Field boundary data is often required to access digital agricultural services and tools that assist with field-level assessment and monitoring. In addition, policy-makers and researchers need field boundaries to accurately assess food security and impacts on climate change. Thus, scalable and efficient automatic field boundary detection algorithms on satellite images have direct, important implications for many stakeholders. Deep learning is one approach that has been successfully applied in recent years to field boundary detection. Qualitatively however, these boundaries are often broken or malformed, necessitating a dependence on fine-tuned post-processing methods with arbitrary thresholds obtained through trial-and-error. Prior work has explored various architectures for predicting field boundaries, but little has been done beyond traditional supervised learning regimes. Thus, in this work, we propose a new approach to improving field boundary prediction by using an adversarial training framework. In particular, we investigated the effects of training a ResUNet model (a standard fully convolutional network architecture) as a generator in a traditional generative adversarial network (GAN) setup, on 30 meter resolution satellite imagery from 2017 over the state of Illinois. We then explored whether or not our methods can be transferred to label-scarce regions in Brazil. Overall, our results showed that adversarial training substantially improved boundary quality and performance, but had a lesser effect when transferred to unseen, low-data agricultural landscapes. Based on these findings, we conclude that adversarial training is a promising way to improve boundary quality during prediction time, and we suggest several ideas for future improvements that may make adversarial training more viable in transfer learning.

通常需要田间边界数据来访问有助于田间评估和监测的数字农业服务和工具。此外，政策制定者和研究人员需要实地界限来准确评估粮食安全和对气候变化的影响。因此，可扩展且高效的卫星图像自动场边界检测算法对许多利益相关者具有直接、重要的影响。深度学习是近年来成功应用于现场边界检测的一种方法。然而，从质量上讲，这些边界经常被打破或畸形，需要依赖于通过反复试验获得任意阈值的微调后处理方法。先前的工作已经探索了用于预测字段边界的各种架构，但除了传统的监督学习机制之外，几乎没有做任何事情。因此，在这项工作中，我们提出了一种通过使用对抗性训练框架来改进场边界预测的新方法。特别是，我们研究了在 2017 年伊利诺伊州上空的 30 米分辨率卫星图像上，在传统的生成对抗网络 (GAN) 设置中训练 ResUNet 模型（一种标准的全卷积网络架构）作为生成器的效果。然后，我们探讨了我们的方法是否可以转移到巴西的标签稀缺地区。总体而言，我们的结果表明，对抗性训练大大提高了边界质量和性能，但在转移到看不见的低数据农业景观时效果较小。基于这些发现，