{
  "abstract": "Agricultural parcels (APs) are fundamental spatial units for precision agriculture and effective agricultural management. However, large-scale AP mapping remains limited, particularly at national scales. To address this gap, this study proposes a national-scale AP mapping framework based on 10-m resolution Sentinel-2 imagery by enhancing the Hierarchical Boundary-Guided multi-task Network (HBGNet). Specifically, we developed HBGNet 2.0, an upgraded version of HBGNet, which introduces a novel self-adaptive weighting loss function, thereby balancing the learning of different tasks and improving AP delineation. Using HBGNet 2.0, we generated a 10-m resolution national-scale AP dataset for Ukraine in 2023. HBGNet 2.0 demonstrates the best overall performance among all compared models (ResUNet-a, BFINet, BsiNet, SEANet, and HBGNet), achieving the highest OA (92.86%), F1 (91.03%), and IoU (85.70%) metrics. Compared with three publicly available national AP datasets for Ukraine, our dataset exhibits better spatial detail and higher delineation accuracy. This high-precision AP dataset has the potential to serve as a critical resource for Ukrainian agricultural monitoring and for assessing conflict-related crop damage.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101 China",
    "University of Chinese Academy of Sciences, Beijing, 100049 China",
    "Science and Technology Research Institute, China Three Gorges Corporation, Beijing, China",
    "Beidou Research Institute, Faculty of Engineering, South China Normal University, Foshan, China",
    "State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081 China",
    "Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081 China",
    "College of Geography and Remote Sensing, Hohai University, Nanjing, 210098 China"
  ],
  "article_type": "data paper",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Junbin Li"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Hongwei Zeng"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Hang Zhao"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Miao Zhang"
    },
    {
      "affs": [
        3
      ],
      "name": "Yang Chen"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Qiangyi Yu"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Wenbin Wu"
    },
    {
      "affs": [
        6
      ],
      "name": "Yuanwei Qin"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Bingfang Wu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Shaohua Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/exzsjrejk2gem.bib",
  "cited_by": 0,
  "doi": "10.1038/s41597-026-07837-5",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13728157/",
  "id": "exzsjrejk2gem",
  "issns": [
    "2052-4463"
  ],
  "landing_url": "https://doi.org/10.1038/s41597-026-07837-5",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Data Descriptor"
    ],
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
  ],
  "openalex": "W7167908415",
  "pdf_url": "https://www.nature.com/articles/s41597-026-07837-5_reference.pdf",
  "pmcid": "PMC13728157",
  "pmid": "42431963",
  "published": "2026-07-10",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/exzsjrejk2gem.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:23:53Z",
      "native_id": "PMC13728157",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41597-026-07837-5"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "A 10-m National Agricultural Parcel Dataset of Ukraine Generated with an Enhanced Multi-Task Network and Sentinel-2 Imagery",
  "title_text": "A 10-m National Agricultural Parcel Dataset of Ukraine Generated with an Enhanced Multi-Task Network and Sentinel-2 Imagery",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:21:12Z",
  "url": "https://research.draconitic.com/paper/exzsjrejk2gem",
  "venue": "Scientific Data",
  "versions": []
}