Images and deep learning in human and urban infrastructure interactions pertinent to sustainable urban studies: Review and perspective

Abstract

As global urbanization intensifies, conflicts between humans and urban infrastructure increasingly affect socio-economic and environmental sustainability. Recently, using image data and deep learning to investigate the interactions between humans and urban infrastructure has been a popular approach since the fast development of Artificial Intelligence (AI). However, the convergence of data fusion, deep learning, and human-urban infrastructure interaction studies remains underexplored. Here we systematically analyze 3,552 papers from 2013 to 2023 that use image data to investigate the intersection area of data fusion, deep learning, and human and urban infrastructure interactions, aiming to elucidate the relationships among these three key elements. We found that the cross-applications of deep learning in the papers reviewed are not standardized. Given the trend of diversified data fusion, data fusion about real-world dynamic interactions is scarce. Lastly, four potential future research directions are identified: (1) understanding the dynamic and complex interaction processes; (2) exploring the potential and standards for the application of deep learning; (3) focusing more on research concerning cities in the Global South; (4) establishing suitable training datasets for the interaction between urban infrastructures and humans, which may provide valuable insights for applying foundation models in future urban studies.