Ruas / Gold Headway in Spatial Data Handling

13th International Symposium on Spatial Data Handling
1. Auflage 2008
ISBN: 978-3-540-68566-1
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 650 Seiten

Geographic information is a key element for our modern society. Put s- ply, it is information whose spatial (and often temporal) location is fun- mental to its value, and this distinguishes it from many other types of data, and analysis. For sustainable development, climate change or more simply resource sharing and economic development, this information helps to - cilitate human activities and to foresee the impact of these activities in space as well as, inversely, the impact of space on our lives. The Inter- tional Symposium on Spatial Data Handing (SDH) is a primary research forum where questions related to spatial and temporal modelling and analysis, data integration, visual representation or semantics are raised. The first symposium commenced in 1984 in Zurich and has since been organised every two years under the umbrella of the International Geographical Union Commission on Geographical Information Science (http://www. igugis. org). Over the last 28 years, the Symposium has been held in: st 1 - Zürich, 1984 nd 2 - Seattle, 1986 rd 3 - Sydney, 1988 th 4 - Zurich, 1990 th 5 - Charleston, 1992 th 6 - Edinburgh, 1994 th 7 - Delft, 1996 th 8 - Vancouver, 1998 th 9 - Beijing, 2000 th 10 - Ottawa, 2002 th 11 - Leicester, 2004 th 12 - Vienna, 2006 th This book is the proceedings of the 13 International Symposium on Spatial Data Handling.

Autoren/Hrsg.

