to Ceph — Ceph Documentation. Retrieved July 2015, from http://ceph.com/docs/master/ Isenburg, M., 2012. LASzip:Lossless compression of LiDAR Data. European LiDAR Mapping Forum. Jones, T., 2010. Ceph: A Linux petabyte-scale distributed file system. Retrieved July 2015, from http:// www.ibm.com/developerworks /library /l-ceph/ Levine, R., 1998. NAS Advantages: A VARs View. Retrieved July 2015, from http ://www.infostor.com/index/articles/display /55961/ articles/infostor/volume-2/issue-4/news-analysistrends/nas-advantages-a-vars-view.html Lewis P., McElhinney C., and McCarthy T., 2012. LiDAR data management pipeline; from spatial database population to web-application visualization. Conference Proceedings at Com.Geo 2012, Washington DC, USA. FOSS4G Seoul, South Korea | September 14 th – 19 th , 2015 89
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PROCEEDINGS Honil Gangni Yeokdae Gu
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C O N T E N T S Welcome Messages Lo
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A Week of Sharing, Learning, and Fu
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ACADEMIC TRACKS 7
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September 17, Thursday 11:00-12:15
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September 16 (Wed) & 18 (Fri) 15:05
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AUTOMATIC IMPROVEMENT OF POINT-OF-I
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In the following, we will list
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and specify their relations manuall
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Conducting a cross-validation on th
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81%. For cuisine=german, the precis
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4.3 Tourism and Leisure Tags We ide
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GENERATING GEOSPATIAL FOOTPRINTS FO
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quality assessment of geocrowdsourc
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Figure 2: The first simple case of
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Figure 5: The footprint of walkways
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say instead “Between 1st Street a
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4. DISCUSSION AND CONCLUSIONS The G
Page 37 and 38: Performance Analysis of MongoDB Vs.
Page 39 and 40: 2. REVIEW OF SPATIAL DATABASES Simi
Page 41 and 42: 4.2 Point Containment Problem Like
Page 43 and 44: If we observe the results above we
Page 45 and 46: esolution data (RapidEye and Landsa
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Page 49 and 50: scatter plot of RapidEye (Global (C
Page 51 and 52: is efficient to extrapolate depth f
Page 53 and 54: DYNAMIC STYLING FOR THEMATIC MAPPIN
Page 55 and 56: all) datasets on offer or applying
Page 57 and 58: 3.2.1 Data inputs The styler accept
Page 59 and 60: determine the radius (both in the x
Page 61 and 62: Figure 5: Map classification of blo
Page 63 and 64: 4.3 Point data Figure 8: Diverging
Page 65 and 66: 4.4 Non-spatial visualisations Figu
Page 67 and 68: 6 ACKNOWLEDGEMENTS The Cooperative
Page 69 and 70: educational resources (OER) and mod
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Page 75 and 76: H 27 Male Student Maths / Geography
Page 77 and 78: 5. CONCLUSIONS AND FUTURE WORK We h
Page 79 and 80: DEVELOPMENT OF DATA ARCHIVING AND D
Page 81 and 82: 2. RELATED WORK Recent developments
Page 83 and 84: 3. Significance and Objectives With
Page 85 and 86: Figure 3. Map Tiles Grid Selection
Page 87: The Ceph architecture comprises of
Page 91 and 92: Object-Based Building Boundary Extr
Page 93 and 94: 2.2. A Grid-Based Algorithm A grid-
Page 95 and 96: 3.1 Boundary Extraction The extract
Page 97 and 98: The Development of Web3D-based Open
Page 99 and 100: Singh et al.(2012) have utilized op
Page 101 and 102: Monitoring concept on operating asp
Page 103 and 104: (a) Mining area (Left:2007, Right:2
Page 105 and 106: geospatial information open platfor
Page 107 and 108: open platform utilized in open-pit
Page 109 and 110: Data Acquisition Methods Acquisitio
Page 111 and 112: (a) Ecological Restoring Area 1 (Le
Page 113 and 114: possibility of development of open-
Page 115 and 116: 2. DATA ACQUISITION 2.1 Image gathe
Page 117 and 118: Hugin Pix4Dmapper Pablo d'Angelo et
Page 119 and 120: utit may be improved using many ste
Page 121 and 122: Wójcicka, A., and Wróbel, Z., 201
Page 123 and 124: platforms, which are easy to use an
Page 125 and 126: SWE framework proposes the followin
Page 127 and 128: Steenkamp et al. (2009) state that
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Page 131 and 132: Measurement value yes Measurement s
Page 133 and 134: 4.3.1 Explore The explore view allo
Page 135 and 136: Figure 8. Registration of a generic
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467 - 471. Sohraby, K., Minoli, D.,
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In augmented reality a live view of
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Figure 4. Dwellings in an informal
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1. Gen eral crite ria 1.1. Platform
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matching, 3D engine, image tracking
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1.4. Implemented standards OGC ARML
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2.3. Object events 2.4. Display rad
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instructional videos, and basic exa
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application for addressing, the pre
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Environments, 6(4), 355-385. Carmig
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1. INTRODUCTION Use of landmarks in
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the junctions which made with major
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4. IMPLEMENTATION 4.1 Technology Se
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Figure 3: Linear map output with si
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AN OPEN SOURCE WEB SERVICE FOR REGI
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2. IGSN OVERVIEW Figure 1 shows the
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types (e.g., dateType and relationT
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REST API(Fielding, 2000). The servi
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An Open Source Web Service for Regi
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Research Centre (ARRC) and the Nati
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Developing a land use database of t
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Figure 3. Land use input applicatio
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Paddy 19.4 23.7 Upland field, orcha
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Figure 6. Overlay point based land
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Integrating Open Source GIS Softwar
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Advanced GIS course, we recognized
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for undergraduates. Research take t
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Figure 8 The USDA Foodshed project
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4. Data is sent to the web pages ei
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Advances in Civic Co-management wit
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2. JAKARTA'S DISASTER RISK MANAGEME
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The aim of the system is to improve
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8. REFERENCES Baker, J. L., 2012. (
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EVALUATION OF AN OPEN-SOURCE COLLAB
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therelevant stakeholderswith differ
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at each stage of the exercise: · l
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differentmitigationmeasuresaccordin
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students before the exercise. 5 4 3
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the third stage of the exercise in
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doi:10.1016/j.envsoft.2003.12.019.
