Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Qualitative representation of spatial knowledge in two-dimensional space
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Efficient greedy learning of Gaussian mixture models
Neural Computation
A Formal Definition of Binary Topological Relationships
FOFO '89 Proceedings of the 3rd International Conference on Foundations of Data Organization and Algorithms
Topological Relations Between Regions in R² and Z²
SSD '93 Proceedings of the Third International Symposium on Advances in Spatial Databases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Modeling and querying uncertain spatial information for situational awareness applications
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Geospatial route extraction from texts
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
Spatial role labeling: Towards extraction of spatial relations from natural language
ACM Transactions on Speech and Language Processing (TSLP)
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Living in the era of data deluge, we have witnessed a web content explosion, largely due to the massive availability of User-Generated Content (UGC). In this work, we specifically consider the problem of geospatial information extraction and representation, where one can exploit diverse sources of information (such as image and audio data, text data, etc), going beyond traditional volunteered geographic information. Our ambition is to include available narrative information in an effort to better explain geospatial relationships: with spatial reasoning being a basic form of human cognition, narratives expressing such experiences typically contain qualitative spatial data, i.e., spatial objects and spatial relationships. To this end, we formulate a quantitative approach for the representation of qualitative spatial relations extracted from UGC in the form of texts. The proposed method quantifies such relations based on multiple text observations. Such observations provide distance and orientation features which are utilized by a greedy Expectation Maximization-based (EM) algorithm to infer a probability distribution over predefined spatial relationships; the latter represent the quantified relationships under user-defined probabilistic assumptions. We evaluate the applicability and quality of the proposed approach using real UGC data originating from an actual travel blog text corpus. To verify the quality of the result, we generate grid-based "maps" visualizing the spatial extent of the various relations.