Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the field of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performance. However, creating and analyzing digital twins of stone masonry buildings can be a complex and time-consuming process that requires specialized skills and equipment. In this paper, we present various methodolo- gies for the generation of damage augmented digital twins (DADTs) of stone masonry buildings that involve the use of machine learning and computer vision techniques to automate the process. These methodologies include crack segmen- tation using convolutional neural networks, crack characterization using machine learning, automatic generation of simplified geometries of buildings, generation of DADTs containing geometrical and damage information, generation of finite element models for stone masonry buildings, and geometrical digital twins for stone masonry elements for numerical modeling. We demonstrate the effective- ness of these methodologies using a variety of datasets and show that they can significantly improve the accuracy and speed of damage assessment compared to traditional methods. Our work contributes to the development of a framework for real-time damage assessment of stone masonry buildings and lays the foundation for future research in this area.