{
  "abstract": "Urban wildfire resilience depends on interactions among built form, vegetation condition, and extreme fire weather, yet city-scale risk models often overlook whether predictive skill transfers across neighborhoods. We developed a spatially validated GeoAI workflow for the January 2025 Palisades Fire, linking 12,081 CAL FIRE damage inspections to pre-fire Sentinel-2 vegetation indices, Landsat surface temperature, LANDFIRE fuels, terrain, and OpenStreetMap buildings and roads. Among 9,883 inspected residential structures, 5,566 were destroyed. Random cross-validation yielded ROC-AUC 0.92 for the integrated XGBoost model, but 1 km spatial block validation reduced performance to 0.75; logistic regression performed similarly and was better calibrated. Building count within 100 m was the strongest predictor, with destruction odds increasing 4.12-fold per standard deviation. Vegetation moisture and greenness showed opposing conditional associations: NDMI at 100-300 m was protective (OR 0.52), whereas NDVI at 30-100 m was positively associated with destruction after accounting for moisture (OR 1.74). Predictive information was concentrated at the 100-300 m neighborhood scale. A separate post-fire track mapped burn severity and vegetation recovery without leakage. The results support neighborhood-scale susceptibility screening, moisture-aware vegetation management, and spatial block validation as a minimum standard for single-event urban wildfire modeling.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Frontiers in Sustainability, 7, 1975724 (2026)",
  "arxiv": "2608.22293",
  "author_total": 2,
  "authors": [
    {
      "name": "Parastoo Farajpoor"
    },
    {
      "name": "Mohammadreza Narimani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ay73p0gpjnnfk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.22293",
  "fulltext_url": null,
  "id": "ay73p0gpjnnfk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.22293",
  "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.LG"
    ],
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "physics.geo-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2608.22293",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ay73p0gpjnnfk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2608.22293",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.22293"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.geo-ph",
      "subfield": "geophysics"
    }
  ],
  "title": "Explainable GeoAI for urban wildfire structure loss: vegetation and built environment in the 2025 Palisades Fire",
  "title_text": "Explainable GeoAI for urban wildfire structure loss: vegetation and built environment in the 2025 Palisades Fire",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/ay73p0gpjnnfk",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.3389/frsus.2026.1975724",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}