The applicant, a company called Assetscape, has been contacted by one of their clients, WSP, who is an agent of the UK National Highways Agency. National Highways and their various framework operators monitor and maintain England's strategic road network. One way that they do this is by taking very large quantities of pictures of the road surface using specially modified vehicles that traverse the network over a two year cycle. This very large number of images is then examined and audited by human inspectors to see if areas of the road need repair or preventative maintenance. National Highways has tried to automate this process but has failed to find software systems that can reliably identify worn or damaged sections of the road better than human inspectors.  WSP, on behalf of National Highways, has asked Assetscape if it would be able to develop an Artificial Intelligence solution that is capable of evaluating and auditing the large body of road surface image data to reliably identify road markings (e.g. white lining) that are in need of repair, replacement or removal. This would save a great deal of money, time and provide better maintenance of the roads. Assetscape is applying for grant funding from the Innovate UK Transformative Technologies competition to experiment with the latest generation of AI to enable the team to demonstrate reliable road surface marking evaluation and condition reporting along with data-output auditing functionality. Once this research work is proven, the business will be in a position to invest in the development of a full scale commercial application for UK customers (e.g. National Highways), other highway authorities and road agencies overseas. The project and funding will enable Assetscape to access a large amount of road image data, experiment and develop the AI technology to process these images automatically. Once developed, the team will then test the performance of the AI-based system by comparing the performance of the software with expert human road surface auditors.