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The application of protective coating systems is the major measure against the corrosion of stationary marine steel structures. During their service, protective coatings are exposed to various loads, and their protectiveness deteriorates. Digital visual inspection tools are becoming a standard for the condition monitoring of coatings for large and complex structures. One of the challenges with the use of these tools is the accumulation of large amounts of visual data to be evaluated and assessed in short time intervals. A comprehensive interpretation of such image collections requires detection, classification, and quantification processes, which are usually performed by domain experts (coating inspectors). However, the data volume and the rich content of the images make the support by software tools inevitable. This contribution defines requirements for image data annotation, and it presents for the first time the application of an online exploration and annotation tool to coating condition monitoring data and its introduction into a prescriptive coating maintenance model. The software architecture of the system is described, special features are illustrated with different use-cases, and future developments are discussed. The utilization of machine learning-based assessment tools is presented. • First integrative data-driven approach for online analysis of large volumes of visual monitoring data. • Integration of visual data into a prescriptive maintenance model. • Coating deterioration modeling based on pixel footprints of images. [ABSTRACT FROM AUTHOR]