Evaluation of county-level poverty alleviation progress by deep learning and satellite observations. Issue 4 (26th November 2021)

• Record Type: Journal Article
• Authors: Jiang, Yanxiao; Zhang, Liqiang; Li, Yang; Lin, Jintai; Li, Jingwen; Zhou, Guoqing; Liu, Suhong; Cao, Jing; Xiao, Zhiqiang
• Is Part Of: Big earth data. Volume 5:Issue 4(2021)
• Page Start: 576
• Page End: 592
• Publication Date: 2021-11-26
• Subjects: Poverty alleviation -- remote sensing imagery -- deep learning -- county level; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2021.1967259
• Languages: English
• ISSNs: 2096-4471

Abstract

ABSTRACT: Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries. China, which had the largest rural poverty-stricken population, has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation (TPA) policy in 2014, and by 2020, all national poverty-stricken counties (NPCs) have been out of poverty. This study combines deep learning with multiple satellite datasets to estimate county-level economic development from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties (NPCs) at country, provincial and county levels. Per capita gross domestic product (GDP) is used to measure the affluence level. From 2014 through 2019, the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%, higher than the average growth rate of 310 adjacent non-NPC counties (7.3%±0.4%) and of the whole country (6.3%). We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages. The inexpensive, timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.