Ruas, Anne

Gold, Christopher

Inhaltsverzeichnis

1;Foreword;5
2;Acknowledgements;7
3;Table of Contents;8
4;Programme Committee;13
5;Local Organizing Committee;13
6;A Study on how Humans Describe Relative Positions of Image Objects;14
6.1;Abstract;14
6.2;1 Introduction;15
6.3;2 Experiment design;17
6.4;3 Collecting descriptions and extracting spatial information;20
6.5;4 Data analysis;24
6.6;5 Conclusions;29
6.7;Acknowledgments;30
6.8;References;30
7;Perceptual Sketch Interpretation;32
7.1;Abstract;32
7.2;1 Introduction;32
7.3;2 Related work;34
7.4;3 Underlying principles;35
7.5;4 The perceptual sketch interpretation algorithm;38
7.6;5 Prototype;43
7.7;6. Evaluation;44
7.8;7 Conclusions and future work;47
7.9;Acknowledgments;49
7.10;References;50
8;The Shape Cognition and Query Supported by Fourier Transform;52
8.1;Abstract;52
8.2;1 Introduction;52
8.3;2 Shape Representation;54
8.4;3 Fourier transform and shape measure;57
8.5;4 Shape based spatial query;61
8.6;5 Conclusion;65
8.7;Acknowledgements;66
8.8;References;66
9;Classification of Landslide Susceptibility in the Development of Early Warning Systems;68
9.1;Abstract;68
9.2;1. Introduction;69
9.3;2. Classification;70
9.4;3 Related work;71
9.5;4 Data;72
9.6;5 Classification Methods;75
9.7;6 Results;79
9.8;7 Conclusions/ Outlook;85
9.9;Acknowledgements;86
9.10;References;86
10;Clusters in Aggregated Health Data;89
10.1;1 Introduction;89
10.2;2 Model;91
10.3;3 Algorithms;95
10.3.1;3.1 Arrangement of placements;96
10.3.2;3.2 Computing the optimal placement;97
10.3.3;3.3 Extensions;98
10.4;4 Discussion;100
10.5;References;101
11;Spatial Simulation of Agricultural Practices using a Robust Extension of Randomized Classification Tree Algorithms;103
11.1;1. Introduction;104
11.2;2. Methods;105
11.3;3. CASE STUDY;109
11.4;4. Results;113
11.5;5. Conclusion;118
11.6;References;118
12;Impact of a Change of Support on the Assessment of Biodiversity with Shannon Entropy;121
12.1;Abstract;121
12.2;1 Introduction;122
12.3;2. The Modifiable Unit Problem;123
12.4;3. Data and biodiversity indexes applied on the Ventoux Mount, Vaucluse, Southern France;125
12.5;4. A way to evaluate and to ‘prevent’ the MAUP from biodiversity assessments;130
12.6;5. Results;133
12.7;6. Conclusion;141
12.8;References;142
13;Implicit Spatial Information Extraction from Remote Sensing Images;144
13.1;Abstract;144
13.2;1 Introduction;145
13.3;2 Spatial Information Processing;146
13.4;3 Generating Descriptors;148
13.5;4 Spatial Information Retrieval;150
13.6;5 Non explicit information;153
13.7;6 Conclusion;154
13.8;References;155
14;The Application of the Concept of Indicative Neighbourhood on Landsat ETM+ Images and Orthophotos Using Circular and Annulus Kernels;158
14.1;Abstract;158
14.2;1 Introduction;159
14.3;2 Materials;162
14.4;3 Methods;165
14.5;4 Results and discussion;168
14.6;5 Conclusions;171
14.7;References;172
15;Sensitivity of the C-band SRTM DEM Vertical Accuracy to Terrain Characteristics and Spatial Resolution;174
15.1;1 Introduction;174
15.2;2 Site and data sets;175
15.3;3 Methods;177
15.4;4 Results;179
15.5;5 Conclusion;186
15.6;References;186
16;Improving the Reusability of Spatiotemporal Simulation Models: Using MDE to Implement Cellular Automata;188
16.1;Abstract;188
16.2;1 Introduction;189
16.3;2 Implementation Technologies and Approaches to Spatiotemporal Modeling;190
16.4;3 A Three-level Model Driven Engineering Approach to Spatiotemporal Modeling;192
16.5;4 A Three-level MDE Approach to Model Cellular Automata;195
16.6;5 Concluding Remarks and Outlook;204
16.7;Acknowledgements;205
16.8;References;205
17;Support Vector Machines for Spatiotemporal Analysis in Geosensor Networks;207
17.1;1 Introduction;208
17.2;2 Geosensor Data and Existing Event Extraction Methods;209
17.3;3 Support Vector Machines;211
17.3.1;3.1 Margins and the Maximum Separating Hyperplane;212
17.3.2;3.2 Non-Linearity and Kernel Substitution;215
17.3.3;3.3 Mapping the Hyperplane Solution to Input Space;216
17.4;4. The Spatiotemporal Helix;217
17.5;5. Simulation;219
17.6;6. Conclusions and Future Work;223
17.7;Acknowledgements;224
18;Toward a Method to Generally Describe Physical Spatial Processes;227
18.1;Abstract:;227
18.2;1 Introduction;228
18.3;2 Spatial processes and geographic information systems;229
18.4;3. What are physical spatial processes;230
18.5;4. Two models of spatial physical processes;231
