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Offline availability is crucial when working in rural areas or in a disaster relief situation where connectivity is limited. Since ARToolkit and DroidAR are open source, users can implement the application in such a way that it reads the data from a local source, for example from a file in comma-separated values (CSV) format or from a JavaScript object notation (JSON) message. With the proprietary frameworks, the default method of accessing information is via a service that requires connectivity. However, if the format of the response is known, the developer can create a file in the same format and the application can then read the data from this file. In all frameworks, the primary data source is a web service. Other options are directly from a database or through native code (refer to Table 3). Layar and Metaio access the data through a proprietary web service that is available as a ‘black box’ to the user. With Metaio, the user accesses the web service through a channel identifier. The channel is the entry point to the Metaio Cloud backend from where the information is requested. The channel identifier can be distributed in two ways: by QR code or by publishing the channel identifier. When using Layar, the user publishes the data on the Layar service and the data is then seen as a layer (the format of the layer is not know). The user can then only access the layers via this Layar service. All frameworks, except Layer, allow the user to make use of native code to acquire data from custom sources. However, with the proprietary frameworks, this is a challenge as the data request has to be overwritten with custom code. For example, with Metaio the data can only be accessed via a channel identifier. The user has to reproduce the Metaio service data format and inject it into the implementation. 2. Fu nc tio na l cri ter ia Table 3. Overview the results of the functional criteria A R La b 2.1. Data Web service X X X X* X* X X* source QR code X Database X X X X X Native code X X X X X X Other X 2.2. Data display The visual representation can be altered? User can swap between which information is displayed? A R To olk it Dr oid A R La ya r Me tai o Pa nic A R X X X X X X X X X X X X X Wi kit ud e 150
2.3. Object events 2.4. Display radius 2.5. Visual search Does the framework implement event triggers? X X X X X X X The display radius can be altered? X X X X X X X Photo X + X X LiDAR X + X X Other, e.g. 3D objects X + X X X + Available through a separate application provided by ARLab. * These frameworks make use of a proprietary web service. Augmented reality superimposes information (data display) on a live feed, for example a camera feed. Various types of data or information can be superimposed, such as text, images or videos. For addressing, the display of text is important. In all frameworks, the fields to be displayed from a table or database, can be configured. However, adjusting the visual representation can be tricky, especially with proprietary frameworks that do not allow 151
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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
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
Page 129 and 130: technology and are published under
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
Page 137 and 138: participants completely agree with
Page 139 and 140: 467 - 471. Sohraby, K., Minoli, D.,
Page 141 and 142: In augmented reality a live view of
Page 143 and 144: Figure 4. Dwellings in an informal
Page 145 and 146: 1. Gen eral crite ria 1.1. Platform
Page 147 and 148: matching, 3D engine, image tracking
Page 149: 1.4. Implemented standards OGC ARML
Page 153 and 154: instructional videos, and basic exa
Page 155 and 156: application for addressing, the pre
Page 157 and 158: Environments, 6(4), 355-385. Carmig
Page 159 and 160: 1. INTRODUCTION Use of landmarks in
Page 161 and 162: the junctions which made with major
Page 163 and 164: 4. IMPLEMENTATION 4.1 Technology Se
Page 165 and 166: Figure 3: Linear map output with si
Page 167 and 168: AN OPEN SOURCE WEB SERVICE FOR REGI
Page 169 and 170: 2. IGSN OVERVIEW Figure 1 shows the
Page 171 and 172: types (e.g., dateType and relationT
Page 173 and 174: REST API(Fielding, 2000). The servi
Page 175 and 176: An Open Source Web Service for Regi
Page 177 and 178: Research Centre (ARRC) and the Nati
Page 179 and 180: Developing a land use database of t
Page 181 and 182: Figure 3. Land use input applicatio
Page 183 and 184: Paddy 19.4 23.7 Upland field, orcha
Page 185 and 186: Figure 6. Overlay point based land
Page 187 and 188: Integrating Open Source GIS Softwar
Page 189 and 190: Advanced GIS course, we recognized
Page 191 and 192: for undergraduates. Research take t
Page 193 and 194: Figure 8 The USDA Foodshed project
Page 195 and 196: 4. Data is sent to the web pages ei
Page 197 and 198: Advances in Civic Co-management wit
Page 199 and 200: 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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