Qualitative spatial logics for buffered geometries

Heshan Du, Natasha Alechina

Research output: Journal PublicationArticlepeer-review

5 Citations (Scopus)

Abstract

This paper describes a series of new qualitative spatial logics for checking consistency of same As and part Of matches between spatial objects from different geospatial datasets, especially from crowd-sourced datasets. Since geometries in crowd-sourced data are usually not very accurate or precise, we boffer geometries by a margin of error or a level of tolerance σ ∈ℝ ≤0, and define spatial relations for boffered geometries. The spatial logics formalize the notions of 'boffered equal' (intuitively corresponding to 'possibly same As'), 'boffered part of' ('possibly part Of'), 'near' ('possibly connected') and 'far' ('definitely disconnected'). A sound and complete axiomatisation of each logic is provided with respect to models based on metric spaces. For each of the logics, the satisfiability problem is shown to be NP-complete. Finally, we briey describe how the logics are used in a system for generating and debugging matches between spatial objects, and report positive experimental evaluation results for the system.

Original languageEnglish
Pages (from-to)693-745
Number of pages53
JournalJournal of Artificial Intelligence Research
Volume56
DOIs
Publication statusPublished - Aug 2016
Externally publishedYes

ASJC Scopus subject areas

  • Artificial Intelligence

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