A multi-category intelligent method for the evaluation of visual comfort in underground space

With the increasing use of underground spaces for various functions, improving the comfort levels in such spaces has become imperative. Although this aspect has been largely addressed from a physical standpoint, psychological problems associated with underground spaces still remain a matter of concern. Different with the questionnaire survey-based method, a multi-category measurable method has been proposed herein for evaluating overall visual comfort in underground spaces, by combing the probability statistics methods and deep learning methods, the overall comfort perception related to the color, brightness, scale, spatial form can be finely classified and quantitatively evaluated. Firstly, a multi-category labeling (MCL) method probability based on statistics method is developed, the image comfort level can be more accurate labelled by repeatedly pairwise comparison and probability distribution update, and a dataset is also established for visual comfort labeling and intelligent evaluation. Then, the Swin Transformer is selected as the visual comfort evaluation algorithm, which serves for searching the common vision comfort features of the picture shooting at areas with different functions, and makes the model with more versatility and accuracy. Finally, the Wujiaochang underground space in Shanghai, China, is undertaken as a case study. The results prove that the proposed method can effectively improve the quantification and refinement of human perception and evaluation of comfort in underground spaces. This can eventually lead to the development of more comfortable underground spaces and avoid the psychological problems associated with such spaces.