Using Satellite Images and Deep Learning to Measure Health and Living Standards in India

Adel Daoud (), Felipe Jordán, Makkunda Sharma, Fredrik Johansson, Devdatt Dubhashi, Sourabh Paul and Subhashis Banerjee
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Adel Daoud: Linköping University
Felipe Jordán: Pontificia Universidad Católica de Chile
Makkunda Sharma: Wadhwani AI
Fredrik Johansson: Chalmers University of Technology
Devdatt Dubhashi: Chalmers University of Technology
Subhashis Banerjee: Ashoka University

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2023, vol. 167, issue 1, No 19, 475-505

Abstract: Abstract Using deep learning with satellite images enhances our understanding of human development at a granular spatial and temporal level. Most studies have focused on Africa and on a narrow set of asset-based indicators. This article leverages georeferenced village-level census data from across 40% of the population of India to train deep models that predicts 16 indicators of human well-being from Landsat 7 imagery. Based on the principles of transfer learning, the census-based model is used as a feature extractor to train another model that predicts an even larger set of developmental variables—over 90 variables—included in two rounds of the National Family Health Survey (NFHS). The census-based-feature-extractor model outperforms the current standard in the literature for most of these NFHS variables. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep models that track human development at an unprecedented geographical and temporal resolution.

Keywords: Measurement of health and living conditions; Indicators; Deep learning; Satellite images; India; Survey; Census (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11205-023-03112-x

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