Consensual Fusion of Uncertain Multisource Spatial data

Multisource spatial data fusion strategies have generally to cope with distinct kinds of uncertainty, deriving by both the trust of the source, the imperfection of the spatial data, and the vagueness of the fusion strategy itself. In this paper we propose a consensual fusion that allows to flexibly model several fusion strategies ranging from a risk-taking to a risk-adverse attitude, and capable to cope with both data imprecision and uncertainty and source reliability. Uncertainty and imprecision in spatial data is represented by associating a fuzzy value with each spatial unit. The fusion function has a quantifier-guided nature reflecting the concept of a fuzzy majority and works on imprecise values to compute an imprecise result. It is formalized by a generalized OWA operator defined in the paper for aggregating imprecise values with distinct importance. The consensual fusion works so that the greater the trust score of the source and its agreement with the other sources, the more influent (important) is the data from the source in determining the consensual values. Further, when the data are affected by uncertainty one can require to fuse them so as to compute a result affected by at most a given maximum uncertainty level.