The paper presents a fast methodology to quantify the damage to the roof in historic buildings soon after a light seismic event occurs in order to evaluate the necessity of provisional interventions to prevent further damage. The survey is based on UAV photogrammetry, a well-known technique that allows the inspection of digital documentation and 3D measurements even in difficult to access or dangerous areas. The research aims to analyze the feasibility of the automated mapping of roof damage using a supervised machine learning classifier. The output orthoimage of the tiled roof allowed the understanding of the past interventions and the current serious damage state by quantifying it through an image segmentation procedure, with promising outcomes regarding the speed of the survey method. The procedure is resumed in an efficient workflow wherein UAV photogrammetry is combined with other 3D survey techniques, such as terrestrial photogrammetry and laser scanning, to provide comprehensive documentation and quantitative data of a historical building. The methodology was validated on a large historical building, now suffering from a serious state of neglect; which roof was never surveyed before and with different damage types.