Automatic extraction of built-up areas in Chinese urban agglomerations based on the deep learning method using NTL data

Abstract

Rapidly and accurately extracting built-up areas is an essential prerequisite of urbanization research. There have been many studies on the extraction of built-up areas using remote sensing technologies. So far, few studies have been conducted to evaluate the applicability of the deep learning method to extract built-up areas under the condition that only nighttime light (NTL) data are used. This study proposed a deep learning method to extract the built-up areas using NTL data, and applied the method to analyze the spatial and temporal changes of the built-up areas in Chinese two urban agglomerations from 2000 to 2020. The results show that the U-Net deep learning method can be used to extract built-up areas efficiently under the condition that only NTL data are used. The proposed method was able to improve the accuracy of built-up area extraction significantly compared to the existing method. For the extraction of built-up areas in large regions with long time series, the proposed method can facilitate the work and improve the processing efficiency. The gravity centre of the built-up areas in the Central Plains Urban Agglomeration migrated south-eastward, and the gravity centre of the built-up areas in the Shandong Peninsula Urban Agglomeration migrated south-westward, with these gravity centres gradually approaching the geometric centres of the corresponding urban agglomerations. The built-up areas in the Central Plains and Shandong Peninsula Urban Agglomerations grew rapidly, increasing by 4.14 times and 3.73 times from 2000 to 2020, respectively. The built-up areas in the Central Plains Urban Agglomeration expanded faster, while the urban development degree of the Shandong Peninsula Urban Agglomeration was higher. The urban distributions and development modes of these two urban agglomerations were quite different. The Central Plains Urban Agglomeration tended to further agglomerate, while the Shandong Peninsula Urban Agglomeration tended to disperse.

Keywords

• built-up area
• nighttime light data
• deep learning
• urban agglomeration