Contemporary Urban Space Philosophy in China Using Lightweight Deep Learning Model-Under Ecological Ethics. (18th March 2022)

• Record Type: Journal Article
• Authors: Cheng, Haiyan
• Other Names: Kumar Vijay Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-03-18
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/8925205
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : It aims to improve the construction of ecological civilization and promote the common development of urban and ecology. Firstly, contemporary ecological ethics is explored, and its principles and characteristics are summarized. Then, the technique of convolutional neural network (CNN) image in a deep learning model is analyzed. Finally, deep convolutional neural networks (DCNN) are used to analyze and model the spatial characteristics of contemporary cities based on ecological ethics. According to the investigations, rural residential areas are more consistent with ecological ethics than urban residential areas when compared with the ecological characteristics of farmland and forest, and the highest ecological eigenvalues of the two areas are about 8 and 6. In the analysis of urban space, the maximum value of ecological eigenvalues of an airport is 9, and that of a stadium is 8. However, the scope of their construction that is consistent with ecological ethics is very small. Moreover, the eigenvalues of ecological ethics in the urban business circle of casinos are not only very low (the highest values are about 5 and 3), but also consistent with the construction norms of ecological ethics. The work of urban spatial philosophy is optimized based on the adoption of the DCNN model of deep learning in ecological ethics, which not only provides the reference for future ecological urban planning but also contributes to the common development of urban and ecology.