Quantitative analysis of urban development and changes around urban airports based on high-resolution remote sensing images

Abstract

Given the difficulties in the statistics and investigation of the development and change of the surrounding area of the metropolitan airport, this paper proposes a method based on L-U-NET deep learning network to extract and analyze the objective elements of urban development in the surrounding urban area of Tianjin Binhai International Airport by processing high-resolution remote sensing images. This paper examines the local remote sensing image data for a continuous period through quantitative indicators, counts the specific values of relevant indicators, and gives the analysis of the development of cities around Tianjin Binhai International Airport, which provides a reference for the quantification of the development of regions around airports in following cities of the same type.