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e integrated directly in the Web-GI
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3. Implementation The main features
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Phase 1: Earthquake data Input Data
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Figure 4 . Magnitude 7.8 Earthquake
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After adding all the layer maps, th
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EVALUATING FLOOD HAZARD POTENTIAL I
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σ 0 = 10*log 10 (DN 2 ) + CF (1) W
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Figure 4. The geomorphological feat
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from water class in land cover map
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ANALYSIS OF SPATIAL DENSITY UTILIZI
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demographic data. He also states th
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Table 1. The combination of COUNTRY
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NUMBER OF SUBSCRIBERS 300,000 250,0
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Figure 9. The number of foreign tou
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Figure 16. Spatial call activity pa
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Recently, additional tourist inform
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country. It uses R-Studio, a statis
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Geosocial Big Data Analysis Using P
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AN ON-BOARD VISUAL-BASED ATTITUDE E
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3.1 Keypoints detection and matchin
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complete the process. This has been
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Fleet, D., & Weiss, Y. (2006). Opti
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technologies in Geoinformation Scie
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technologies becomes irrelevant in
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Figure 2. Server (upper block) and
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Name Presence Description Table 1.
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SampleExtraApp sampleExtraApp annot
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4. RESULTS Summing up, we should no
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Paradigms. John Wiley & Sons Inc.,
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FREEWAT: FREE and open source tools
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An Evaluation of Open Source Geogra
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2. DATA AND METHOD The approach ado
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the beginning and end of each trip
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Table 4 - GIS Routing Tools S/N GIS
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State cold store- Hungu Primary Hea
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Figure 3 - Comparing Discrepancy in
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nations based on human per capita i
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investigating metropolitan traffic.
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A Cross National Comparison on the
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in 1980(Saaty, 1980). It assesses t
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ex or current government officials
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Herold, S., & Sawada, M. C. (2012).
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frameworkwhich covers an infrastruc
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The architecture of the application
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5. CONCLUDING REMARKS Open source s
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cloud environment, so that end-user
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Figure 2 represents user interface
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geo-science application. Also the o
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PO-04 GeoDjango-Framwork-based Popu
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pH of 5.5 to 7.0; forest loam, rock
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Figure 4. Study Area (Davao Region)
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Figure 6. Topographic Map of Mindan
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Figure 8. Land Areas suitable for C
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It is respectfully recommended that
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ECDIS through introduction of Open
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to adopt development using open sou
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3. Conclusion ECDIS is navigation e
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PO-08 Development of an Agent Based
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1. INTRODUCTION 1.1 Background of t
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Figure 2. Workflow of the semi-auto
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threshold set. GRASS 7.0 is used in
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Figure 7. Result of the application
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Ming, D. M. D., Luo, J. L. J., Shen
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PO-10 3D Visualization of City GML
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As A Service (PAAS) and Infrastruct
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Figure 2: System Architecture and D
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The resulted crowd source data cont
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Table 1: Confusion Matrix Crowd sou
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PO-12 House Number Interpolation Fo
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(DOE) has awarded a total of 82 Gri
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Most solar radiation models compute
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2. OBJECTIVES, SCOPE, AND LIMITATIO
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Figure 4. BSWM Solar Sensors for Va
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4.2 Site Suitability Analysis Inter
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5. RESULTS AND DISCUSSION 5.1 Month
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Figure 8. Monthly Average Real-sky
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Figure 19. GHI for November Figure
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Figure 26. Non-Resource Criteria Su
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Kryza, M. et al. 2010. “Spatial i
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example coordinates agents, based o
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A benefit of using multiple agents
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A local GeoServer instance was set
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Figure 3. Screen captures of search
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In combination with ranking of resu
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INTRODUCTION TO A NEW GEO-REFERENCE
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Figure 2. ‘Click-to-go’ functio
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Figure 5. The conceptual structure
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Figure 6. Software architecture of
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GIS ORIENTED SERVICE OPTIMIZATION T
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Vehicle Routing Problem (VRP) refer
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So, buffer of 2 meters was created
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log data which was the actual dista
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was being served by three vehicles.
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Analyzing the number of customers,
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PO-17 A Study of the Development an
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MODELING OF TERMINOLOGY DATABASE IN
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3.2 Interpretation of the terms 4 I
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Figure 4 shows the result of SWOT a
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Add terms in the database: Super us
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6. REFERENCES Damdinsuren A., 2013.
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PO-20 Vulnerability Assessment Usin
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LEIGHTWEIGHT URBAN COMPUTATION INTE
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where events are instantly propagat
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not be conceived as responsive by a
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3.5. Remote Services Among a few
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4. USE CASES 4.1. Transition Worksh
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to display the rank of the player i
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PO-23 Development of Opensource-bas
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Author Index Aburizaiza, Ahmad O. 2
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