Mapping Gridded Gross Domestic Product Distribution of China Using Deep Learning With Multiple Geospatial Big Data
文献类型：期刊论文
| 作者 | Chen, Yuehong1; Wu, Guohao1; Ge, Yong2; Xu, Zekun1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 15页码:1791-1802 |
| 关键词 | Economic indicators Deep learning Convolutional neural networks Decoding Encoding Geospatial analysis Big Data 1-km gridded gross domestic product (GDP) China deep learning downscaling geospatial big data gross domestic product (GDP) |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2022.3148448 |
| 通讯作者 | Ge, Yong(gey@igsnrr.ac.at) |
| 英文摘要 | Timely griddedgross domestic product (GDP) data is a fundamental indicator in many applications. It is critical to characterize the complex relationship between GDP and its auxiliary information for accurately estimating gridded GDP. However, few knowledge is available about the performance of deep learning approaches for learning this complex relationship. This article develops a novel convolutional neural network based GDP downscaling approach (GDPnet) to transform the statistical GDP data into GDP grids by integrating various geospatial big data. An existing autoencoder-based downscaling approach (Resautonet) is employed to compare with GDPnet. The latest county-level GDP data of China and the multiple geospatial big data are adopted to generate the 1-km gridded GDP data in 2019. Due to the different related auxiliary data of each GDP sector, the two downscaling approaches are first separately built for each GDP sector and then the results are merged to the gridded total GDP data. Experimental results show that the two deep learning approaches had good predictive power with R-2 over 0.8, 0.9, and 0.92 for the three sectors tested by county-level GDP data. Meanwhile, the proposed GDPnet outperformed the existing Resautonet. The average R-2 of GDPnet was 0.034 higher than that of Resautonet in terms of county-level GDP test data. Furthermore, GDPnet had higher accuracy (R-2 = 0.739) than Resautonet (R-2 = 0.704) assessed by town-level GDP data. In addition, the proposed GDPnet is faster (about 78% running time) than the Resautonet. Hence, the proposed approach provides a valuable option for generating gridded GDP data. |
| WOS关键词 | ECONOMIC-ACTIVITY ; TIME-SERIES ; LAND-COVER ; GDP ; POPULATION ; IMAGERY ; SPATIALISATION ; LEVEL |
| 资助项目 | National Key Research and Development Program of China[2019YFC1510601] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20030302] ; National Natural Science Foundation of China[42071315] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000761219000002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/171516]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ge, Yong |
| 作者单位 | 1.Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Yuehong,Wu, Guohao,Ge, Yong,et al. Mapping Gridded Gross Domestic Product Distribution of China Using Deep Learning With Multiple Geospatial Big Data[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2022,15:1791-1802. |
| APA | Chen, Yuehong,Wu, Guohao,Ge, Yong,&Xu, Zekun.(2022).Mapping Gridded Gross Domestic Product Distribution of China Using Deep Learning With Multiple Geospatial Big Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,15,1791-1802. |
| MLA | Chen, Yuehong,et al."Mapping Gridded Gross Domestic Product Distribution of China Using Deep Learning With Multiple Geospatial Big Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 15(2022):1791-1802. |
入库方式： OAI收割
来源：地理科学与资源研究所
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