Underground networks, particularly sewerage networks require accurate information for their management. To successfully and smoothly accomplish the required tasks for network expansion, reparation or simulation analysis, operators collect and analyse data coming from multiple sources. The various and heterogeneous sources available often provide different representations for a network. Thus, raising challenges for information exploitation and communication between the different actors managing these networks. In addition, the imperfections related to the sources are numerous and their consideration in the decision making process is mandatory. In this paper, we propose a generic data modelling for the fusion of sewerage networks data. Our meta-model supports imperfection modelling at data-source level as well as at network object position and attribute levels, allowing thus formal fusion operations to be conducted efficiently and reliably. To validate our meta-model, we implemented it using data analysis and reengineering platform called Moose, and we conducted a test on the town of Prades-le-Lez (France). We took into account three data-sources providing information on the node positions of the sewerage network : 1- the official network map as semi-structured source, 2- a high resolution aerial image database and 3- a Google Street View database as unstructured sources. As result, we were able to reliably perform data monitoring and visualization requests on real heterogeneous multi-source data related to a specific sewerage network.