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Using Satellite Ima...
Using Satellite Images and Deep Learning to Measure Health and Living Standards in India
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- Daoud, Adel, 1981 (författare)
- Linköpings universitet,Institutet för analytisk sociologi, IAS,Filosofiska fakulteten,Chalmers Univ Technol, Sweden,Göteborgs universitet,University of Gothenburg,Linköping University
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- Jordán, Felipe (författare)
- Pontificia Universidad Catolica de Chile,Pontificia Univ Catolica Chile, Chile; Pontificia Univ Catolica Chile, Chile
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- Sharma, Makkunda (författare)
- Indian Institute of Technology,Wadhwani AI, India; Indian Inst Technol Delhi, India
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- Johansson, Fredrik, 1988 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,Chalmers Univ Technol, Sweden
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- Dubhashi, Devdatt, 1965 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,Chalmers Univ Technol, Sweden
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- Paul, Sourabh (författare)
- Indian Institute of Technology,Indian Inst Technol Delhi, India
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- Banerjee, Subhashis (författare)
- Indian Institute of Technology,Ashoka University,Ashoka Univ, India; Indian Inst Technol Delhi, India
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(creator_code:org_t)
- SPRINGER, 2023
- 2023
- Engelska.
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Ingår i: Social Indicators Research. - : SPRINGER. - 0303-8300 .- 1573-0921. ; 167:1-3, s. 475-505
- Relaterad länk:
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https://research.cha... (primary) (free)
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https://liu.diva-por... (primary) (Raw object)
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https://research.cha...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Nationalekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Economics (hsv//eng)
Nyckelord
- Measurement of health and living conditions
- Deep learning
- Satellite images
- India
- Indicators
- Survey
- Census
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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