Water companies across the UK (and world) regularly inspect their sewers to prioritise maintenance and ensure the effective operation of their network. Failure to do so can result in incidents, including the discharge of untreated sewage to the environment, pipe collapse or even the formation of sewer blocking fatbergs. The importance of minimising these events is reinforced by the UKWIR objective to achieve zero uncontrolled sewer discharges by 2050. In most cases these occurrences are prevented using CCTV surveying and resolved with an early intervention. However, surveys are time consuming and expensive. Moreover, these reports are often inconsistent and inaccurate, largely due to human error and the subjective nature of fault codes. This project aims to augment the existing annotation and reporting process, with the overall ambition of fully automating the full CCTV surveying process. This proposed combination of AI and robotics will revolutionise sewer surveying and maintenance, improving the speed accuracy and efficiency of the entire practice. In turn this should result in the completion of more surveys and a much higher chance of pre-empting sewer failure. Currently SWW and the UoE are completing a KTP project, to internally implement the prototype fault detection method, investigated during the preceding PhD. The two-year partnership (due to complete in November 2020), has developed and trained the detection system on SWW's archive of CCTV footage and implementing this as a decision support tool. This is capable of highlighting faults and estimating their general type from recorded CCTV footage; extremely useful for the quick analysis of previously unused video that lacks annotation. Alongside technical developments, the project has built a network of collaborators (including iTouch and the WRc), whilst being widely publicised at both academic and industry events. Although the KTP has achieved its goal of bringing a functional tool to SWW, it is clear that the technology has potential for so much more, driving up efficiency and accuracy over current practices. The three key goals of the project are: (1) Develop the annotation capabilities of the technology to achieve the full standards outlined in the MSCC. (2) Implement the developed software so as to assist and perform live reporting. (3) Record and annotate previously unreported pipe features. The proposed project offers the opportunity to not only develop this research into a fully flourished technology for both UK and international use, but provides the resources and foundations for future image processing and machine learning research within SWW and the water industry as a whole. This research would continue to contribute solutions to national and global initiatives, aligning with the UN sustainable development goal ('protecting important sites for terrestrial and freshwater biodiversity'), UKWIR's Big Questions ('How do we achieve zero uncontrolled discharges from sewers by 2050?') and the UK industrial Strategy ('Increase sector productivity utilising AI'). Whether this takes the form of future visual inspection techniques or automation and support of other operational functions, the work would continue to drive efficiencies and improve performance using cutting edge computer science techniques.