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

arXiv 2506.16318 ↗First posted 19 Jun 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 19 Jun 2025.

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11 of 23 references.

References · 11

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2023Segment Anythingcited by 8,234
  4. 2017Mask R-CNNcited by 7,277
  5. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
  6. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 490
  7. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 261
  8. 2023The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shotcited by 83
  9. 2019Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural networkcited by 33
  10. 2019Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time seriescited by 18
  11. 2020Farmland Parcel Delineation Using Spatio-temporal Convolutional Networkscited by 13

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