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Computer vision-based inspection methods show promise for automating post-earthquake building inspections. These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images. Nevertheless, assessing the damage's impact on structural safety requires localizing damage to specific building components with known design and function. This paper proposes a BIM-based automated inspection framework to provide context for visual surveys. A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images. The BIM automatically associates any identified damage with specific building components. Then, components are classified into damage states consistent with component fragility models for integration with a structural analysis. To demonstrate the framework, methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment. A graphics model of a real building in Urbana, Illinois, is generated to test the framework; the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner. A simulated UAV survey is flown of the graphics model and the framework is applied. The method achieves high accuracy in assigning damage states to visible structural components. This assignment enables integration with a performance-based earthquake assessment to classify building safety. [ABSTRACT FROM AUTHOR]