Representing geographical uncertainties of utility location data in 3D

Three-dimensional (3D) uncertainty representations help to avoid ambiguity in the interpretation of utility data. Existing data models and 3D-solutions do not, however, facilitate this adequately yet. They store uncertainties only by means of textual attributes or require stochastic data and expert input to visualize uncertainties. Such data is difficult to obtain in practice. We address this issue by proposing an approach that integrates multiple available utility location datasets to represent geographical uncertainties. To this end, we identified four parameters that practitioners use to store location data – i.e. surveyed, standard, estimated and unknown; and used these to extend the existing CityGML Utility Network ADE model. Next, we calculated and visualized 3D uncertainty buffer shapes for three scenarios that were based on real data of a city district in the Netherlands. The approach may eventually enable engineers to avoid design errors and support utility localization in the field.