Research on the Change in Public Art Landscape Pattern Based on Deep Learning

Abstract

With the limited design level of urban external space and place environment, however, the city image design and public art design play an important role in urban development, which leads to the lack of rational understanding that vague urban style is the effective implementation. In this article, a deep learning model is proposed to study the changes in public art landscape pattern in urban space, and it is constantly found that the changes in urban spatial layout have an impact on urban development. Firstly, the landscape index was analyzed effectively, and the Markov model was used to predict land use change, which provided a theoretical basis for the analysis of urban landscape change. Then the GeoSOS-FLUS model based on deep learning is used to make up for the lack of diversity of land use types by using the suitability probability calculation module of ANN and the adaptive inertia and competition mechanism, and the competition between different land use types is introduced. The experimental results show that the GeoSOS-FLUS framework based on deep learning model has good prediction effect and application ability.