Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation
This work addresses the problem of efficient agricultural operation mapping for farmers and planners, but it is incremental as it adapts an existing model to a specific domain.
The paper tackles agricultural field boundary mapping by fine-tuning the Segment Anything Model (SAM) for satellite imagery, achieving a robust baseline for automated delineation and releasing a new regional dataset called ERAS.
Accurate mapping of agricultural field boundaries is essential for the efficient operation of agriculture. Automatic extraction from high-resolution satellite imagery, supported by computer vision techniques, can avoid costly ground surveys. In this paper, we present a pipeline for field delineation based on the Segment Anything Model (SAM), introducing a fine-tuning strategy to adapt SAM to this task. In addition to using published datasets, we describe a method for acquiring a complementary regional dataset that covers areas beyond current sources. Extensive experiments assess segmentation accuracy and evaluate the generalization capabilities. Our approach provides a robust baseline for automated field delineation. The new regional dataset, known as ERAS, is now publicly available.