Burned Area Mapping With Radarsat Constellation Mission Data and Deep Learning
Bibliographic record
Abstract
Monitoring wildfires has become increasingly critical due to the sharp rise in wildfire incidents in recent years. Optical satellites like Sentinel-2 and Landsat are extensively utilized for mapping burned areas. However, the effectiveness of optical sensors is compromised by clouds and smoke, which obstruct the detection of burned areas. As a result, there is growing interest in satellites equipped with Synthetic Aperture Radar (SAR), which can penetrate clouds and smoke. Previous studies have investigated the potential of Sentinel-1 and RADARSAT-1/-2 C-band SAR for burned area mapping. However, to the best of our knowledge, no published research is found using RADARSAT Constellation Mission (RCM) SAR data for this purpose. The objective of this study is to investigate RCM SAR data for burned area mapping using deep learning. We propose a deep-learning-based processing pipeline specifically for RCM data. The deep learningbased pipeline utilizes the U-Net as the segmentation model. The training samples are preprocessed to generate log-ratio images based on the same beam mode. The training labels are generated from binarized log-ratio images and Sentinel2 polygons. Our results demonstrate that RCM data can effectively detect burned areas in the 2023 Canadian Wildfires, achieving an F1 Score of 0.765 and an IoU Score of 0.620 for the study area in Alberta, and an F1 Score of 0.655 and an IoU Score of 0.487 for the study area in Quebec. These results indicate the promising potential of RCM data in wildfire monitoring.
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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) | 0.000 | 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 | 0.001 |
| 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".