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 Areas, Road Structure, and Industrial Areas.

EXISTING SYSTEM

• 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.

DISADVANTAGES

• 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.

PROPOSED SYSTEM

• 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.

PROJECT VIDEO

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