Weak Labeling for Cropland Mapping in Africa

If the goal is to achieve better results in specific regions, models Cropland mapping can play a vital role in addressing environmental, that are tailored to those regions usually perform better than agricultural, and food security challenges. However, models that are designed for the whole world. in the context of Africa, practical applications are often hindered To this end, we develop a modeling workflow for generating by the limited availability of high-resolution cropland high-resolution cropland maps that are tailored toward a maps. Such maps typically require extensive human labeling, given area of interest (AOI), using Kenya as a use case. We use thereby creating a scalability bottleneck. To address this, we a deep learning based semantic segmentation workflow - an approach propose an approach that utilizes unsupervised object clustering often employed for land-cover maps [9, 10, 11, 12, 13]. to refine existing weak labels, such as those obtained In order to train the models, we used a mixture of sparse human from global cropland maps. The refined labels, in conjunction labels gathered in the AOI and weak labels from global with sparse human annotations, serve as training data for a cropland maps. Specifically we use the area of intersection semantic segmentation network designed to identify cropland between an unsupervised object based clustering of the input areas. We conduct experiments to demonstrate the benefits of satellite imagery and the weak labels to mine stronger cropland the improved weak labels generated by our method. In a scenario (positive class) and non-cropland (negative class) samples (see where we train our model with only 33 human-annotated Figure 1 for an overview of this approach).