Reference field boundaries (paper: FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery)
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866902310220726272 |
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| author | Borges Ferreira, Lucas Souza Martins, Vitor |
| author_facet | Borges Ferreira, Lucas Souza Martins, Vitor |
| contents | <p>Reference field boundaries dataset generated in the paper<a href="https://www.sciencedirect.com/science/article/pii/S0168169925001929"> "FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery</a>".</p> <p>A hand-annotated field boundary dataset (2022) covering 8 10x10 km areas across the world is made available. The study areas are located in Argentina, Australia, Brazil, China, South Africa, Spain, USA-California, and USA-Iowa.</p> <p>This dataset contains two files:</p> <ul> <li>reference_field_boundaries.gpkg: hand-annotated dataset, with polygons defining the field boundaries.</li> <li>study_areas.gpkg: polygons defining the limits of the study areas and additional metadata about each area.</li> </ul> <p>More information on how this dataset was prepared is available in the paper <a href="https://www.sciencedirect.com/science/article/pii/S0168169925001929">"FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery"</a>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14630397 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Reference field boundaries (paper: FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery) Borges Ferreira, Lucas Souza Martins, Vitor Agriculture Remote sensing Artificial intelligence <p>Reference field boundaries dataset generated in the paper<a href="https://www.sciencedirect.com/science/article/pii/S0168169925001929"> "FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery</a>".</p> <p>A hand-annotated field boundary dataset (2022) covering 8 10x10 km areas across the world is made available. The study areas are located in Argentina, Australia, Brazil, China, South Africa, Spain, USA-California, and USA-Iowa.</p> <p>This dataset contains two files:</p> <ul> <li>reference_field_boundaries.gpkg: hand-annotated dataset, with polygons defining the field boundaries.</li> <li>study_areas.gpkg: polygons defining the limits of the study areas and additional metadata about each area.</li> </ul> <p>More information on how this dataset was prepared is available in the paper <a href="https://www.sciencedirect.com/science/article/pii/S0168169925001929">"FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery"</a>.</p> |
| title | Reference field boundaries (paper: FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery) |
| topic | Agriculture Remote sensing Artificial intelligence |
| url | https://doi.org/10.5281/zenodo.14630397 |