The University of Cambridge’s mission on AI, ai@cam, has announced six projects for its inaugural Local Government AI Accelerator.

The programme establishes a new model for how universities and government can work together to advance AI innovation in public services.

The Accelerator is funded by the Ministry of Housing, Communities and Local Government (MHCLG).

The 12-month programme pairs Cambridge researchers directly with local councils to develop practical, proof-of-concept AI solutions to real operational challenges, from automating housing data collection to detecting fly-tipping using cameras on refuse vehicles.

The Accelerator will pilot a new model for university-council collaboration by embedding councils as active partners from the outset and building shared capability across the sector.

Each project will receive up to £25,000, dedicated technical support from machine learning engineers, and access to a structured community of practice. The programme places public concerns directly into the research process.

Jessica Montgomery, director of ai@cam said: "This partnership with MHCLG represents a new model for how universities and government can work together to advance AI innovation in public services.

“We’re building a community of practice where local authorities can share challenges and collectively advance what’s possible in public service delivery.”

One project will look at AI-Enabled Surveys for Housing Trajectories. Dr Matteo Zallio, University of Cambridge will work with Greater Cambridge Shared Planning Service (Cambridge City Council and South Cambridgeshire District Council) to develop a human-in-the-loop AI-enabled workflow to automate questionnaire drafting and distribution, convert free-text responses into structured data, and provide live monitoring dashboards for planning officers. It is hoped that this will free officers from repetitive administrative tasks and deliver faster and more consistent housing delivery evidence, improve the experience for external respondents, and produce an open, reusable framework that other local authorities can adopt.

Alexis Litvine, University of Cambridge will work with Greater Cambridge Shared Planning Service on 'MAPLE: Map Automation for Planning and Local Efficiency'. MAPLE will develop an AI-assisted pipeline that automates map detection, georeferencing, and object extraction from submitted planning documents, outputting structured vector data compatible with existing GIS workflows. Cambridge City Council and South Cambridgeshire District Council collectively process tens of thousands of planning-related map submissions each year, each requiring manual georeferencing and vectorisation by specialist GIS officers — a process that can take between five minutes and over an hour per map. The project aims to reduce GIS processing time per map by at least 75%, freeing specialist capacity for higher-value work, accelerating application turnaround, and producing open-source tools and methods transferable to local authorities across the UK.

Other projects will look at Predictive Risk Intelligence for Social Housing Maintenance; Deep Learning for Fly-tipped Waste Detection; Bidding Behaviour and Outcomes in Choice-Based Lettings; and Human-Oriented AI: Design Framework for Reaching Vulnerable Tenants.