A Crowdsensing-based Framework to Enhance Wildland-Urban Interface Fire Risk Assessment
Bibliographic record
Abstract
As climate change continues to intensify, various regions across Alberta are increasingly vulnerable to wildfires that pose significant threats to infrastructure near forested areas, commonly referred to as Wildland-Urban Interface (WUI) fires. According to statistics from the Canadian National Forestry Database, more than 8,000 wildfire incidents are recorded annually, affecting over 2.1 million hectares on average. This growing risk highlights the urgent need for an enhanced analytical approach capable of estimating fire dynamics—specifically, parameters such as the rate of spread (ROS) and head fire intensity (HFI)—at a localized scale. The research introduces a novel framework designed to improve fire behavior assessment by leveraging ground-based crowdsourced video data. This real-time visual input aids in identifying micro-scale fuel conditions critical for accurate predictions. In areas where such ground data is sparse, satellite imagery is analyzed to supplement fuel information. Advanced computer vision techniques, including AI-driven object detection and image segmentation, are utilized to classify and extract relevant fuel characteristics from both video and satellite sources. A case study focusing on the Kinsmen Sport Centre in Edmonton was conducted, using crowdsensed footage captured during summer and fall to estimate ROS and HFI values. The findings illustrate how integrating crowdsensing with AI tools offers a promising path toward obtaining precise, current fuel data, supporting proactive strategies for managing wildfire hazards in WUI 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".