{
  "abstract": "Forest is defined rather than observed, and definitions are written to serve different management objectives. In Norway, four authoritative datasets place forest between 35 and 43% of the mainland because each encodes a different rule. Categorical forest maps inherit this problem, since each serves only the definition it was built on. We present a documented object-based workflow that separates measurement from definition. Very high-resolution imagery, Sentinel-2, and a laser-derived canopy height model are combined with the existing national masks in a spatial database, and the result is segmented in two steps into tree crowns, tree points derived from them, and forest-stand segments carrying height, crown cover, species, and site index. A rule-based expert system then classifies the tree points. Applied to mainland Norway, the workflow produced 2.72 billion tree crowns and 57.8 million forest-stand segments covering 144,755 km2. Because structure is stored as attributed objects rather than a single class, thresholding one dataset reproduces forest areas from 15.8 to 44.7% and brackets all four national estimates. Independent interpretation of 702 sampled points gave a commission error of 0.6% for the accepted tree points. The resulting dataset is called Skogmaske.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Arnt Kristian Gjertsen"
    },
    {
      "name": "Shivesh Kishore Karan"
    },
    {
      "name": "Knut Bjørkelo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/39rvc7m0m3dcg.bib",
  "cited_by": 0,
  "doi": "10.31223/x5vj7b",
  "fulltext_url": null,
  "id": "39rvc7m0m3dcg",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/15370/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Engineering"
    ],
    [
      "bepress",
      "Forest definition"
    ],
    [
      "bepress",
      "Life Sciences"
    ],
    [
      "bepress",
      "canopy height model"
    ],
    [
      "bepress",
      "forest mapping"
    ],
    [
      "bepress",
      "individual tree detection"
    ],
    [
      "bepress",
      "object-based image analysis"
    ],
    [
      "openalex",
      "2305|Remote Sensing and LiDAR Applications"
    ]
  ],
  "openalex": "W7220404821",
  "pdf_url": "https://eartharxiv.org/repository/object/15370/download/26644/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/39rvc7m0m3dcg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:42Z",
      "native_id": "15370",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/15370/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Remote Sensing and LiDAR Applications",
      "subfield": "environmental-science"
    }
  ],
  "title": "Separating measurement from definition: an object-based workflow for national forest mapping",
  "title_text": "Separating measurement from definition: an object-based workflow for national forest mapping",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:42Z",
  "url": "https://research.draconitic.com/paper/39rvc7m0m3dcg",
  "venue": "EarthArXiv",
  "versions": []
}