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Measuring poverty in India with machine learning and remote sensing

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arxiv 2202.00109 v2 pith:LETJIZKR submitted 2021-12-27 econ.GN cs.CYq-fin.EC

Adel Daoud , Felipe Jordan , Makkunda Sharma , Fredrik Johansson , Devdatt Dubhashi , Sourabh Paul , Subhashis Banerjee This is my paper · ORCID
classification econ.GNcs.CYq-fin.EC
keywords indialearningachievescensuscomparableconditionsdeepestimate
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In this paper, we use deep learning to estimate living conditions in India. We use both census and surveys to train the models. Our procedure achieves comparable results to those found in the literature, but for a wide range of outcomes.

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  1. Joint Utilization of Geospatial and census proxies for Autoencoder-Assisted Downscaling (JUGAAD) of socioeconomic indicators in India

    cs.LG 2026-07 reject novelty 5.0 of 10

    An autoencoder-plus-regression pipeline downscales Indian NSSO district indicators to village-cluster maps using census and geospatial data, but its reported validation is in-sample rather than independent.

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