LinU-Mamba: Visual Mamba U-Net with Linear Attention to Predict Wildfire Spread
Bibliographic record
Abstract
Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. We propose LinU-Mamba, a model with a U-Net-based vision Mamba architecture, with light spatial attention in skip connections, and an efficient linear attention mechanism in the encoder and decoder to better capture salient fire information in the dataset. The model is trained and evaluated on the two-dimensional remote sensing dataset Next Day Wildfire Spread (NDWS), which maps fire data across the United States with fire entries, topography, vegetation, weather, drought index, and population density variables. The results demonstrate that our approach achieves superior performance compared to existing deep learning methods applied to the same dataset, while showing an efficient training time. Furthermore, we highlight the impacts of pre-training and feature selection in remote sensing, as well as the impacts of linear attention use in our model. As far as we know, LinU-Mamba is the first model based on Mamba used for wildfire spread prediction, making it a strong foundation for future 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".