To remain cost-competitive the wind industry must find new efficiencies such as transitioning from reactive maintenance to a more proactive strategy that exploits advanced analytics to identify damage. In this talk, Jonathan and Ulrich will discuss their work with Sulzer&Schmid Laboratories, a Swiss company that provides wind turbine inspection services, to automate the image pre-annotation stage of their inspection pipeline using deep learning-based predictive analytics. They will discuss the challenges related to training large supervised models with incomplete and noisy labels, and demonstrate the solution in action, showing how it not only reduces cost and increases throughput, but also actually improves upon the performance of the human annotators in terms of both accuracy and utility.