Quantitative Research on Complexity of Urban Environment Based on AI Model

Visual complexity is a pivotal metric in urban environment quality, and the objective quantification of visual complexity holds immense importance for research in this field. Drawing from the definition of visual complexity, this paper categorizes complexity into environmental element feature complexity, Edge Feature Complexity, and color complexity. By harnessing computer image processing technology and incorporating artificial intelligence methodologies, the study successfully deploys a supervised training model to amalgamate these multiple features, which created a preliminary framework for quantifying visual complexity, offering valuable insights for future investigations into human aesthetic experiences and urban visual quality assessments.