nasaharvest/kenya-crop-mask

Annual and in-season crop mapping in Kenya # Kenya crop mapping Annual and in-season mapping of cropland in Kenya ## Introduction This repository contains code and data to generate an annual and in-season crop mask for Kenya. Two models are trained - a multi-headed pixel wise classifier to classify pixels as containing crop or not, and a multi-spectral satellite image forecaster which forecasts a 12 month timeseries given a partial input: These can be used to create annual and in season crop maps. This repository contains the code to do this for Kenya, and Busia county in Kenya: These maps are available on Google Earth Engine: * Kenya (post season) * Busia (in season) In addition, they are available on Zenodo. The model which originally accompanied the paper requires data from Plant Village and One Acre Fund. We thank those organizations for making these datasets available to us - please contact them if you are interested in accessing the data. The code used to process these datasets is available on the `ccai_2020` branch of this repository. The main branch currently uses Plant Village's open source dataset, hosted on MLHub (but copied into the `data folder` here for convenience). ## Pipeline The main entrypoints into the pipeline are the scripts. Specifically: * scripts/export.py exports data (locally, or to Google Drive - see below) * scripts/process.py processes the raw data * scripts/engineer.py combines the earth observation data with the labels to create (x, y) training data * scripts/models.py trains the models * scripts/predict.py takes a trained model and runs it on an area The split_tiff.py script is useful to break large exports from Google Earth Engine, which may be too large to fit into memory. ## Setup Anaconda running python 3.6 is used as the package manager. To get set up with an environment, install Anaconda from the link above, and (from this directory) run ```bash conda env create -f environment.yml ``` This will create an environment named `landcover-mapping` with all t …