• 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 Areas, Road Structure, and Industrial Areas.


  • Currently, poverty is formally calculated by numerous philanthropic agencies including the World Bank.
  • One of the reasons why data on poverty is sparse in the developing world is because it is infrequently collected due to the high cost associated with on-the-ground surveys.
  • Only after this step does the country realize where it stands with respect to the income levels.


  • The current challenge in this domain is that agencies across the world who predict income levels take a huge amount of time to do the same.
  • Once done this topic is not raised until the next decennial census comes up.
  • Not only does it take a big chunk of time but also staggering amounts of money is invested into these kinds of projects.
  • This is a real headache for agencies and governments all around the world.


  • Recent advancements in deep learning present an exciting opportunity for application to poverty prediction.
  • More specifically, both daytime and nighttime satellite imagery of regions can be used to estimate poverty in certain regions.
  • Deep learning has been a main factor behind recent breakthroughs in numerous computer vision tasks such as image classification, segmentation, and object detection.
  • In this project, we test the hypothesis that deep learning can leverage satellite imagery to reliably predict the poverty level of a region.
  • We assemble a dataset of 88,386 images from 44,193 cities spanning Africa, South America, Asia, Europe, and the Caribbean.
  • For each city, we obtain a daytime satellite image, a nighttime satellite image, and the city’s wealth index.
  •  I then train Recurrent neural networks (RNNs) to predict a city’s wealth index, given a satellite image.



Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements:

  • Hard Disk: Greater than 500 GB
  • RAM: Greater than 4 GB
  • Processor: I3 and Above

 Including Packages

* Base Paper

* Complete Source Code

* Complete Documentation

* Complete Presentation Slides

* Flow Diagram

* Database File

* Screenshots

* Execution Procedure

* Readme File

* Addons

* Video Tutorials