DLSR-FireCNet: A deep learning framework for burned area mapping based on decision level super-resolution
Bibliographic record
Abstract
Widespread availability of Earth observing satellites offer the much-needed information to monitor global wildfire activity. Here, we propose a novel decision level super-resolution deep learning burned area mapping (BAM) model based on the MODIS surface reflectance product, which resolves the limitations associated with both coarse and medium-to-high resolution satellites. Medium-to-high resolution satellite imagery has poor temporal resolution, which is further limited by cloud and aerosol blockage, posing a challenge for timely and accurate BAM. Medium-to-high resolution sensors offer more frequent imagery, but their spatial resolution limits their application for BAM. Our model, dubbed DLSR-FireCNet, comprises two spectral bands (Red and Near-Infrared; 250 m resolution) for deep feature extraction from bi-temporal pre- and post-fire imagery, with a target 30 m resolution BAM. DLSR-FireCNet has a cascading structure to preserve BA edges while alleviating missed detections and false alarms. Trained on 834 large wildfires from 2000 to 2007, the model's performance was rigorously evaluated in 91 out-of-sample large wildfires across the U.S. from 2008 to 2020. With an average Overall Accuracy of 0.98 and a Matthew's correlation coefficient of 0.89, DLSR-FireCNet not only outperformed state-of-the-art U-NET++, U-NET+++, Swin-Unet, and HR-Net models but also showed robust performance across various test areas. Additionally, DLSR-FireCNet markedly outperforms available global MCD64A1 and FireCCI burned area products on the test cases. The proposed model structure offers opportunities to develop accurate, medium-to-high resolution global burned area products for improved monitoring and mitigation of wildfires. • DLSR-FireCNet combines decision-level super-resolution with deep learning to map burned areas from MODIS imagery at enhanced resolution. • Transforms coarse satellite data (250m) to finer resolution (30m), enabling more detailed and frequent burned area monitoring. • Achieves superior accuracy (0.98 Overall, 0.89 Kappa) compared to existing models and global burned area products. • Advances the development of more precise global burned area monitoring, supporting improved wildfire management.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 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".