| Popis: |
The scale of urban built-up areas is one of the important indicators for measuring urban development, understanding the evolution and underlying mechanisms of urban built-up areas is of significant value for the development and planning of urban agglomerations. Based on Nighttime Light (NTL) data, Point of Interest (POI) data and LnadScan data, this study constructs a new index to extract the built-up area through multi-source big data fusion, automatically extracts the built-up area of urban agglomerations in a long time series based on U-net neural network, and finally analyzes the dominant factors driving the evolution of built-up area of urban agglomerations in different periods. The results indicate that the fusion of multi-source big data can accurately extract urban built-up areas and analyze their evolution process. The dominant factors driving the evolution of built-up areas vary across different periods, with a weakening influence of the per GDP factors and population dynamics, while the driving force of urban planning for the evolution of built-up areas is strengthened. This study, through the analysis of the evolution process and influencing factors of urban built-up areas in the Yangtze River Delta (YRD) urban agglomeration, contributes to the accurate identification of the internal urban development within the YRD urban agglomeration, assisting in the formulation of subsequent development plans. Furthermore, it provides relevant references for the development of urban areas in other regions. |
