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2020年通过数据集成和人工智能绘制贫困地图:亚太地区关键指标补充 - 亚洲开发银行(英文版)(45页).pdf

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1、ASIAN DEVELOPMENT BANK A Special Supplement of the Key Indicators for Asia and the Pacific 2020 MAPPING POVERTY THROUGH DATA INTEGRATION AND ARTIFICIAL INTELLIGENCE SEPTEMBER 2020 ASIAN DEVELOPMENT BANK A Special Supplement of the Key Indicators for Asia and the Pacific 2020 MAPPING POVERTY THROUGH

2、DATA INTEGRATION AND ARTIFICIAL INTELLIGENCE SEPTEMBER 2020 Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) 2020 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in

3、2020. ISBN 978-92-9262-313-5 (print); 978-92-9262-314-2 electronic); 978-92-9262-315-9 (ebook) Publication Stock No. FLS200215-3 DOI: http:/dx.doi.org/10.22617/FLS200215-3 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asi

4、an Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not

5、imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area, or by using the term “country” inthis document, ADB does not intend to make any judgments as

6、to the legal or other status of any territory or area. This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) https:/creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be bound bytheterms of this license. For attrib

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本文主要探讨了使用大数据和人工智能技术,特别是卫星图像和神经网络,来估计贫困的方法。文章首先介绍了使用传统数据源估计贫困的方法,包括收入和支出调查以及生活标准调查。然后,文章讨论了使用大数据,特别是地理空间数据和移动电话数据,来增强发展统计数据的潜力。文章重点介绍了使用卫星图像预测贫困的方法,包括两种主要方法:一种是建立结构模型,使用地理空间数据和其他可以从卫星图像中派生的信息作为协变量;另一种是使用神经网络和深度机器学习算法,如卷积神经网络(CNN)。文章还详细介绍了如何使用神经网络开发算法,包括输入层、隐藏层和输出层。最后,文章讨论了国家统计办公室在利用大数据方面面临的挑战,包括获取非传统数据、技术要求、数据隐私和生态系统等。
利用卫星图像如何预测贫困? 神经网络在算法开发中如何应用? 非传统数据源如何增强发展统计?
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