Incremental updating geospatial data by granular computing

Updating geospatial data has recently become an important work for related fields. Constantly changing geospatial data are meaningless for all geospatial databases at all scales with problems in the representation condition and reasoning for new objects. We proposed an incremental updating strategy and method for geospatial data based on granular computing, to solve the problems in both static and dynamic conditions. We pointed out that proper representation of geospatial data at a given scale cannot be achieved unless the original data of geospatial objects satisfy the representation condition. With granular computing, we can implement the representation condition, with which new geospatial data can be inferred. In addition, we also introduced the method for a case.