Near real-time wildfire progression mapping with VIIRS time-series and autoregressive SwinUNETR
Bibliographic record
Abstract
Wildfire management and response requires frequent and accurate burned area mapping. How to map daily burned areas with satisfactory accuracy remains challenging due to missed detections caused by accumulating active fire points as well as the low temporal resolution of sensors onboard satellites like Sentinel-2/Landsat-8/9 and monthly burned area product generated from the Visible Infrared Imaging Radiometer Suite (VIIRS) data. ConvNet-based and Transformer-based deep-learning models are widely applied to mid-spatial-resolution satellite images. But these models perform poorly on low-spatial-resolution images. Also, cloud interference is one major issue when continuously monitoring the burned area. To improve detection accuracy and reduce cloud inference by combining temporal and spatial information, we propose an autoregressive spatial-temporal model AR-SwinUNETR to segment daily burned areas from VIIRS time-series. AR-SwinUNETR processes the image time-series as a 3D tensor but considers the temporal connections between images in the time-series by applying an autoregressive mask in Swin-Transformer Block. The model is trained with 2017-2020 wildfire events in the US and validated on 2021 US wildfire events. The quantitative results indicate AR-SwinUNETR can achieve a higher F1-Score than baseline deep learning models. The quantitative results of testset which consists of eight 2023 long-duration wildfires in Canada show a better F1 Score (0.757) and IoU Score (0.607) than baseline accumulated VIIRS Active Fire Hotspots (0.715) and IoU Score (0.557) compared with labels generated from Sentinel-2 images. In conclusion, the proposed AR-SwinUNETR with VIIRS image time-series can efficiently detect daily burned area providing better accuracy than direct burned area mapping with VIIRS active fire hotspots. Also, burned area mapping using VIIRS time-series and AR-SwinUNETR keeps a high temporal resolution (daily) compared to other burned area mapping products. The qualitative results also show improvements in detecting burned areas with cloudy images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".