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Poverty Prediction by Satellite Images using Deep Learning

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Poverty Prediction by Satellite Images and Deep Learning

Capstone Project 2 (Springboard - Data Science Career Track)

Chiyuan Cheng (08/2020)

Summary

  • This project uses transfer learning to predict poverty (Wealth index) of a sub-Saharan African country, Burundi, in 2010.
  • Regression models are used to predict Wealth index from luminosity of nighttime satellite images, with r-squared of 0.54.
  • Gaussian Mixture Model is used to classify the daytime satellite imagery into three classes, based on the luminosity.
  • Transfer learning (VGG16, ResNet50, Inception C3) to capture features from daytime satellite imagery and predict poverty, with the 80% accuracy from the best model.

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Report

Jupyter Notebook

  1. Data Access
  2. Data Prep (Nightlight Satellite Image)
  3. Machine Learning (Nightlight)
  4. Data Processing (Daylight Satellite Image)
  5. Data Analysis (Simularity
  6. Deep Learnining

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