Automated classification of fuel types using roadside images via deep learning
Bibliographic record
Abstract
There is an urgent need to develop new data-driven methods for assessing wildfire-related risks in large areas susceptible to such risks. To assess these risks, one of the critical parameters to analyse is fuel. This research note presents a framework for classifying fuels through the analysis of roadside images to complement the current practice of assessing fuels through aerial images and visual inspections. Some of the most prevalent fuel types in North America were considered for automated classification, including grasses, shrubs and timbers. A framework was developed using convolutional neural networks (CNNs), which can automate the process of fuel classification. Various pre-trained neural networks were examined and the best network in terms of time efficiency and accuracy was identified, and had ~94% accuracy in identifying the chosen fuel types. This framework was initially applied to street view images collected from Google Earth. Indeed, the results showed that the framework has the potential for application for fuel classification using roadside images, and this makes it suitable for crowdsensing-based fuel mapping for wildfire risk assessment, which is the future goal of this research.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".