空间化
社会经济地位
地理
可见红外成像辐射计套件
地图学
气象学
环境科学
遥感
计算机科学
统计
数学
人口学
艺术
社会学
人口
文学类
校准
作者
Inam Ullah,Weidong Li,Fanqian Meng,Muhammad Imran Nadeem,Kanwal Ahmed
标识
DOI:10.14358/pers.23-00010r2
摘要
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.
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