OpenStreetMap in GIScience: Experiences, Research, and Applications
An Introduction to OpenStreetMap in Geographic Information Science: Experiences, Research, and Applications -- Assessment of logical consistency in OpenStreetMap based on the spatial similarity concept -- Quality assessment of the contributed land use information from OpenStreetMap versus authoritat...
Gespeichert in:
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| Weitere Verfasser: | , , |
| Dokumenttyp: | Book/Monograph |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham Heidelberg
Springer
2015
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| Schriftenreihe: | Lecture Notes in Geoinformation and Cartography
SpringerLink Bücher |
| DOI: | 10.1007/978-3-319-14280-7 |
| Schlagworte: | |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/978-3-319-14280-7 Verlag, Volltext: http://dx.doi.org/10.1007/978-3-319-14280-7 Cover: https://swbplus.bsz-bw.de/bsz429163479cov.jpg 
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| Verfasserangaben: | edited by Jamal Jokar Arsanjani, Alexander Zipf, Peter Mooney, Marco Helbich |
Inhaltsangabe:
- Foreword; Contents; 1 An Introduction to OpenStreetMap in Geographic Information Science: Experiences, Research, and Applications; Abstract; 1 Introduction; 2 A Short Overview of the OpenStreetMap Research Landscape; 3 Geography of OpenStreetMap; 4 Objectives and Scope; 5 Structure of the Book; References; Part IData Management and Quality; 2 Assessment of Logical Consistency in OpenStreetMap Based on the Spatial Similarity Concept; Abstract; 1 Introduction; 2 Logical Consistency for OSM; 3 Proposed Framework; 3.1 Directional Relationships; 3.2 Topological Relationships
- 3.3 Metric Distance Relationships3.4 Proposed Methodology; 4 Implementation; 5 Conclusion; References; 3 Quality Assessment of the Contributed Land Use Information from OpenStreetMap Versus Authoritative Datasets; Abstract; 1 Introduction; 2 Materials and Data Processing; 2.1 OSM Dataset; 2.2 GMESUA Dataset as a Reference Dataset; 2.3 Study Areas; 3 Methods; 3.1 Logical Consistency and Topology; 3.2 Harmonization of the Datasets Nomenclatures; 3.3 Completeness; 3.4 Thematic Accuracy; 4 Results; 4.1 Sensitivity to Pixel Size; 4.2 Degree of Data Completeness
- 4.3 Overall and Per-class Analysis of Thematic Accuracy4.3.1 Frankfurt; 4.3.2 Munich; 4.3.3 Berlin; 4.3.4 Hamburg; 4.4 Spatial Distribution of Agreements and Disagreements; 5 Discussions and Conclusions; 6 Recommendations; Acknowledgments; References; 4 Improving Volunteered Geographic Information Quality Using a Tag Recommender System: The Case of OpenStreetMap; Abstract; 1 Introduction; 2 Volunteered Geographic Information Quality; 2.1 Data-Centric Approach; 2.2 User-Centric Approach; 2.3 Context-Centric Approach; 3 Semantic Heterogeneity of OpenStreetMap Dataset
- 3.1 Sources of Semantic Heterogeneities3.2 OpenStreetMap Tag Distribution; 4 Improving VGI Dataset Quality Using Semantic Measurements and Folksonomy of Tags; 4.1 Data Sources; 4.2 User Interface and Prototype Functionalities; 4.2.1 Automatic Suggestions of Tags; 4.2.2 Notification of Unrelated Tags; 5 Evaluations and Results; 5.1 Experimental Setup; 5.2 User Evaluation Results; 6 Discussion and Conclusions; Acknowledgments; References; 5 Inferring the Scale of OpenStreetMap Features; Abstract; 1 Introduction; 2 Scale and Level of Detail; 3 Two Methods for the Automatic Inference of Scale
- 3.1 Scale Inference with a Multiple Criteria Decision Technique3.1.1 Measuring Level of Detail; 3.1.2 Combining Criteria to Infer a LoD Category; 3.2 Empiric Scale Inference; 4 Combining Both Methods to Improve Scale Inference; 4.1 Compared Evaluation of Both Inference Methods; 4.2 Mixing Both Inference Methods; 5 LoD Harmonization for Large-Scale Automatic Mapping; 6 Open Problems; 6.1 Scale Inference for Point Objects; 6.2 Does Feature Density Alter Scale Level?; 6.3 The Scale of Objects with Simple Shapes; 7 Conclusions; References
- 6 Data Retrieval for Small Spatial Regions in OpenStreetMap