Quantum technology – mapping and map integration for buried assets (QT-MIBA)

Quantum technology -- mapping and map integration for buried assets (QT-MIBA) seeks to evaluate the feasibility of obtaining and publishing more complete and accurate information on the location of buried assets through enhanced processing of geophysical sensor data. The goal of QT-MIBA is to address the accidental strikes on underground utility pipes and cables that cost the country £1.2bn a year as well as reducing the traffic delays caused by utility streetworks estimated as 6.16 million days of work lost between 2014-2015\. It will also prevent incidents of workers accidentally hitting gas and electric pipes and thereby endangering their lives and interrupting supply of services to customers.

QT-MIBA represents a major collaboration between Great Britain's national mapping agency and world-leading geospatial authority, an asset owner, a survey company, a data processing SME and an academic partner leading the application of quantum technology sensors for civil engineering applications.

The project aligns with quantum technology sensor development, by providing a roadmap and value assessment of the data to end users. It also supports the initiative promoted by the Geospatial Commission to bring together existing data on underground infrastructure currently held by individual organisations (both privatised and non-privatised) to create a National Underground Asset Register (NUAR). OS and NWL currently collaborate on a pilot project in the North East to explore how accurate geospatial data can reduce the likelihood of utility strikes, improve underground infrastructure maintenance and inform new-build development projects. While bringing together existing buried infrastructure data is a significant step forward, there are many questions about the quality of this existing data, including omissions. There is, then, a role for data derived from geophysical surveys to update statutory record data.

QT-MIBA will deliver a feasibility study to assess how data from QT, combined with data from traditional geophysical sensors, can be enhanced using novel processing techniques including Artificial Intelligence, deep learning and quantum machine learning. Moreover, it will develop protocols which will enable survey data collected at disparate locations across the network to be integrated into geospatial maps. This will enable an assessment of the value of enhancing the positional accuracy of buried asset records without the need to wait until they are dug up for maintenance.