Remote Sensing Data Processing of Urban Land Using Based on Artificial Neural Network

With the rapid development of urbanization, the utilization rate of land has become the focus of attention. Remote sensing technology can provide a large amount of data for the prediction of urban land. It is also a thorny problem to find the correlation between the complex data of land change. The neural network technology has obvious advantages in finding the mapping relationship between high-dimensional and nonlinear data. This paper combines the dynamic changes of urban land and neural network methods to analyze the utilization rate and coverage of urban land in the future. In this paper, the data obtained by remote sensing technology is normalized and clustered to classify different types of urban land. Convolutional neural networks and long-short-term memory neural networks are used to extract the spatial and temporal dynamic characteristics of urban land use. The research results show that the clustering method used in this paper can reasonably classify different urban land types, especially the classification of buildings. The method of predicting the future trend of land use is also in line with the dynamic process of land use. The largest prediction error comes from the prediction of the building, and the largest error is only 2.56%, which is a reasonable error range. The smallest error does not exceed 1%, and the correlation coefficient between the real and predicted values of urban land use types reaches 0.9698.