SUBTERRANEAN URBANISM FOR WILDFIRE RESILIENCE: POST-DISASTER PLANNING AND GIS-BASED DESIGN IN TOPANGA–PALISADES, LOS ANGELES (2025)
Bibliographic record
Abstract
The increasing frequency and intensity of wildfires, particularly in urban-wildland interface zones, pose serious threats to cities such as Los Angeles. The January (2025) wildfire in the Topanga – Palisades Highlands region revealed systemic vulnerabilities in both urban planning and structural resilience. This research explores the use of underground architecture as a passive defense mechanism against wildfire intrusion. Through geospatial analysis, thermal mapping, and case comparison with international precedents—including projects in Wroclaw (Poland), Athens (Greece), and the UBC thermal void pilot in Canada—the study identifies key design strategies for enhancing thermal resistance, minimizing damage, and ensuring emergency survivability. A pilot reconstruction model is proposed, emphasizing clustered earth-sheltered housing, adaptive ventilation, and multi-layered zoning. The findings demonstrate that underground systems, when integrated with local topography and supported by robust regulatory frameworks, can significantly enhance wildfire resilience and long-term sustainability in vulnerable urban zones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".