FieldMapper: Automated Agricultural Field Delineation via Vision Foundation Model and Machinery Trajectories

The modernization of agriculture is a cornerstone of national economic growth and sustainable development. Accurate delineation of agricultural field boundaries plays a vital role in field protection, agricultural yield forecasting, and land use regulation. However, existing approaches often rely on large-scale human annotations and exhibit poor generalization ability. With the increasing availability of data from autonomous agricultural operations, particularly the movement patterns of field machinery, a new paradigm for field boundary extraction emerges. In this paper, we introduce FieldMapper, a novel multi-modal framework designed to automatically delineate agricultural fields by synergistically leveraging remote sensing imagery and machinery trajectory data. The framework first employs a powerful vision foundation model to generate fine-grained field boundary proposals from satellite images. These proposals are then weakly annotated by aligning them with coverage patterns derived from machinery trajectories, eliminating the need for dense manual labeling. Finally, a discriminative model is trained through weakly supervised learning to distinguish true field instances from background candidates. Extensive experiments demonstrate FieldMapper outperforms the best baseline by 23.0% in terms of IoU, highlighting its robustness and effectiveness in real-world field delineation scenarios.