PROCEEDINGS

Recommendations

Info

(a) Mining Area 1 (Left:2007, Right:2014) (b) Mining Area 2 (Left:2007, Right:2014) Figure 17. Change detection of mining area using 3D modeling Figure 18 and Figure 19 shows the 2D and 3D analysis on change detection of ecological restoring area. Two-dimensional and three-dimensional changes of the area were analyzed using the same image and terrain information as did in the mining area. Ecological restoring area is an area that is under ecological restoration according to restoring plan after the end of mining. Figure 18 (a) describes the change of ground coverage into vegetation under ecological restoration over time. In the areas of Figure 18 (b) and Figure 19, ecological and topographical restoration had taken place completely, showing no signs of three-dimensional topographical changes such as those of the mining area. (a) Change detection of ecological restoring area using ortho image(2D) (b) Change detection of ecological restoring area using residual DEM(3D) Figure 18. Change detection of ecological restoring area Instructions for authors 110
(a) Ecological Restoring Area 1 (Left:2007, Right:2014) (b) Ecological Restoring Area 2 (Left:2007, Right:2014) (c) Ecological Restoring Area 3 (Left:2007, Right:2014) Figure 19. Change detection of ecological restoring area using 3D modeling In 2013, terrestrial LiDAR surveying took place for disaster restoring area to understand the status quo and to establish a restoration plan of the disaster that had taken place in the area of interest. The topographical information of before and after the disaster were utilized for the change detection analysis. Figure 20 and Figure 21 are analyses of 2D and 3D changes of disaster restoring area. Disaster restoring area, mining operation having finished, does not have any topographical nor ecological transition until in the event of accidents. Figure 20 (a) pictures the collapse of the mining slope through the comparison between 2012 and 2013 image, and the restoration in progress of the collapsed area with 2013 and 2014 image. Figure 20 (b) is the analysis of three-dimensional transition of mining area between 2007 and 2014 using DEM; Figure 21 shows the definite transition of topography before and after the restoration of collapse. (a) Change detection of disaster restoring area using ortho image(2D) (b) Change detection of disaster restoring area using residual DEM(3D) Figure 20. Change detection of disaster restoring area FOSS4G Seoul, South Korea | September 14 th – 19 th , 2015 111
Page 1 and 2:
PROCEEDINGS Honil Gangni Yeokdae Gu
Page 3 and 4:
C O N T E N T S Welcome Messages Lo
Page 5 and 6:
A Week of Sharing, Learning, and Fu
Page 7 and 8:
ACADEMIC TRACKS 7
Page 9 and 10:
September 17, Thursday 11:00-12:15
Page 11 and 12:
September 16 (Wed) & 18 (Fri) 15:05
Page 13 and 14:
AUTOMATIC IMPROVEMENT OF POINT-OF-I
Page 15 and 16:
In the following, we will list
Page 17 and 18:
and specify their relations manuall
Page 19 and 20:
Conducting a cross-validation on th
Page 21 and 22:
81%. For cuisine=german, the precis
Page 23 and 24:
4.3 Tourism and Leisure Tags We ide
Page 25 and 26:
GENERATING GEOSPATIAL FOOTPRINTS FO
Page 27 and 28:
quality assessment of geocrowdsourc
Page 29 and 30:
Figure 2: The first simple case of
Page 31 and 32:
Figure 5: The footprint of walkways
Page 33 and 34:
say instead “Between 1st Street a
Page 35 and 36:
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
Page 47 and 48:
and used for calibration and coeffi
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
Page 71 and 72: workshops have revealed that microc
Page 73 and 74: 300 250 200 150 100 50 0 Figure 4.
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 and 88: The Ceph architecture comprises of
Page 89 and 90: Boundless, 2015. GeoExplorer — Ge
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: Data Acquisition Methods Acquisitio
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 and 150: 1.4. Implemented standards OGC ARML
Page 151 and 152: 2.3. Object events 2.4. Display rad
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
Page 201 and 202:
The aim of the system is to improve
Page 203 and 204:
8. REFERENCES Baker, J. L., 2012. (
Page 205 and 206:
EVALUATION OF AN OPEN-SOURCE COLLAB
Page 207 and 208:
therelevant stakeholderswith differ
Page 209 and 210:
at each stage of the exercise: · l
Page 211 and 212:
differentmitigationmeasuresaccordin
Page 213 and 214:
students before the exercise. 5 4 3
Page 215 and 216:
the third stage of the exercise in
Page 217 and 218:
doi:10.1016/j.envsoft.2003.12.019.
Page 219 and 220:
e integrated directly in the Web-GI
Page 221 and 222:
