{
  "abstract": "We introduce IAMAP, a user-friendly plugin for the geographic information system QGIS that allows users to (1) tile raster images and feed them into pre-trained deep learning model to extract image features; (2) reduce feature dimensionality; (3) perform clustering on features or their reduced representations; (4) generate feature similarity maps; (5) calibrate and validate supervised machine learning models to create maps. By enabling non-AI specialists to leverage the high-quality features provided by foundation models without requiring GPU capacity or extensive reference datasets, IAMAP contributes to the democratization of these computationally efficient and energy-conscious methods. Development of the plugin is opensource on GitHub. Documentation and tutorials are available on Read the Docs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2508.00627",
  "author_total": 5,
  "authors": [
    {
      "name": "Paul Tresson"
    },
    {
      "name": "Pierre Le Coz"
    },
    {
      "name": "Hadrien Tulet"
    },
    {
      "name": "Anthony Malkassian"
    },
    {
      "name": "Maxime Réjou-Méchain"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7wgcshehea23f.bib",
  "cited_by": 0,
  "doi": "10.21105/joss.10329",
  "fulltext_url": null,
  "id": "7wgcshehea23f",
  "issns": [
    "1942-3926"
  ],
  "landing_url": "https://hal.science/hal-05793642v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.info"
    ],
    [
      "hal",
      "0.sde"
    ],
    [
      "openalex",
      "2214|Remote-Sensing Image Classification"
    ]
  ],
  "openalex": "W4416551691",
  "pdf_url": "https://hal.science/hal-05793642/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-25",
  "publisher": "Open Journals",
  "ris": "https://research.draconitic.com/paper/7wgcshehea23f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:54Z",
      "native_id": "hal-05793642",
      "source": "hal",
      "url": "https://hal.science/hal-05793642v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:0.sde",
      "subfield": "environmental-science"
    }
  ],
  "title": "IAMAP: Unlocking Deep Learning in QGIS for non-coders and limited computing resources",
  "title_text": "IAMAP: Unlocking Deep Learning in QGIS for non-coders and limited computing resources",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:54Z",
  "url": "https://research.draconitic.com/paper/7wgcshehea23f",
  "venue": "Journal of Open Source Software",
  "versions": []
}