In near coastal environments, nautical charts provide crucial information for navigation and routing both in real-time operations and during planning stages. The cost of data collection as well as capacity constraints in the processing pipeline make reliable bathymetric information in such areas sparse. Prioritization rules can help guide the efforts to where information is the most valuable. AIS data provide accounts of real ship movements, indicating both desirable paths and minimum depths. We propose a statistical model for combining sparse bathymetric soundings with AIS observations for improved prediction of depths for generation of feasible transportation corridors. The method relies on viewing AIS draughts as censored observations of the true depth. A case-study is performed for the southern archipelago of Gothenburg using the program R-INLA. The non-stationarity caused by having boundaries with known (zero) depth and holes (land) in the domain is handled through discretization. Varying amounts of AIS data, ranging from none to 1824 observations, are used in the experiments. Results show predicted depths within the range of data values, and that inclusion of AIS data serve to push the field down to ensure that traverseable areas are predicted as such revealing corridors in narrow passages where bathymetric soundings are lacking.