Abstract
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.
Abstract (translated)
野外边界勾勒的目标是预测覆盖遥感图像（如卫星或无人机）中单个农田的多边形边界和内部。自动划分田野边界对于许多农业现实应用场景（如估算地区耕地面积或预测田地收获量）是必要的。将野外边界勾勒视为实例分割问题，但与用于实例分割的传统计算机视觉数据集相比，它呈现出了独特的研究挑战。以前工作的实用性也受到假设足够大的有标签数据集存在的限制，该数据集将用于应用田野边界分割模型，这在大多数地区并不现实（尤其是在资源相对匮乏的地区，如撒哈拉以南非洲地区）。我们提出了一个在缺乏有标签数据集的地区分割卫星图像中作物农田边界的分割方法，利用多区域迁移学习来适应目标区域。我们证明了我们的方法超越了现有方法，多区域迁移学习在多个模型架构上显著提高了性能。我们的实现和数据集都是公开的，以便用户使用，并作为未来工作的基准。
URL
https://arxiv.org/abs/2404.00179
