Public and private stakeholders of the wastewater and stormwater sectors are increasingly faced with large quantities and multiple sources of information/data of different nature: databases of factual data, geographical data, various types of images, digital and analogue maps, intervention reports, incomplete and imprecise data (on locations and the geometric features of networks), evolving and conflicting data (from different eras and sources), etc. Obtaining accurate and updated information on the underground wastewater and stormwater networks is a challenge and a cumbersome task, especially in cities undergoing urban expansion. Within this context, the main objective of this multidisciplinary project, STARWARS (STormwAteR and WastewAteR networkS heterogeneous data AI-driven management), is to address this challenge by providing novel proposals for the management of heterogeneous data in stormwater and wastewater networks. The STARWARS project aims to bring together researchers from the AI and Water Sciences communities in order to enhance the emergence of new practical solutions for representing, managing, modelling, merging, completing, reasoning, explaining and query answering over data of different forms pertaining to stormwater and wastewater networks. The project is implemented through five work packages (WP). The first four WP concern research developments of new AI methodologies for managing heterogeneous stormwater and wastewater networks’ data. The fifth WP is dedicated to project management and dissemination activities. The second objective of the project is to produce new knowledge and to promote knowledge exchange, with a strong will and a plan to encourage knowledge sharing between the researchers involved in this STARWARS project. The scheduled secondment plan is designed with the aim of maximizing knowledge transfer and training between the two fields of Water Sciences and AI and thus facilitating the achievement of the project objectives.