#6295. Evaluation of county-level poverty alleviation progress by deep learning and satellite observations

September 2026publication date
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Journal’s subject area:
Computers in Earth Sciences;
Computer Science Applications;
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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 20XX, and by 20XX, 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 20XX to 20XX 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.
Keywords:
county level; deep learning; Poverty alleviation; remote sensing imagery

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