vishakhanihore/Poverty-Predictor

Combining satellite imagery and machine learning to produce poverty

Combining satellite imagery and machine learning to predict poverty

Combining satellite imagery and machine learning to produce poverty

Code was written in R 3.2.4

Users of these data should cite Jean, Burke, et al. (2016). If you find an error or have a question, please submit an issue.

Description of folders

• data: Input and output data stored here
• figures: Notebooks used to generate Figs. 3-5
• scripts: Scripts used to process data and produce Fig. 1
• model: Store parameters for trained convolutional neural network

Packages required

R

• R.utils
• magrittr
• foreign
• raster
• readstata13
• plyr
• RColorBrewer
• sp
• lattice
• ggplot2
• grid
• gridExtra

The user can run the following command to automatically install the R packages

install.packages(c('R.utils', 'magrittr', 'foreign', 'raster', 'readstata13', 'plyr', 'RColorBrewer', 'sp', 'lattice', 'ggplot2', 'grid', 'gridExtra'), dependencies = T)

Python

• NumPy
• Pandas
• SciPy
• scikit-learn
• Seaborn
• Geospatial Data Abstraction Library (GDAL)
• Caffe

Caffe and pycaffe

• See Caffe Installation

We recommend using the open data science platform Anaconda.

To generate Figure 5, the user needs to run

1. DownloadPublicData.R
2. ProcessSurveyData.R
3. save_survey_data.py
4. get_image_download_locations.py
5. (download images)
6. extract_features.py
7. Figure 5.ipynb

Referrence Taken From - : https://arxiv.org/abs/1510.00098 http://science.sciencemag.org/content/353/6301/790