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Combining satellite imagery and machine learning to predict poverty.
Jean, Neal; Burke, Marshall; Xie, Michael; Davis, W Matthew; Lobell, David B; Ermon, Stefano.
Affiliation
  • Jean N; Department of Computer Science, Stanford University, Stanford, CA, USA. Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Burke M; Department of Earth System Science, Stanford University, Stanford, CA, USA. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA. National Bureau of Economic Research, Boston, MA, USA. mburke@stanford.edu.
  • Xie M; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Davis WM; Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.
  • Lobell DB; Department of Earth System Science, Stanford University, Stanford, CA, USA. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.
  • Ermon S; Department of Computer Science, Stanford University, Stanford, CA, USA.
Science ; 353(6301): 790-4, 2016 Aug 19.
Article in En | MEDLINE | ID: mdl-27540167

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Poverty / Developing Countries / Satellite Imagery / Machine Learning / Income Type of study: Evaluation_studies / Prognostic_studies / Risk_factors_studies Aspects: Determinantes_sociais_saude Limits: Humans Country/Region as subject: Africa Language: En Journal: Science Year: 2016 Document type: Article Affiliation country: United States Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Poverty / Developing Countries / Satellite Imagery / Machine Learning / Income Type of study: Evaluation_studies / Prognostic_studies / Risk_factors_studies Aspects: Determinantes_sociais_saude Limits: Humans Country/Region as subject: Africa Language: En Journal: Science Year: 2016 Document type: Article Affiliation country: United States Country of publication: United States