Analysing 88 sources published from 2011 to 2021, this paper presents a first systematic review of the computer vision-based analysis of buildings and the built environment. Its aim is to assess the potential of this research for architectural studies and the implications of a shift to a crossdisciplinarity approach between architecture and computer science for research problems, aims, processes, and applications. To this end, the types of algorithms and data sources used in the reviewed studies are discussed in respect to architectural applications such as a building classification, detail classification, qualitative environmental analysis, building condition survey, and building value estimation. Based on this, current research gaps and trends are identified, with two main research aims emerging. First, studies that use or optimise computer vision methods to automate time-consuming, labour-intensive, or complex tasks when analysing architectural image data. Second, work that explores the methodological benefits of machine learning approaches to overcome limitations of conventional analysis in order to investigate new questions about the built environment by finding patterns and relationships between visual, statistical, and qualitative data. The growing body of research offers new methods to architectural and design studies, with the paper identifying future challenges and directions of research.