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Figure 3. Binary image generation by thresholding. Figure 4. User interface for Bayesian image fusion and the processed result example. 4. CONCLUDING REMARK Cloud computing and its services are still on the early maturing stage in the geo-based data manipulation and processing application. There are many ways and platforms to build a cloud-based application. This study employed OpenStack cloud computing platform. This study based on full open source presents a test case with geo-based image processing functions. Bayesian image fusion and image thresholding as geo-based image processing algorithm are based on the open source algorithms of OTB. For the further study, performance test with multiple source data sets needs for practical uses of mobile cloud in FOSS4G Seoul, South Korea | September 14 th – 19 th , 2015 312
geo-science application. Also the on-the-fly image matching or image registration on mobile devices for heterogeneous image sets from different sensor are also necessary. 5. ACKNOWLEDGMENTS This research was partly supported by National Land Space Information Research Program by Ministry of Land, Infrastructure and Transport of Korean government (No. 14NSIP-B080144-01). 6. Reference Chou, D. C., 2015. Cloud computing: A value creation model, Computer Standards & Interfaces 38, 72-77. Evangelidis, K., K. Ntouros, S. Makridis, and C. Papatheodorou, 2014. Geospatial services in theCloud, Computers & Geosciences63, 116-122. Kang, S. and K. Lee, 2014. A Performance Test of Mobile Cloud Service for Bayesian Image Fusion, Korean Journal of Remote Sensing 30, 445-454. Lee, K. and S. Kang, 2013. Mobile cloud service of geo-based image processing functions: a test iPad implementation, Remote Sensing Letters 4, 910-919. Lee, K., S. Kang, T.Chae, and Y. Kim, 2015. Satellite Image Processing Application System onOpenStack Cloud Computing Environment, 2015 ICEO&SI and ICLEI Resilience Forum, Kaohsiung. OpenStack, 2015.OpenStack User Stories, http://www.openstack.org/user-stories (Accessed July 22, 2015). Geo-based image analysis service in open source cloud computing environment 313
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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
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Performance Analysis of MongoDB Vs.
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2. REVIEW OF SPATIAL DATABASES Simi
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4.2 Point Containment Problem Like
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If we observe the results above we
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esolution data (RapidEye and Landsa
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and used for calibration and coeffi
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scatter plot of RapidEye (Global (C
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is efficient to extrapolate depth f
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DYNAMIC STYLING FOR THEMATIC MAPPIN
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all) datasets on offer or applying
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3.2.1 Data inputs The styler accept
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determine the radius (both in the x
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Figure 5: Map classification of blo
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4.3 Point data Figure 8: Diverging
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4.4 Non-spatial visualisations Figu
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6 ACKNOWLEDGEMENTS The Cooperative
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educational resources (OER) and mod
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workshops have revealed that microc
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300 250 200 150 100 50 0 Figure 4.
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H 27 Male Student Maths / Geography
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5. CONCLUSIONS AND FUTURE WORK We h
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DEVELOPMENT OF DATA ARCHIVING AND D
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2. RELATED WORK Recent developments
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3. Significance and Objectives With
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Figure 3. Map Tiles Grid Selection
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The Ceph architecture comprises of
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Boundless, 2015. GeoExplorer — Ge
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Object-Based Building Boundary Extr
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2.2. A Grid-Based Algorithm A grid-
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3.1 Boundary Extraction The extract
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The Development of Web3D-based Open
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Singh et al.(2012) have utilized op
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Monitoring concept on operating asp
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(a) Mining area (Left:2007, Right:2
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geospatial information open platfor
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open platform utilized in open-pit
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Data Acquisition Methods Acquisitio
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(a) Ecological Restoring Area 1 (Le
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possibility of development of open-
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2. DATA ACQUISITION 2.1 Image gathe
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Hugin Pix4Dmapper Pablo d'Angelo et
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utit may be improved using many ste
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Wójcicka, A., and Wróbel, Z., 201
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platforms, which are easy to use an
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SWE framework proposes the followin
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Steenkamp et al. (2009) state that
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technology and are published under
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Measurement value yes Measurement s
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4.3.1 Explore The explore view allo
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Figure 8. Registration of a generic
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participants completely agree with
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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
Page 261 and 262: Fleet, D., & Weiss, Y. (2006). Opti
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Page 267 and 268: Figure 2. Server (upper block) and
Page 269 and 270: Name Presence Description Table 1.
Page 271 and 272: SampleExtraApp sampleExtraApp annot
Page 273 and 274: 4. RESULTS Summing up, we should no
Page 275 and 276: Paradigms. John Wiley & Sons Inc.,
Page 277 and 278: FREEWAT: FREE and open source tools
Page 279 and 280: An Evaluation of Open Source Geogra
Page 281 and 282: 2. DATA AND METHOD The approach ado
Page 283 and 284: the beginning and end of each trip
Page 285 and 286: Table 4 - GIS Routing Tools S/N GIS
Page 287 and 288: State cold store- Hungu Primary Hea
Page 289 and 290: Figure 3 - Comparing Discrepancy in
Page 291 and 292: nations based on human per capita i
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Page 297 and 298: in 1980(Saaty, 1980). It assesses t
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Page 301 and 302: Herold, S., & Sawada, M. C. (2012).
Page 303 and 304: frameworkwhich covers an infrastruc
Page 305 and 306: The architecture of the application
Page 307 and 308: 5. CONCLUDING REMARKS Open source s
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Page 311: Figure 2 represents user interface
Page 315 and 316: PO-04 GeoDjango-Framwork-based Popu
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Page 319 and 320: Figure 4. Study Area (Davao Region)
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Page 331 and 332: 3. Conclusion ECDIS is navigation e
Page 333 and 334: PO-08 Development of an Agent Based
Page 335 and 336: 1. INTRODUCTION 1.1 Background of t
Page 337 and 338: Figure 2. Workflow of the semi-auto
Page 339 and 340: threshold set. GRASS 7.0 is used in
Page 341 and 342: Figure 7. Result of the application
Page 343 and 344: Ming, D. M. D., Luo, J. L. J., Shen
Page 345 and 346: PO-10 3D Visualization of City GML
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Page 351 and 352: The resulted crowd source data cont
Page 353 and 354: Table 1: Confusion Matrix Crowd sou
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Page 359 and 360: Most solar radiation models compute
Page 361 and 362: 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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