Qualitative Spatial Logics for Buffered Geometries

Heshan Du, Natasha Alechina
2016 The Journal of Artificial Intelligence Research  
This paper describes a series of new qualitative spatial logics for checking consistency of sameAs and partOf 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 buffer geometries by a margin of error or a level of tolerance, and define spatial relations for buffered geometries. The spatial logics formalize the notions of 'buffered equal' (intuitively
more » ... (intuitively corresponding to 'possibly sameAs'), 'buffered part of' ('possibly partOf'), '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 briefly 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.
doi:10.1613/jair.5140 fatcat:e6k4smuv7rcs3gqqdrgb5fk3ja