{
  "abstract": "Geospatial analysis is essential across the earth and environmental sciences, yet conventional geographic information systems (GIS) rely on a procedure-oriented paradigm that demands substantial operational expertise. We present GeoAgent, an open-source QGIS plugin that enables goal-driven geospatial workflows through natural language conversation powered by large language models (LLMs). GeoAgent features two operating modes: a General mode for exploratory querying and layer management, and a Processing mode for multi-step workflow execution. Built on LangGraph state machines, the system decomposes requests into dependency-ordered task queues, discovers algorithms across the entire QGIS Processing registry (~250+ native, GDAL, GRASS GIS, and SAGA tools) via lexical scoring on LLM-generated keywords, introspects parameter schemas at runtime, and executes workflows with LLM-steered error diagnosis and retry loops. Schema-validated structured outputs ensure consistent performance across interchangeable cloud (OpenAI, Google Gemini, Anthropic) and local privacy-preserving (Ollama) backends. We demonstrate GeoAgent through a reproducible flood-exposure screening case study using a bundled dataset and compare its architecture against existing LLM-GIS integrations. GeoAgent is released under the MIT license on the official QGIS Plugin Repository.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Tek Bahadur Kshetri"
    },
    {
      "name": "Rabin Ojha"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/48v27188wy4z4.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23048185",
  "fulltext_url": null,
  "id": "48v27188wy4z4",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/15304/",
  "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",
      "Agentic workflows"
    ],
    [
      "bepress",
      "Computer and Systems Architecture"
    ],
    [
      "bepress",
      "Geographic Information Sciences"
    ],
    [
      "bepress",
      "LLM Agent"
    ],
    [
      "bepress",
      "Large Language Models"
    ],
    [
      "bepress",
      "Natural-language interfaces"
    ],
    [
      "bepress",
      "Other Earth Sciences"
    ],
    [
      "bepress",
      "QGIS"
    ],
    [
      "bepress",
      "Spatial Science"
    ],
    [
      "bepress",
      "geo-processing"
    ],
    [
      "bepress",
      "open-source GIS"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://eartharxiv.org/repository/object/15304/download/26522/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/48v27188wy4z4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:39Z",
      "native_id": "15304",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/15304/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "source:eartharxiv",
      "subfield": "general"
    }
  ],
  "title": "GeoAgent: An Agent-Based QGIS Plugin for Natural-Language-Driven Geospatial Analysis with Multi-Backend Large Language Model Support",
  "title_text": "GeoAgent: An Agent-Based QGIS Plugin for Natural-Language-Driven Geospatial Analysis with Multi-Backend Large Language Model Support",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:39Z",
  "url": "https://research.draconitic.com/paper/48v27188wy4z4",
  "venue": "EarthArXiv",
  "versions": []
}