LAUSR

With the growing use of human perception data streams in audits of the built environment, their value for enhancing objectivity and human-centeredness has become increasingly evident. This review synthesizes 63 publications through July 2024, providing a comprehensive analysis of perception data types, collection modalities and spatial strategies. This review introduces an Artificial Intelligence (AI)-enabled framework and utilizes Artificial Intelligence-Generated Content (AIGC) to assist literature retrieval and analysis, improving efficiency and transparency. The results indicate that heart rate and mood are currently the most frequently used perception data types in built-environment audits. Existing audit practices primarily focus on roads, green spaces, and residential areas at community and block-scale settings, with data choices varying by spatial typology. This review advances a systematic understanding of the application of perception data streams in built-environment audits and offers evidence-based recommendations for data collection, thereby providing stronger data support for future research.