# The spatial anatomy of urban wildfire vulnerability: a spatially validated GeoAI framework reveals the roles of building density and vegetation moisture in structure loss during the 2025 Palisades Fire

arXiv: https://arxiv.org/abs/2608.22293 · HTML: https://mlsanity.com/paper/2608.22293/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 23 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Drone-Based Multispectral Imaging and Deep Learning for Timely Detection of Branched Broomrape in Tomato Farms](https://mlsanity.com/paper/2509.09972/) (2025)
- [Sentinel-2 for Crop Yield Estimation: A Systematic Review](https://mlsanity.com/paper/2603.23779/) (2026)
- [Branched Broomrape Detection in Tomato Farms Using Satellite Imagery and Time-Series Analysis](https://mlsanity.com/paper/2509.10804/) (2025)
- [Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement](https://mlsanity.com/paper/2607.21881/) (2026)
- [Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis](https://mlsanity.com/paper/2605.21804/) (2026)
- [From crown candidates to neighborhood screening: integrating optical GeoAI and spatial modeling for urban-canopy assessment in Davis, California](https://mlsanity.com/paper/2608.13856/) (2026)
- [Leaf Spectral Reflectance Prediction Using Multi-Head Attention Neural Networks](https://mlsanity.com/paper/2606.01432/) (2026)

## Built on by (most cited first, 2 of 2)

- [Multisource Remote Sensing and Geospatial Analysis of Vineyard Wildfire Impacts and Resilience: The 2019 Kincade Fire](https://mlsanity.com/paper/2609.16168/) (2026)
- [A Sentinel-2 benchmark dataset for deep-learning active-fire segmentation across 25 California wildfires](https://mlsanity.com/paper/2609.16199/) (2026)

## Abstract (arXiv)

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.