  Crop field boundaries aid in mapping crop types, predicting yields, and
delivering field-scale analytics to farmers. Recent years have seen the
successful application of deep learning to delineating field boundaries in
industrial agricultural systems, but field boundary datasets remain missing in
smallholder systems due to (1) small fields that require high resolution
satellite imagery to delineate and (2) a lack of ground labels for model
training and validation. In this work, we combine transfer learning and weak
supervision to overcome these challenges, and we demonstrate the methods'
success in India where we efficiently generated 10,000 new field labels. Our
best model uses 1.5m resolution Airbus SPOT imagery as input, pre-trains a
state-of-the-art neural network on France field boundaries, and fine-tunes on
India labels to achieve a median Intersection over Union (IoU) of 0.86 in
India. If using 4.8m resolution PlanetScope imagery instead, the best model
achieves a median IoU of 0.72. Experiments also show that pre-training in
France reduces the number of India field labels needed to achieve a given
performance level by as much as $20\times$ when datasets are small. These
findings suggest our method is a scalable approach for delineating crop fields
in regions of the world that currently lack field boundary datasets. We
publicly release the 10,000 labels and delineation model to facilitate the
creation of field boundary maps and new methods by the community.
