When planning for ship navigation or compiling data for a bathymetry map, the navigator or mapper uses many different sources of bathymetry information and navigation hazards. The quality of these sources is inconsistent in general, however, making it challenging to provide a coherent picture for planning. Here, we describe an approach for consistent planning/mapping that uses a combination of soft computing and Bayesian estimation. The case study used to exercise this system involves NOAA Electronic Nautical Charts for an area in the Chesapeake Bay. We first interpolate each set of irregularly spaced soundings to gridded versions of each point-cloud set. Each of these intermediate grids is then aggregated into a fused bathymetric realization using order weighted averaging (OWA) to provide the weights for each source based on their subjective reliabilities. The OWA allows for fusion informed by the user’s subjective risk allowed in the reconstruction of the seafloor surface and provides quantitative methods to generate, use, and record subjective reliability weights. Each sounding point that went into the bathymetry estimate is then categorized as “no-go,” “caution,” or “go” status. Reliability estimates are reused for weighted Bayesian categorization of each output grid cell to compute the navigable surface.