This repo takes Matt McFarland's raster lambda demo and uses it to provide stats and raster-to-vector downloads for our GLAD data.
After receiving an input polygon and an operation type (stats or download), the code looks at the GLAD raster mosaic stored in data/glad.vrt. This points to a bunch of rasters, like this one over part of Nigeria:
s3://palm-risk-poc/data/glad/analysis-staging/afr_asia/tiles/000E_00N_010E_10N/raster_processing/date_conf/1_prep/date_conf_all_nd_0.tif
The endpoints deployed are designed to exactly mimic existing GFW API endpoints, making it easy to 'plug' this service into existing code. The current exposed endpoints are as follows:
Base URL: https://0kepi1kf41.execute-api.us-east-1.amazonaws.com/dev/
Matches the /glad-alerts endpoint
Allows for vector download of GLAD data in CSV or JSON format. Called when someone wants to download an AOI or geostore from: https://github.com/gfw-api/glad-analysis-tiled
Our main obstacle here is speed; particularly large areas may time out. If we need to cross this bridge, the subdivide_polygon function in the original repo will likely help.
- Clone locally
- Create .env file to store AWS credentials in the root of this project
AWS_ACCESS_KEY_ID=<my key id>
AWS_SECRET_ACCESS_KEY=<my key>
- Spin up the docker container and ssh in
docker-compose run base
- Change to shared directory
cd /home/geolambda/
- Run handler.py to test analysis and alerts endpoints (see
if __name__ == '__main__':block)
-
install serverless
npm install -g serverless -
Test! Run
docker-compose run testto automatically spin up the docker container and run your tests (in the/testfolder) -
Package -
docker-compose run package -
Deploy -
serverless deploy -v