A region-wide, multi-year set of crop field boundary labels for Africa

Description

Crop field boundaries digitized in Planet imagery collected across Africa between 2017 and 2023, developed by Farmerline, Spatial Collective, and the Agricultural Impacts Research Group at Clark University, with support from the Lacuna Fund (Estes et al, 2024; Wussah et al. (2023)). This dataset has been further supplemented by additional labels collected primarily for for 2018 over a subset of countries, which provide an example of their application in training and validating a CNN-based cropland mapping model (Khallaghi et al. 2025).

Update Frequency

Updated versions of the dataset are added as they are developed

License

Planet NICFI participant license agreement

Documentation

Information on the primary dataset can be found here. Documentation for added labels is available here.

Managed By

The Agricultural Impacts Research Group

See all datasets managed by The Agricultural Impacts Research Group.

Contact

airg@clarku.edu

How to Cite

A region-wide, multi-year set of crop field boundary labels for Africa was accessed on DATE from https://registry.opendata.aws/africa-field-boundary-labels. Primary dataset: Estes et al. (2024). A region-wide, multi-year set of crop field boundary labels for Africa. arXiv:2412.18483. Additional labels: Khallaghi et al. (2025). Generalization enhancement strategies to enable cross-year cropland mapping with convolutional neural networks trained using historical samples. Remote Sensing, 17(3), 474.

Usage Examples

Tutorials

• Instructions on data access and label-making demonstration notebook by Lyndon Estes
  
  

Publications

• A platform for crowdsourcing the creation of representative, accurate landcover maps by Estes et al. (2016)
  
• A region-wide, multi-year set of crop field boundary labels for Africa by Estes et al. (2024)
  
• A region-wide, multi-year set of crop field boundary labels for Africa by Wussah et al. (2023)
  
• Generalization enhancement strategies to enable cross-year cropland mapping with convolutional neural networks trained using historical samples by Khallaghi et al. (2025)
  
• High resolution, annual maps of field boundaries for smallholder-dominated croplands at national scales by Estes et al. (2022)
  
• Technical report on label develop and processing by Wussah et al. (2023)