Abstract
Large-scale maps of field boundaries are essential for agricultural monitoring tasks. Existing deep learning approaches for satellite-based field mapping are sensitive to illumination, spatial scale, and changes in geographic location. We conduct the first systematic evaluation of segmentation and geospatial foundation models (GFMs) for global field boundary delineation using the Fields of The World (FTW) benchmark. We evaluate 18 models under unified experimental settings, showing that a U-Net semantic segmentation model outperforms instance-based and GFM alternatives on a suite of performance and deployment metrics. We propose a new segmentation approach that combines a U-Net backbone, composite loss functions, and targeted data augmentations to enhance performance and robustness under real-world conditions. Our model achieves a 76\% IoU and 47\% object-F1 on FTW, an increase of 6\% and 9\% over the previous baseline. Our approach provides a practical framework for reliable, scalable, and reproducible field boundary delineation across model design, training, and inference. We release all models and model-derived field boundary datasets for five countries.
Abstract (translated)
大规模的地块边界地图对于农业监测任务至关重要。现有的基于卫星的地块制图深度学习方法对光照、空间尺度和地理位置变化较为敏感。我们首次利用《世界农田》（FTW）基准数据集，系统评估了用于全球地块边界勾绘的分割模型与地理空间基础模型（GFMs）。在统一的实验设置下，我们对18个模型进行了评估，结果表明U-Net语义分割模型在一系列性能与部署指标上均优于基于实例的方法和GFMs。我们提出了一种新的分割方法，该方法结合U-Net主干网络、复合损失函数和定向数据增强技术，以提升模型在真实场景下的性能与鲁棒性。我们的模型在FTW数据集上达到76%的IoU和47%的目标F1分数，较此前基准分别提升了6%和9%。该方法为跨模型设计、训练与推理的可靠、可扩展且可复现的地块边界勾绘提供了实用框架。我们已发布涵盖五个国家的全部模型及基于模型生成的地块边界数据集。
URL
https://arxiv.org/abs/2603.27101
