Weak Labeling for Cropland Mapping in Africa
Gilles Quentin Hacheme Akram Zaytar Girmaw Abebe Tadesse Caleb Robinson Rahul Dodhia Juan M. Lavista Ferres Stephen Wood
Cropland mapping can play a vital role in addressing environmental,
agricultural, and food security challenges. However, in the context of Africa,
practical applications are often hindered by the limited availability of
high-resolution cropland maps. Such maps typically require extensive human
labeling, thereby creating a scalability bottleneck. To address this, we
propose an approach that utilizes unsupervised object clustering to refine
existing weak labels, such as those obtained from global cropland maps. The
refined labels, in conjunction with sparse human annotations, serve as training
data for a semantic segmentation network designed to identify cropland areas.
We conduct experiments to demonstrate the benefits of the improved weak labels
generated by our method. In a scenario where we train our model with only 33
human-annotated labels, the F_1 score for the cropland category increases from
0.53 to 0.84 when we add the mined negative labels.