Combining satellite imagery and machine learning to predict poverty
The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we need more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide.
In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth.
For more information, check out...
Our recently published Science paper:
http://science.sciencemag.org/content/353/6301/790
A project website featuring poverty maps of Nigeria, Tanzania, Uganda, Malawi, and Rwanda:
http://sustain.stanford.edu/predicting-poverty
The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we need more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide.
In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth.
For more information, check out…
Our recently published Science paper:
http://science.sciencemag.org/content/353/6301/790
A project website featuring poverty maps of Nigeria, Tanzania, Uganda, Malawi, and Rwanda:
http://sustain.stanford.edu/predicting-poverty
