Back in 2020 we completed our Version 1 map of Ghana’s croplands for the year 2018 (see figure below), which is a proof of concept for the ability to create high resolution, annually updateable map of crop field boundaries over smallholder-dominated cropland sat a national scale. The methods include a novel procedure for converting daily, high resolution PlanetScope imagery into cloud-free seasonal composites, and a rigorous approach to training and validating a machine learning model, which are described in our working paper (which is nearly through the peer review process) along with the results. The field boundary maps can be viewed on our web map, which is undergoing a redesign that will be completed in a few weeks, as we transition data to a STAC-compliant server(developed by Azavea). The map data can be downloaded here (please read the paper before using to understand the accuracy of the dataset).
Since completing those Version 1 results, we have been working to improve the accuracy and precision of the field boundaries, so that we can release a more accurate Version 2 map. We have replaced the Random Forests model we used to identify cropland in the Version 1 map with a more accurate convolutional neural network, U-Net. We train this more powerful model not just to classify pixels into cropland and non-cropland classes, but also to distinguish between the edges and interiors of fields. This approach allows us to develop maps that delineate individual fields much more precisely, for about a tenth of the computational cost. The figure below shows the score maps(probabilities) for the field interior class resulting from a single model trained using 4,100 training labels collected across the entire country. Compared to the Version 1 maps, you can see that the individual fields are more clearly separated, and that the predictions are more confident.
This enables individual field boundaries to be more precisely mapped, as seen in the next figure, which compares Version 1 (middle row) and Version 2 (bottom row) field boundaries.
We are now refining the U-Net model, to improve its accuracy. Once that is completed (in the coming weeks), we will release our Version 2 field boundaries, which should be used in place of Version 1.
For more details about the evolution of our mapping approach, you can view our presentation at the Planet Explore 2021 conference this week, in the session on Global Agriculture: From Smallholder to Commercial Scale.