Applying Artificial Intelligence on Satellite Imagery to Compile Granular Poverty Statistics

Martin Hofer (), Tomas Sako (), Arturo Martinez, Jr. (), Mildred Addawe () and Ron Lester Durante ()
Additional contact information
Martin Hofer: Vienna University of Economics and Business
Tomas Sako: Freelance data scientist
Arturo Martinez, Jr.: Asian Development Bank
Mildred Addawe: Asian Development Bank
Ron Lester Durante: Asian Development Bank

No 629, ADB Economics Working Paper Series from Asian Development Bank

Abstract: The spatial granularity of poverty statistics can have a significant impact on the efficiency of targeting resources meant to improve the living conditions of the poor. However, achieving granularity typically requires increasing the sample sizes of surveys on household income and expenditure or living standards, an option that is not always practical for government agencies that conduct these surveys. Previous studies that examined the use of innovative (geospatial) data sources such as those from high-resolution satellite imagery suggest that such method may be an alternative approach of producing granular poverty maps. This study outlines a computational framework to enhance the spatial granularity of government-published poverty estimates using a deep layer computer vision technique applied on publicly available medium-resolution satellite imagery, household surveys, and census data from the Philippines and Thailand. By doing so, the study explores a potentially more cost-effective alternative method for poverty estimation method. The results suggest that even using publicly accessible satellite imagery, in which the resolutions are not as fine as those in commercially sourced images, predictions generally aligned with the distributional structure of government-published poverty estimates, after calibration. The study further contributes to the existing literature by examining robustness of the resulting estimates to user-specified algorithmic parameters and model specifications.

Keywords: big data; computer vision; data for development; machine learning algorithm; official statistics; poverty; SDG (search for similar items in EconPapers)
JEL-codes: C19 D31 I32 O15 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2020-12-29
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dev and nep-sea
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