• Corpus ID: 219708523

Predicting Livelihood Indicators from Crowdsourced Street Level Images

@article{Lee2020PredictingLI,
  title={Predicting Livelihood Indicators from Crowdsourced Street Level Images},
  author={Jihyeon Janel Lee and Dylan Grosz and Sicheng Zeng and Burak Uzkent and M. Burke and D. Lobell and Stefano Ermon},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.08661}
}
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world. We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery. Such imagery can be cheaply collected and more frequently updated compared to traditional surveying methods, while… 
1 Citations
Poverty Classification Using Machine Learning: The Case of Jordan
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An original machine learning approach to assess and monitor the poverty status of Jordanian households is proposed, which takes into account all the household expenditure and income surveys that took place since the early beginning of the new millennium and makes poverty identification cheaper and much closer to real-time.

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