A Method of Urban Wind Field Visualization Based on Deep Learning

Abstract

In order to solve the problems of incomplete feature extraction, visual results that disrupt the continuity of the flow field, and unstable clustering resulting in poor streamline representation during urban wind field visualization, a three-dimensional streamline visualization method based on deep learning was proposed. This method consists of two parts: one is streamline feature learning, and the other is clustering method. The Euclidean distance represented by the streamline is used as the similarity between the streamlines for clustering, and the clustering results obtained are weighted and combined before being divided. The method was tested on a real urban wind field dataset and qualitatively compared with existing methods. The results show that this method can better balance the relationship between feature extraction and streamline distribution compared to existing methods.