3. Implementation The main features
Page 223 and 224:
Phase 1: Earthquake data Input Data
Page 225 and 226:
Figure 4 . Magnitude 7.8 Earthquake
Page 227 and 228:
After adding all the layer maps, th
Page 229 and 230:
EVALUATING FLOOD HAZARD POTENTIAL I
Page 231 and 232:
σ 0 = 10*log 10 (DN 2 ) + CF (1) W
Page 233 and 234:
Figure 4. The geomorphological feat
Page 235 and 236:
from water class in land cover map
Page 237 and 238:
ANALYSIS OF SPATIAL DENSITY UTILIZI
Page 239 and 240:
demographic data. He also states th
Page 241 and 242:
Table 1. The combination of COUNTRY
Page 243 and 244:
NUMBER OF SUBSCRIBERS 300,000 250,0
Page 245 and 246:
Figure 9. The number of foreign tou
Page 247 and 248:
Figure 16. Spatial call activity pa
Page 249 and 250:
Recently, additional tourist inform
Page 251 and 252:
country. It uses R-Studio, a statis
Page 253 and 254:
Geosocial Big Data Analysis Using P
Page 255 and 256:
AN ON-BOARD VISUAL-BASED ATTITUDE E
Page 257 and 258:
3.1 Keypoints detection and matchin
Page 259 and 260:
complete the process. This has been
Page 261 and 262:
Fleet, D., & Weiss, Y. (2006). Opti
Page 263 and 264:
technologies in Geoinformation Scie
Page 265 and 266:
technologies becomes irrelevant in
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
Page 293 and 294:
investigating metropolitan traffic.
Page 295 and 296:
A Cross National Comparison on the
Page 297 and 298:
in 1980(Saaty, 1980). It assesses t
Page 299 and 300:
ex or current government officials
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
Page 309 and 310:
cloud environment, so that end-user
Page 311 and 312:
Figure 2 represents user interface
Page 313 and 314:
geo-science application. Also the o
Page 315 and 316:
PO-04 GeoDjango-Framwork-based Popu
Page 317 and 318:
pH of 5.5 to 7.0; forest loam, rock
Page 319 and 320:
Figure 4. Study Area (Davao Region)
Page 321 and 322:
Figure 6. Topographic Map of Mindan
Page 323 and 324:
Figure 8. Land Areas suitable for C
Page 325 and 326:
It is respectfully recommended that
Page 327 and 328:
ECDIS through introduction of Open
Page 329 and 330:
to adopt development using open sou
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
Page 347 and 348:
As A Service (PAAS) and Infrastruct
Page 349 and 350:
Figure 2: System Architecture and D
Page 351 and 352:
The resulted crowd source data cont
Page 353 and 354:
Table 1: Confusion Matrix Crowd sou
Page 355 and 356:
PO-12 House Number Interpolation Fo
Page 357 and 358:
(DOE) has awarded a total of 82 Gri
Page 359 and 360:
Most solar radiation models compute
Page 361 and 362:
2. OBJECTIVES, SCOPE, AND LIMITATIO
Page 363 and 364:
Figure 4. BSWM Solar Sensors for Va
Page 365 and 366:
4.2 Site Suitability Analysis Inter
Page 367 and 368:
5. RESULTS AND DISCUSSION 5.1 Month
Page 369 and 370:
Figure 8. Monthly Average Real-sky
Page 371 and 372:
Figure 19. GHI for November Figure
Page 373 and 374:
Figure 26. Non-Resource Criteria Su
Page 375 and 376:
Kryza, M. et al. 2010. “Spatial i
Page 377 and 378:
example coordinates agents, based o
Page 379 and 380:
A benefit of using multiple agents
Page 381 and 382:
A local GeoServer instance was set
Page 383 and 384:
Figure 3. Screen captures of search
Page 385 and 386:
In combination with ranking of resu
Page 387 and 388:
INTRODUCTION TO A NEW GEO-REFERENCE
Page 389 and 390:
Figure 2. ‘Click-to-go’ functio
Page 391 and 392:
Figure 5. The conceptual structure
Page 393 and 394:
Figure 6. Software architecture of
Page 395 and 396:
GIS ORIENTED SERVICE OPTIMIZATION T
Page 397 and 398:
Vehicle Routing Problem (VRP) refer
Page 399 and 400:
So, buffer of 2 meters was created
Page 401 and 402:
log data which was the actual dista
Page 403 and 404:
was being served by three vehicles.
Page 405 and 406:
Analyzing the number of customers,
Page 407 and 408:
PO-17 A Study of the Development an
Page 409 and 410:
MODELING OF TERMINOLOGY DATABASE IN
Page 411 and 412:
3.2 Interpretation of the terms 4 I
Page 413 and 414:
Figure 4 shows the result of SWOT a
Page 415 and 416:
Add terms in the database: Super us
Page 417 and 418:
6. REFERENCES Damdinsuren A., 2013.
Page 419 and 420:
PO-20 Vulnerability Assessment Usin
Page 421 and 422:
LEIGHTWEIGHT URBAN COMPUTATION INTE
Page 423 and 424:
where events are instantly propagat
Page 425 and 426:
not be conceived as responsive by a
Page 427 and 428:
3.5. Remote Services Among a few
Page 429 and 430:
4. USE CASES 4.1. Transition Worksh
Page 431 and 432:
to display the rank of the player i
Page 433 and 434:
PO-23 Development of Opensource-bas
Page 435 and 436:
Author Index Aburizaiza, Ahmad O. 2
show all

PROCEEDINGS

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?