Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation

• URL: http://arxiv.org/abs/2506.16318v2
• Date: Mon, 23 Jun 2025 10:01:33 GMT
• Title: Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation
• Authors: Carmelo Scribano, Elena Govi, Paolo Bertellini, Simone Parisi, Giorgia Franchini, Marko Bertogna,
• Abstract summary: We present a pipeline for field delineation based on the Segment Anything Model (SAM)<n>In addition to using published datasets, we describe a method for acquiring a complementary regional dataset.<n>The new regional dataset, known as ERAS, is now publicly available.
• Score: 3.4965138159482048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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