agroimpacts/lacunalabels

Processing and analysis of Africa-wide field boundary labels created for the Lacuna Fund. # A region-wide, multi-year set of crop field boundary labels for Africa This repository hosts the analytical code and pointers to datasets resulting from a project to generate a continent-wide set of crop field labels for Africa covering the years 2017-2023. The data are intended for training and assessing machine learning models that can be used to map agricultural fields over large areas and multiple years. The project was funded by the Lacuna Fund, and led by Farmerline, in collaboration with Spatial Collective and the Agricultural Impacts Research Group at Clark University. Please refer to the technical report for more details on the methods used to develop the dataset, an analysis of label quality, and usage guidelines. The report and additional documents, analyses, and demonstration code used to develop labels by cloning the repository: ``` bash git clone git@github.com:agroimpacts/lacunalabels.git cd lacunalabels pip install -e . ``` Please see the next sections for details on accessing the imagery and label data. ## Access and usage The imagery and labels can be obtained either from Zenodo or the Registry of Open Data on AWS, and may used in accordance with Planet’s participant license agreement for the NICFI contract. The code used to annotate and analyze the data is available under an Apache 2.0 license. ### Data on AWS The data are in the bucket `s3://africa-field-boundary-labels` in the `us-west-2` region, and are organized as follows: Prefix/key Description imagery/ Contains 4 band Planet image chips in geotiff format, named as follows: XX1234567890_YYYY-MM.tif Representing a grid identifier and the image acquisition date and month labels/ A set of 3-class labels in geotiff format, named as: XX1234567890_123456_YYYY-MM.tif Representing a grid identifier, a labelling assignment identifier, and month and year of imagery being labelled. These labels represent one possible set from a larger number of labelling assignments, which wer …