Inam Ullah, Weidong Li, Fanqian Meng, Muhammad Imran Nadeem, Kanwal Ahmed
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引用次数: 0
摘要
本文介绍了一种利用夜间照明(NTL)数据以及 2012 年至 2017 年的社会经济统计数据绘制和评估城市建成区并建立郑州空间国内生产总值(GDP)模型的综合方法。提出了两种监督分类算法,即支持向量机(SVM)算法和深度学习(DL)算法,其中包括 U-Net 和全卷积神经(FCN)网络模型,用于城市建成区识别和图像分类。通过与市统计局数据的比较,U-Net 神经网络模型显示出更高的准确性,尤其是在具有不同特征的区域。基于郑州城市 GDP 和 U-Net 分类图像,建立了 2012 年至 2017 年每年的空间 GDP 模型。这项研究为该市的城市发展和经济评估提供了有价值的见解。
GDP Spatialization in City of Zhengzhou Based on NPP/VIIRS Night-time Light and Socioeconomic Statistical Data Using Machine Learning
This article introduces a comprehensive methodology for mapping and assessing the urban built-up areas and establishing a spatial gross domestic product (GDP) model for Zhengzhou using night-time light (NTL) data, alongside socioeconomic statistical data from 2012 to 2017. Two supervised
sorting algorithms, namely the support vector machine (SVM) algorithm and the deep learning (DL) algorithm, which includes the U-Net and fully convolutional neural (FCN) network models, are proposed for urban built-up area identification and image classification. Comparisons with Municipal
Bureau of Statistics data highlight the U-Net neural network model exhibits superior accuracy, especially in areas with diverse characteristics. For each year from 2012 to 2017, a spatial GDP model was developed based on Zhengzhou's urban GDP and U-Net sorted images. This research provides
valuable insights into urban development and economic assessment for the city.