18.6;5. Example: diffusion of a contaminant in water;235
18.7;6 Qualitative insights about the example process;238
18.8;7 Conclusions and future work;239
18.9;Acknowledgements;240
18.10;References;240
19;A Data Model for Multi-scale Topographical Data;243
19.1;Abstract;243
19.2;1. Introduction;244
19.3;2. Previous approaches for multi-scale and single data models;245
19.4;3. Scope of IMTOP;246
19.5;4. A data model for multi-scale topographical data;249
19.6;5. Results of IMTOP with respect to the requirements;259
19.7;6. Conclusions;262
19.8;References;263
20;An Interoperable Web Service Architecture to Provide Base Maps Empowered by Automated Generalisation;265
20.1;Abstract;265
20.2;Introduction;265
20.3;Physical Planning Maps on the Web;267
20.4;Related Literature;270
20.5;Design of the Architecture;273
20.6;Implementation of the Architecture;278
20.7;Outlook & Conclusion;281
20.8;Acknowledgements;283
20.9;References;283
21;Combining Three Multi-agent Based Generalisation Models: AGENT, CARTACOM and GAEL;286
21.1;Abstract;286
21.2;1. Introduction;287
21.3;2. Comparative presentation of AGENT, CARTACOM and GAEL ;288
21.4;3 Proposed scenarios to combine AGENT, CARTACOM and GAEL;293
21.4.1;3.1. Scenario 1: separate use of AGENT, GAEL and CARTACOM on a spatially and/ or thematically partitioned dataset;294
21.4.2;3.2. Scenario 2: “interlaced” sequential use of AGENT, CARTACOM and GAEL on a set of objects;295
21.4.3;3.3. Scenario 3: simultaneous use of AGENT and CARTACOM data on one object;296
21.5;4. How to put the proposed scenarios into practice;298
21.5.1;4.1. Technical requirements underlying scenarios 1, 2 and3: summary;298
21.5.2;4.2. Status of the technical issues underlying scenarios 1 and 2;298
21.5.3;4.3. Re-engineering of constraint modelling in AGENT and CARTACOM to support scenario 3;299
21.6;5. Discussion;302
21.7;6. Conclusion and perspectives;303
21.8;Acknowledgement;304
21.9;References;304
22;Implementation of Building Reconstruction Algorithm Using Real World LIDAR Data;306
22.1;Abstract;306
22.2;1 Introduction;306
22.3;2 Airborne Laser Scanning (ALS);307
22.4;3 Building Blocks Identification;308
22.5;4 Roof Planes Recognition;314
22.6;5 Building Extrusion;317
22.7;6 Implementation of Real-world Lidar Data;317
22.8;7 Conclusion;319
22.9;Acknowledgment;321
22.10;References;321
23;A New Approach for Mountain Areas Cartography;323
23.1;Abstract;323
23.2;Keywords:;323
23.3;1. Context;324
23.4;2. Information extraction;326
23.5;3. Cartographic representation;333
23.6;4. Conclusion;338
23.7;References;341
24;Processing 3D Geo-Information for Augmenting Georeferenced and Oriented Photographs with Text Labels;357
24.1;Abstract;357
24.2;1 Introduction;358
24.3;2 Related Research;359
24.4;3 Data collection and preparation;361
24.5;4 Object identification;363
24.6;5 Label Placement;365
24.7;6. Results and conclusions;369
24.8;Acknowledgements;370
24.9;References;370
25;Interactive Geovisualization and Geometric Modelling of 3D Data - A Case Study from the Åknes Rockslide Site, Norway;372
25.1;Abstract;372
25.2;1 Introduction;373
25.3;2 OpenSceneGraph;375
25.4;3 Design and implementation of the 3D Åknes model.;377
25.5;4 Methods used in the preparation and visualization of the 3D model;380
25.6;5 Interactive modelling of the sliding surfaces;384
25.7;6 Discussion and summary;386
25.8;Acknowledgements;388
25.9;References;388
26;Marine GIS: Progress in 3D Visualization for Dynamic GIS;406
26.1;Abstract.;406
26.2;1 Introduction;406
26.3;2 System requirements;408
26.4;3 System design;412
26.4.1;3.1 GIS 3D Graphical Engine;412
26.4.2;3.2 ENC Reader;413
26.4.3;3.3 Geo converter;413
26.4.4;3.4 AIS module;414
26.4.5;3.5 Display of 3D models;414
26.4.6;3.6 Display of terrain;414
26.4.7;3.7 Data structures;415
26.4.8;3.8 External data sources;416
26.5;4 Other applications;417
26.6;5 Future work and possibilities;418
26.7;6 Conclusions;419
26.8;7 References;420
27;The IGN-E Case: Integrating Through a Hidden Ontology;422
27.1;Abstract;422
27.2;1. Introduction;423
27.3;2. Existing catalogues;424
27.4;3. Problems and the proposed approach;428
27.5;4. Heterogeneity;430
27.6;5. From proposal to reality;432
27.7;6. Automatic ontology creation ;433
27.7.1;6.1 Scales and coverage;433
27.7.2;6.2 Criteria for taxonomy creation;433
27.7.3;6.3 Attributes by values;435
27.7.4;6.4 PhenomenOntology;435
