The goal of field boundary delineation is to predict the polygonal boundaries
and interiors of individual crop fields in overhead remotely sensed images
(e.g., from satellites or drones). Automatic delineation of field boundaries is
a necessary task for many real-world use cases in agriculture, such as
estimating cultivated area in a region or predicting end-of-season yield in a
field. Field boundary delineation can be framed as an instance segmentation
problem, but presents unique research challenges compared to traditional
computer vision datasets used for instance segmentation. The practical
applicability of previous work is also limited by the assumption that a
sufficiently-large labeled dataset is available where field boundary
delineation models will be applied, which is not the reality for most regions
(especially under-resourced regions such as Sub-Saharan Africa). We present an
approach for segmentation of crop field boundaries in satellite images in
regions lacking labeled data that uses multi-region transfer learning to adapt
model weights for the target region. We show that our approach outperforms
existing methods and that multi-region transfer learning substantially boosts
performance for multiple model architectures. Our implementation and datasets
are publicly available to enable use of the approach by end-users and serve as
a benchmark for future work.

通过多区域迁移学习适应目标区域的模型权重，提出了一种用于卫星图像中农田边界分割的方法，在缺乏标记数据的地区，我们的方法优于现有方法，并且多区域迁移学习显著提升了多个模型架构的性能。