Environmental monitoring and landscape design of green city based on remote sensing image and improved neural network

Abstract

In some developed countries in Europe, cities are developing very rapidly, but the rapid development of these countries also has to go through the following stages, which are the three essential development processes of urbanization, counter-urbanization and re-urbanization. My country's development situation is relatively uneven. In the more developed areas of my country, the speed of urbanization is relatively rapid, such as cities near the Yangtze River Delta and the Pearl River Delta. But with the development of urbanization, these areas are also facing a very troublesome problem, that is, the balance between the development of the city and the environmental ecosystem. In order to monitor this situation, we will use remote sensing technology to conduct a comprehensive survey of the urban environmental ecosystem and analyze the impact and effect of the rapid development of the city on the changes in the ecosystem. This article will focus on the area around the Yangtze River Delta, and investigate the spatial patterns of urban environmental ecosystems by comparing this area with cities in developed regions such as Western Europe and North America. Under the conditions of different regions and different degrees of urbanization, the comprehensive situation of environmental elements in the city is investigated, and the environmental impact effects and the causes, effects and effects of environmental changes are more accurately studied. Combine the BP network model related to the nervous system in the experiment, and continuously optimize the model during the experiment. The experimental data selects environmental difference reports displayed in different years and different seasons to predict the air pollution value of a certain city. The reason why we use this method is that this model can improve the accuracy of statistical data and greatly simplify the unnecessary and complicated steps in the research process, and it can also reduce the error problems generated in the operation of the BP system, such as when we When entering a piece of data, the data obtained is often very different from the target data. Therefore, we will use the ant colony algorithm to solve the above problems, and add stereo and 3D technologies, which can monitor experiments in real time, greatly improve the speed of the experiment, and the 3D stereo technology can make the results more perceptual to meet the needs of the experiment.