27.8;7. Automatic mapping discovery;436
27.8.1;7.1 Knowledge discovery;438
27.9;8. Conclusion and future work;438
27.10;Acknowledgements;439
27.11;References;439
28;All Roads Lead to Rome – Geospatial Modeling of Hungarian Street Names with Destination Reference;441
28.1;Abstract;441
28.2;1. Introduction;441
28.3;2. First case study: Street names of town Kaposvár ( Hungary);444
28.4;3. Extension to whole Hungary;447
28.5;4. Conclusions;451
28.6;References;452
29;Where is the Terraced House? On the Use of Ontologies for Recognition of Urban Concepts in Cartographic Databases;453
29.1;Abstract;453
29.2;1 Introduction;454
29.3;2 Ontology-driven Cartographic Pattern Recognition;455
29.4;3 An ontology of terraced houses;459
29.5;4 Experiment;461
29.6;5 Discussion;465
29.7;6 Conclusions;466
29.8;Acknowledgements;467
29.9;References;468
30;Information Processes Produce Imperfections in Data— The Information Infrastructure Compensates for Them;471
30.1;Abstract;471
30.2;1 Introduction;471
30.3;2 Ontology;473
30.4;3 Information Processes Transform between Tiers;475
30.5;4 Compensation Improves Decisions with Imperfect GIS Data;483
30.6;5 Conclusion;486
31;Moving from Pixels to Parcels: the Use of Possibility Theory to Explore the Uncertainty Associated object Oriented Remote Sensing;490
31.1;Abstract;490
31.2;1. Introduction;491
31.3;2. Background;492
31.4;3. Problem;494
31.5;4. Method;496
31.6;5. Results;497
31.7;6. Discussion;500
31.8;Acknowledgements;502
31.9;References;502
32;Data Matching – a Matter of Belief;504
32.1;Abstract;504
32.2;1. Introduction;504
32.3;2. Geographic data matching;505
32.4;3. Matching Approach based on the Belief Theory;507
32.5;4. Experimentation;515
32.6;5. DISCUSSION AND COMPARISON;518
32.7;6. Conclusion;520
32.8;Acknowledgements;520
32.9;References;520
33;Deriving Topological Relationships Between Simple Regions with Holes;523
33.1;Abstract;523
33.2;1 Introduction;523
33.3;2 Related Work;525
33.4;3 Constructing topological relationships between simple regions with holes;527
33.5;4 Conclusions;533
33.6;References;533
34;Spatial Rules Generate Urban Patterns: Emergence of the Small- World Network;534
34.1;Abstract;534
34.2;1 Introduction;535
34.3;2 The Small-World Network;537
34.4;3 Simulation Model;540
34.5;4 Rules Validation;540
34.6;5 Determinacy of the Simulation Model;547
34.7;6 Numerical Evaluation of the Small-World Network Pattern Emergence in the Model;551
34.8;7 Conclusions;554
34.9;Acknowledgments;555
34.10;References;555
35;Conceptual Neighborhoods of Topological Relations Between Lines;557
35.1;Abstract;557
35.2;1. Introduction;557
35.3;2. Conceptual Neighborhood Graphs;559
35.4;3. Conceptual Neighborhood Graph For Topological Relations Between Two Undirected Lines;560
35.5;4. Conceptual Neighborhood Graph For Topological Relations Between Two Broad- Boundary Lines;566
35.6;5. Comparisons of Conceptual Neighborhood Graphs;570
35.7;7. Conclusions and Future Work;572
35.8;8. Acknowledgments;573
35.9;References;573
36;Spatial Support and Spatial Confidence for Spatial Association Rules;575
36.1;Abstract;575
36.2;1 Introduction;575
36.3;2 Background;577
36.4;3 Quality measures for spatial association rules;580
36.5;4 Proximity measures;583
36.6;5 Spatio-temporal support and confidence;589
36.7;6 Discussion;590
36.8;7 Conclusions;591
36.9;Acknowledgments;591
36.10;References;591
37;A Primer of Picture-Aided Navigation in Mobile Systems;594
37.1;Abstract;594
37.2;1. Introduction;595
37.3;2. Different modes for describing itineraries;596
37.4;3. Basic considerations for picture-based itinerary description;600
37.5;4. Main characteristics of a picture-aided navigational system;608
37.6;5. Conclusions;610
37.7;References;610
38;Road Network Model for Vehicle Navigation using Traffic Direction Approach;611
38.1;Abstract;611
38.2;1. Introduction;612
38.3;2 Existing modeling schemas;613
38.4;3. Traffic direction based modeling schema;619
38.5;4. Model comparison;624
38.6;5. Conclusions;625
38.7;Acknowledgement;626
38.8;References;627
39;Clustering Algorithm for Network Constraint Trajectories;628
39.1;Abstract.;628
39.2;1. Introduction;628
39.3;2. Related work;630
39.4;3. The Clustering Procedure;632
39.5;4. Two-stepClustering Algorithme NETSCAN;634
39.6;5. Experimental Evaluation;637
39.7;6. Conclusion;642
39.8;Acknowledgements;643
39.9;References;643
40;Author Index;645