  Applications of digital agricultural services often require either farmers or
their advisers to provide digital records of their field boundaries. Automatic
extraction of field boundaries from satellite imagery would reduce the reliance
on manual input of these records which is time consuming and error-prone, and
would underpin the provision of remote products and services. The lack of
current field boundary data sets seems to indicate low uptake of existing
methods,presumably because of expensive image preprocessing requirements and
local, often arbitrary, tuning. In this paper, we address the problem of field
boundary extraction from satellite images as a multitask semantic segmentation
problem. We used ResUNet-a, a deep convolutional neural network with a fully
connected UNet backbone that features dilated convolutions and conditioned
inference, to assign three labels to each pixel: 1) the probability of
belonging to a field; 2) the probability of being part of a boundary; and 3)
the distance to the closest boundary. These labels can then be combined to
obtain closed field boundaries. Using a single composite image from Sentinel-2,
the model was highly accurate in mapping field extent, field boundaries, and,
consequently, individual fields. Replacing the monthly composite with a
single-date image close to the compositing period only marginally decreased
accuracy. We then showed in a series of experiments that our model generalised
well across resolutions, sensors, space and time without recalibration.
Building consensus by averaging model predictions from at least four images
acquired across the season is the key to coping with the temporal variations of
accuracy. By minimising image preprocessing requirements and replacing local
arbitrary decisions by data-driven ones, our approach is expected to facilitate
the extraction of individual crop fields at scale.
