Yaping Chen’s research while affiliated with Zhejiang University of Technology and other places

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.

The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas.