Changes of Remote Sensing Data in Urban Buildings Based on Neural Network Algorithm

Abstract

Remote sensing data refer to remote sensing data sets with typical big data characteristics obtained by various remote sensing technologies. Based on this understanding, massive remote sensing datasets are the main method, and auxiliary data from other sources are integrated. The DN value (digital number) is the brightness value of the remote sensing image pixel and the gray value of the recorded ground objects. Unitless is an integer value. The value is related to the sensor’s radiation resolution, ground object emissivity, atmospheric transmittance, and scattering rate. This study aims to study the neural network algorithm for remote sensing data changes in urban buildings. This study deliberately goes to Tianjin Binhai New Area, taking this as an example to analyze and study the change of construction land. It uses satellite remote sensing data as a basis to analyze the trend of its building land. This study proposes which factors lead to the decline of urban construction land in Tianjin Binhai New Area. Finally, through experimental research and analysis, it is found that from 1996 to 2016, the registered population of Tianjin Binhai New Area has increased a lot, accounting for 26.49% of the total population. The number of urban employees increased by 956,500. This is also the leading factor in the direction of the city for the building.