Enhanced fine-scale satellite-based wildfire exposure assessment in wildland–urban interface communities using deep learning
Bibliographic record
Abstract
Background Wildfires occurring within urban areas pose significant risks to structures and infrastructure. The wildland–urban interface (WUI) regions in Canada face heightened wildfire challenges due to climate change, emphasising the need for accurate fire exposure assessments. Aims This study aims to enhance the precision and scalability of WUI fire exposure assessment by automating fine-scale vegetation classification and integrating slope analysis using publicly available satellite imagery, aligned with Canadian national guidelines. Methods A computer vision framework is introduced, leveraging satellite imagery and convolutional neural networks to characterise vegetative fuels. Topographical factors, such as slope, are integrated to generate topo-fuel maps, enabling the extraction of exposure levels within each zone, and finally the assessment of fire exposure levels for individual buildings. Key results The framework is applied to the City of Edmonton as a case study, demonstrating its capability to assess WUI fire exposure levels at an urban scale. Conclusions The proposed method provides a scalable and automated solution for assessing fire exposure levels in WUI regions. Implications This framework supports fire management authorities in enhancing preparedness and mitigation efforts for WUI fire risks, contributing to safer urban planning and infrastructure resilience in wildfire-prone regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".