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.
View on arXiv@article{scribano2025_2506.16318,
title={ Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation },
author={ Carmelo Scribano and Elena Govi and Paolo Bertellini and Simone Parisi and Giorgia Franchini and Marko Bertogna },
journal={arXiv preprint arXiv:2506.16318},
year={ 2025 }
}