From Data to Action: Scalable AI Data Platform for Urban Decision Support Systems

Cities and Public Administrations increasingly face the challenge of managing fragmented and heterogeneous urban data ecosystems. Valuable information is often distributed across multiple sources, ranging from global open-data standards such as OpenStreetMap (OSM) to municipal databases containing high-resolution LiDAR surveys, demographic information, sidewalk characteristics, pavement conditions, public transport data, and other operational datasets. 

Building upon the extensive urban data mapping activities carried out in Milan and the research developed within the European Horizon Europe project ELABORATOR, this challenge aims to create an open, extensible, and scalable Data Engine capable of integrating diverse datasets and supporting a new generation of AI-enabled Decision Support Systems (DSS) for urban governance.  

The challenge focuses on the development of a modular multi-source Data Fusion Platform capable of orchestrating open and proprietary datasets to power specialized analytical and predictive urban services. To demonstrate the platform’s effectiveness, proposals should address a first flagship vertical dedicated to Universal Accessibility and Micro-Pedestrian Mobility, while ensuring that the underlying architecture can seamlessly support future application domains such as cycling infrastructure planning, traffic management, and climate adaptation. 

The proposed solution should support two complementary operational dimensions. On the one hand, it should provide advanced decision-support capabilities for public authorities, including multi-source KPI generation, urban benchmarking, scenario simulations, and AI-driven optimization of public investments. On the other hand, it should enable adaptive citizen-facing services through context-aware APIs, personalized routing solutions for users with different accessibility needs, and bidirectional feedback mechanisms allowing citizens to contribute to the continuous improvement of urban datasets. 

Current urban data assets include OpenStreetMap pedestrian networks and accessibility information, municipal demographic datasets, sidewalk characteristics, pavement typologies, LiDAR-based 3D geometries, public transport data, and analytical use cases and UI/UX concepts developed within the ELABORATOR project. 

We are seeking innovative technologies, data architecture (i.e. ETL), analytical tools, artificial intelligence applications, digital services, and scalable solutions that can contribute to the creation of a comprehensive urban data platform.  

We encourage multidisciplinary approaches involving startups, research centers, universities, technology providers, public institutions, and industrial partners. Solutions should demonstrate the potential to improve accessibility, support inclusive mobility, enhance urban planning processes, strengthen data-driven governance, increase operational efficiency, and improve the quality of life for citizens. 

The ambition is to establish a replicable model that can be adopted by Milan and other metropolitan areas facing similar urban planning and mobility challenges, ultimately contributing to the creation of more accessible, inclusive, and sustainable cities for all.