Poverty Prediction using Satellite Images | Machine Learning Project Ideas
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ABSTRACT
Determining the poverty levels of various regions throughout the world is crucial in identifying interventions for poverty reduction initiatives and directing resources fairly.
However, reliable data on global economic livelihoods is hard to come by, especially for areas in the developing world, hampering efforts to both deploy services and monitor/evaluate progress.
This project proposes to use satellite images to detect economic activity and, as a result, estimate poverty in a location.
A Recurrent neural network is trained to learn various developmental parameters like rooftop type, source of lighting and proximity to water sources, Agriculture Area, Industrial Areas.

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