{
  "abstract": "Low earth orbit (LEO) satellite networks are emerging as important infrastructure for global connectivity and space-based sensing, while their rapidly varying geometry, heterogeneous measurements, and dynamic propagation environments create challenges for acquiring and exploiting spatial radio information. Spectrum cartography provides a framework for constructing and updating spatial representations of radio conditions from sparse observations. This survey reviews learning-based spectrum cartography for LEO satellite networks from an integrated inference--decision perspective. We organize the literature around three representative task categories: radio map reconstruction, localization and state estimation, and map-informed resource allocation. We further examine the progression from classical interpolation, statistical estimation, and structured optimization toward learning-based approaches that exploit environmental context, relational structure, generative modeling, and adaptive information fusion. Particular attention is given to attention mechanisms for learning query- and context-dependent interactions among irregular observations, while distinguishing learned relevance from calibrated measurement reliability. Representative formulations and illustrative examples clarify these modeling principles. Finally, we synthesize cross-cutting lessons and identify open challenges in uncertainty modeling, generalization, dynamic map updating, scalable inference, realistic evaluation, and tighter coupling between radio-map inference and network decision making.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.10359",
  "author_total": 3,
  "authors": [
    {
      "name": "Liping Tao"
    },
    {
      "name": "Xindi Tong"
    },
    {
      "name": "Chee Wei Tan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3rw847x20rcgk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.10359",
  "fulltext_url": null,
  "id": "3rw847x20rcgk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.10359",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.NI"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2202|Satellite Communication Systems"
    ]
  ],
  "openalex": "W7160909113",
  "pdf_url": "https://arxiv.org/pdf/2605.10359",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3rw847x20rcgk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:08Z",
      "native_id": "2605.10359",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.10359"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: A Survey",
  "title_text": "Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: A Survey",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:08Z",
  "url": "https://research.draconitic.com/paper/3rw847x20rcgk",
  "venue": "arXiv",
  "versions": []
}