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# Poverty estimates
This study is investigating using existing survey data and earth observation data to train a ML model that can then predict poverty rates using only earth observation data.
I think in principle the idea of using earth observation and readily available imagery to model systems is very powerful. Mainly as once you havea trained model the only limit on your use of the model throughout the world is based on the models generability.
Some questions that I have are:
- I assume that they used some other "off the shelf" style models or datasets to get the earth observation data. However did they consider/investigate the idea of using satalite imagery directly with Neural Networks?
- With regard to the under performance in the urban areas was it due to the data not being dense enough in that area (i.e trying to predict 10,000 people from the same size set of earth observation as opposed to only 100 people in rural areas), or was it more to do with urban areas have differnet relationships with assests and spending so less applicable of a method?