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

arXiv 2607.20559 ↗First posted 17 Jul 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Monitoring poverty and food security indicators is imperative for addressing socioeconomic challenges in developing nations. A limitation is mismatches in scale between data sources: census data provide geographic coverage, while socioeconomic indicators are derived from infrequently conducted surveys at coarse resolutions, posing a methodological challenge. This study introduces a deep learning framework, JuGAAD, using Indian census and survey data from 2001 and 2011 as a case study. We employ a three-step process: census and geospatial data are averaged into intermediate village-cluster-scale tessellations to reduce noise and regularize administrative boundary changes; an autoencoder compresses high-dimensional National Sample Survey Office (NSSO) data into a low-dimensional latent representation; and a regression model maps upscaled census and geospatial data to this representation. This function is applied to fine-grained census data to generate high-resolution predictions, validated against ground-truth district-level NSSO indicators. Results confirm the methodology predicts socioeconomic indicators at fine scales with strong accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 41 references that are in the map. First posted on arXiv 17 Jul 2026.

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References · 9

  1. 2014CNN Features off-the-shelf: an Astounding Baseline for Recognitioncited by 316
  2. 2020A Survey on Neural Network Interpretabilitycited by 291
  3. 2013OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networkscited by 224
  4. 2016Variational Autoencoder for Deep Learning of Images, Labels and Captionscited by 137
  5. 2015Transfer Learning from Deep Features for Remote Sensing and Poverty Mappingcited by 37
  6. 2016Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencodercited by 34
  7. 2020Generating Interpretable Poverty Maps using Object Detection in Satellite Imagescited by 27
  8. 2020Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Informationcited by 8
  9. 2021Measuring poverty in India with machine learning and remote sensingcited by 2

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