  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 environments to assess its value to architectural and urban design
studies. Following a multi-stage selection process, the types of algorithms and
data sources used are discussed in respect to architectural applications such
as a building classification, detail classification, qualitative environmental
analysis, building condition survey, and building value estimation. This
reveals current research gaps and trends, and highlights two main categories of
research aims. First, to use or optimise computer vision methods for
architectural image data, which can then help automate time-consuming,
labour-intensive, or complex tasks of visual analysis. Second, to explore the
methodological benefits of machine learning approaches to investigate new
questions about the built environment by finding patterns and relationships
between visual, statistical, and qualitative data, which can overcome
limitations of conventional manual analysis. The growing body of research
offers new methods to architectural and design studies, with the paper
identifying future challenges and directions of research.
