{
  "abstract": "LiDAR point-count rasters can contain regular stripes that are not present in the surveyed scene. They arise when the fixed step on which LAS files store coordinates, often 1 cm, interacts with the grid used to count points. We identify three mechanisms, grid mismatch, floating-point boundary rounding and uneven filling of stored positions, predict where each pattern appears and how strong it is, and name a controlled change that should weaken or move it. We tested the predictions on UAV, mobile and airborne point clouds and in PDAL, lidR and LAStools. Stripe sizes matched the predictions (median regression slope 1.00), and moving a 10 cm grid by 5 mm reduced the RMS relative count change per cell of a UAV raster from 11.6 % to 0.5 %. In an airborne tile where the stripe was too weak to see in individual cells, its spectral lines stood 4.6 to 22 times above a conservative background level. Across eight cell sizes the grid-mismatch line followed its predicted ground wavelength while a reference component already present in the finer-coordinate data stayed fixed. The patterns changed texture metrics of UAV count rasters by up to 5 %, while per-cell ratios and Voronoi-based density were almost unaffected. In a sample of 87 public files from three providers, 28 showed detectable uneven filling of stored values, mostly as weak metre-period patterns; in one redistributed file the filling produced stripes of 18 %. The effects are largest for cells below a few decimetres. Because each mechanism responds to a different controlled change, a suspicious pattern can be tested, and each mechanism has its own remedy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Alejandro Urias"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ef111rrk1b6dw.bib",
  "cited_by": 0,
  "doi": "10.31223/x5tv40",
  "fulltext_url": null,
  "id": "ef111rrk1b6dw",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/15211/",
  "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",
      "Computer Sciences"
    ],
    [
      "bepress",
      "Earth Sciences"
    ],
    [
      "bepress",
      "Geophysics and Seismology"
    ],
    [
      "bepress",
      "LAS format"
    ],
    [
      "bepress",
      "LiDAR"
    ],
    [
      "bepress",
      "Numerical Analysis and Scientific Computing"
    ],
    [
      "bepress",
      "Other Earth Sciences"
    ],
    [
      "bepress",
      "Physical Sciences and Mathematics"
    ],
    [
      "bepress",
      "aliasing"
    ],
    [
      "bepress",
      "point density"
    ],
    [
      "bepress",
      "quantization"
    ],
    [
      "bepress",
      "rasterization"
    ],
    [
      "bepress",
      "spectral analysis"
    ],
    [
      "openalex",
      "2305|Remote Sensing and LiDAR Applications"
    ]
  ],
  "openalex": "W7214441819",
  "pdf_url": "https://eartharxiv.org/repository/object/15211/download/26417/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ef111rrk1b6dw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:39Z",
      "native_id": "15211",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/15211/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "bepress:Geophysics and Seismology",
      "subfield": "geophysics"
    }
  ],
  "title": "Hidden periodicity in LiDAR point-count rasters",
  "title_text": "Hidden periodicity in LiDAR point-count rasters",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:39Z",
  "url": "https://research.draconitic.com/paper/ef111rrk1b6dw",
  "venue": "EarthArXiv",
  "versions": []
}