High resolution, annual cropland and landcover maps for African

countries

Background

This site provides links to view and obtain high resolution cropland and landcover maps developed by Clark University’s Agricultural Impacts Research Group for selected African countries using various machine learning approaches applied to Planet imagery.

Datasets

There are two types of data currently available:

1. cropland: Annual (beginning in year 2018) crop field boundary maps of several African countries, developed using several different modeling approaches applied to Planet imagery (Estes et al, 2022a; Estes et al, 2022b; Wussah et al, 2023). Data are provided as vectorized boundaries, in both pmtile and geoparquet formats. These datasets are under active development, and more countries and annual maps are updated as they are created.

2. land cover: A 2018 multi-class land cover map for Tanzania developed using U-Net applied to Planet imagery and Sentinel-1 time series derivatives (Song et al, 2023). See here for more detail on the methods and larger project (led by Dr. Lei Song) for which this map was created.

Accessing data

From S3

These datasets can be downloaded from this bucket by AWS account holders. Data are stored under the following prefixes:

└── mappingafrica/
    ├── croplands/
    │   ├── pmtiles
    │   └── geoparquet
    └── landcover

These can be viewed using the AWS command line interface (CLI):

aws s3 ls s3://mappingafrica/ 

PRE croplands/pmtiles/
PRE croplands/mbtiles/
PRE landcover/

To download a dataset, please use the following an example command:

aws s3 cp \
s3://mappingafrica/landcover/tanzania_2018.tif \
~/Desktop/ 

download: s3://mappingafrica/landcover/tanzania_2018.tif to ../../..
/Desktop/tanzania_2018.tif

That will download a map of predicted land cover for Tanzania for the year 2019 to your desktop (you might need to replace ~/ with the full path to your home directory).

From OSF

The land cover map and ancillary data can also be downloaded from the Open Science Foundation, and model code is here.

Viewing data

The datasets can be viewed through the web map hosted here (and accessible from here).

Maps can also be loaded and displayed using a Jupyter notebook (see the example here.

Usage

Use of these maps is governed by the terms of the Planet NICFI participant license agreement

Publications

Estes, L.D., Wussah, A.O. & Asipinu, M.D. (2022a) Final report - Phase 1: Creating open agricultural maps and ground truth data to better deliver farm extension services

Estes, L.D., Ye, S., Song, L., Luo, B., Eastman, J.R., Meng, Z., Zhang, Q., McRitchie, D., Debats, S.R., Muhando, J., Amukoa, A.H., Kaloo, B.W., Makuru, J., Mbatia, B.K., Muasa, I.M., Mucha, J., Mugami, A.M., Mugami, J.M., Muinde, F.W., Mwawaza, F.M., Ochieng, J., Oduol, C.J., Oduor, P., Wanjiku, T., Wanyoike, J.G., Avery, R.B. & Caylor, K.K. (2022b) High resolution, annual maps of field boundaries for smallholder-dominated croplands at national scales. Frontiers in Artificial Intelligence, 4, 744863.

Song, L., Estes, A.B. & Estes, L.D. (2023) A super-ensemble approach to map land cover types with high resolution over data-sparse African savanna landscapes. International Journal of Applied Earth Observation and Geoinformation, 116, 103152.

Wussah, A.O., Asipinu, M.D. & Estes, L.D. (2022) Final report - Phase 2: creating next generation field boundary and crop type maps: Rigorous multi-scale groundtruth provides sustainable extension services for smallholders