Using Satellite Images and Deep Learning to Measure Health and Living Standards in India
Journal article, 2023

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.

Measurement of health and living conditions

Deep learning

Satellite images

India

Indicators

Survey

Census

Author

Adel Daoud

University of Gothenburg

Linköping University

Felipe Jordán

Pontificia Universidad Catolica de Chile

Makkunda Sharma

Indian Institute of Technology

Wadhwani AI

Fredrik Johansson

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Sourabh Paul

Indian Institute of Technology

Subhashis Banerjee

Indian Institute of Technology

Ashoka University

Social Indicators Research

0303-8300 (ISSN) 1573-0921 (eISSN)

Vol. 167 1-3 475-505

Subject Categories

Other Computer and Information Science

Bioinformatics (Computational Biology)

Computer Vision and Robotics (Autonomous Systems)

Public Health, Global Health, Social Medicine and Epidemiology

DOI

10.1007/s11205-023-03112-x

More information

Latest update

5/29/2024