Cartographic line simplification and polygon CSG formulæ in O(n log* n) time
WADS '97 Selected papers presented at the international workshop on Algorithms and data structure
Ontology-driven geographic information systems
Proceedings of the 7th ACM international symposium on Advances in geographic information systems
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Topologically-Consistent Map Generalisation Procedures and Multi-scale Spatial Databases
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Efficient and consistent line simplification for web mapping
International Journal of Web Engineering and Technology
Querying Multigranular Spatio-temporal Objects
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Cartographic generalisation of lines based on a B-spline snake model
International Journal of Geographical Information Science
ISWC '09 Proceedings of the 8th International Semantic Web Conference
A map ontology driven approach to natural language traffic information processing and services
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Exploiting qualitative spatial reasoning for topological adjustment of spatial data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Creating task-specific maps with map content transformations
Proceedings of the 1st ACM SIGSPATIAL International Workshop on MapInteraction
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Different users of geospatial information have different requirements of that information. Matching information to users' requirements demands an understanding of the ontological aspects of geospatial data. In this paper, we present an ontology-driven map generalization algorithm, called DMin, that can be tailored to particular users and users' tasks. The level of detail in a generated map is automatically adapted by DMin according to the semantics of the features represented. The DMin algorithm is based on a weighting function that has two components: (1) a geometric component that differs from previous approaches to map generalization in that no fixed threshold values are needed to parameterize the generalization process and (2) a semantic component that considers the relevance of map features to the user. The flexibility of DMin is demonstrated using the example of a transportation network.