High Resolution Mapping of Food Security

for information on the project, please refer to the GitHub page.

The application takes as input geo-referenced survey data, then for every survey cluster:

• downloads relevant satellite images from the Google Maps Static API and Sentinel-2 from Google Earth Engine API. The class that handles this is the img_lib.py
• extract features from the images using neural networks trained here. Class that handles it is the nn_extractor.py
• extract features as distance to hospital and school from OpenStreetMap using the Overpass API. The [OSM_extactor.py handles that part.
• extract remote sensing indices from Sentinel 2, namely NDVI, NDBI and NDWI. S2_indexes.py handles this part.
• pull information from ACLED on violent events.

• use ridge regression to infer indicator's value from the extarcted features.

  All of the training is coordinated by the scripts/master.py. Predictions for an area are made with scripts/score_area.py.

The trained models can then be used for making predictions in areas where no data is available. Use the scripts/score_area.py for that. Work is in progress in the application directory for taking the method to produciton.

How to run the code:

File-system

Make sure to have the following file-system in place:

config
└── example_config.yaml 
Data
├── datasets
│   └── processed_survey.csv
├── Features
├── Geofiles
│   ├── ACLED
│   ├── NDs
│   ├── nightlights
│   ├── OSM
│   └── Rasters
│       └── base_layer.tif
└── Satellite   
    ├── Sentinel
    └── Google   
Models/
env.list

The mandatory files are:

Data/datasets/processed_survey.csv this is your survey data! should contain at least 3 columns: "gpsLongitude","gpsLatitude" and one indicator. You can either work with individual survey data or aggregate the surveys to some geographic level.

Data/Geofiles/Rasters/base_layer.tif is a raster file that containing the area of interest and the population density. Survey points will be snapped to its grid and the pulled layers over-laid.Please use 100x100m resolution WorldPop's rasters, available here.

config/example_config is the config file that you should fill in. Please use the template provided, fields list in there.

env.list this should contain the key to access the Google Maps Static API. After you get yours, add it to the file if you will be using Docker or to your environment variables if you run with Python. The format should be Google_key=<your key here>.

Google Earth Engine API credentials

Because you will be pulling Sentinel-2 and Nightlights data from Google Earth Engine, you will need to set up some credentials. Not so easy because of Google's OAuth2. Please follow this link to create your credentials file.

Train Model

To run the app that trains the model on your survey data you can either set up your python environment (install libraries listed in environment.yml) or use docker.

With Python

To run the training with Python simply run the /scripts/master.py:

python master.py args 

where args is one or more example_config.yaml. Each .yaml should be space separated. Please run from the root directory of the application. For example to trigger for configs config_1.yaml, config_2.yaml and config_3.yaml do:

python master.py config_1.yaml config_2.yaml config_3.yaml > log.txt &

This will:

• download the relevant satellite images. (if not there already)
• extract the features for each image. (if no features for that id)
• pull and vectorize data from OSM, ACLED, Sentinel-2 and NOAA
• train the model on the existing data.
• write r2 Pearson scores to the Results/ directory on a 5-fold cross validation loop.
• save the full predictions on the left-out data, aslo in Results/.
• save the trained model.

With Docker

If you want to use docker, build the image with docker build -t hrm . then run with:

docker run -v ~/Desktop/HRM/HRM/Data:/app/Data -v ~/.config/earthengine:/root/.config/earthengine --env-file ./env.list hrm ../config/example_config.yaml

First -v flag maps local directory Data to the same directory in the container. Second -v maps the earth engine credentials. The --env-file ./env.list adds the Google_key environment variable to the container.

Contacts

For more info to collaborate, use or just to know more reach us at jeanbaptiste.pasquier@wfp.org and lorenzo.riches@wfp.org or submit an issue.