NIA SSEN 0084 AI-ID: AI Identification of service cable location

Key Activities

Significant increases in domestic electricity demand means that many service cables feeding individual properties will need to be changed to accommodate higher loads. Many of these cables have been in place for decades and network companies do not have good enough records to understand the location of these assets or their replacement requirements. This project will develop a novel AI-enabled system using Google Maps Street View images (street view) and LiDAR maps to locate service-cables between the street and the property. Where service-cables are underground/visually obscured between properties, machine learning models will infer cable locations, either directly between the mains and identified service point, or from the adjacent property and automatically populate GIS maps with vectors representing identified service-cable position so allowing efficient replacement/upgrade.

The skills and expertise required to fully develop and deliver this project do not currently exist within SSEN and will therefore be sourced through a collaboration with Aston University. As part of this partnership, a new post will be created at Aston University, with the appointed individual based at the project site in Thatcham. This role will enable the direct transfer of knowledge from Aston’s academic experts to the SSEN project team.

The project will be supported by both NIA and Knowledge Transfer Partnership (KTP) funding. KTPs, supported by Innovate UK, connect businesses with academic expertise to deliver tailored, innovative solutions. Through the programme, a highly qualified graduate is placed within the business to lead solution development, supported by the academic partner. The partnership is part-funded by Innovate UK and other government co-funders, with the remaining costs contributed by the business.

For more information on Innovate UK Knowledge Transfer Partnerships, please visit https://iuk-ktp.org.uk/

Expected Outcomes

The successful conclusion of this project will result in the inclusion of vectors representing the identified cable position of each property in a selected GIS sub-section the SEPD area. It is envisaged that at the end of this project, the AI-enabled mechanism for automating the process of locating power cables will be implemented on SSEN’s GIS for further testing and expansion through a pilot project.

This project aims to deliver the following objectives:

1. Use multiple sources to identify candidate cable regions
2. Create a method of identifying where service cables run into domestic properties using visual cues
3. Use LiDAR to identify overhead cable locations
4. Design and train a purpose-built AI model to estimate location of underground and looped services within defined area of SEPD
5. Create a vectorised GIS map featuring cable location for each property and test with users within a defined area of SEPD
6. Create plan to integrate the tool with existing workflows
7. Embed new knowledge into SSEN and share with other DNOs

Project Budget

£193,074 – NIA funding
£193,074 – KTP funding

Start/End Date

June 2026 – December 2028

Project Manager

Cori Critchlow-Watton

Additional Resource

NIA SSEN 0084 : AI-ID: AI Identification of service cable location : ENA Smarter Networks Portal