Assessing Human-perceived Window View Openness in High-rise High-density Cities: An Automatic Machine Learning-based City Information Modeling Approach

Abstract: High window view openness benefits urban dwellers’ health and well-being, especially in high-rise, high-density cities. The benefits such as stress relief and mood restoration are further amplified for inhabitants in narrow and small rooms, especially in the post-Covid-19 era. However, the crowded cityscapes and vertical development of the urban environment lead to an imbalanced sharing of window view openness. Currently, there still exists no consensus on the definition of human-perceived window view openness in high-rise, high-density cities, and the assessment methods are limited to small-scale sites and inaccurate. Thus, an urban-scale accurate window view openness assessment is significant in examining the disparity of openness possession and providing quantified evidence for precise decision-making in the healthy high-rise, high-density urban development. The objectives of the proposed project include: i) To develop a human-perceived window view openness index using a hierarchy of window view openness characteristics. ii) To propose an urban-scale assessment method for human-perceived window view openness using photorealistic CIM and Automatic Machine Learning (AutoML). iii)To visualize urban-scale human-perceived window view openness using a 3D GIS platform for the evaluation, planning, and design of healthy high-rise, high-density urban development.