Zero-shot burned area mapping with the Segment Anything Model (SAM): a label-free framework for post-fire...

Area of Science:

• Remote Sensing
• Geospatial Analysis
• Ecology

Background:

• Accurate burned area mapping is crucial for understanding fire impacts on ecosystems and carbon cycles.
• Traditional methods face limitations in accuracy and scalability due to spectral confusion and reliance on thresholds.
• Deep learning models show promise but require extensive labeled data and retraining for new regions.

Purpose of the Study:

• To propose and evaluate a novel zero-shot burned area mapping approach using the Segment Anything Model (SAM) with Sentinel-2 data.
• To assess the performance of SAM in achieving accurate and scalable burned area detection without task-specific training.
• To investigate the impact of different input data combinations and model configurations on mapping accuracy.

Main Methods:

• Utilized the Segment Anything Model (SAM), a foundation model, for zero-shot segmentation of burned areas.
• Generated composite input images from Sentinel-2 derived indices: Normalized Burn Ratio (NBR), NBR2, and Normalized Difference Vegetation Index (NDVI).
• Systematically investigated the effects of pre-processing, post-processing, and hyperparameter tuning (e.g., multi-scale configurations) on SAM's performance.

Main Results:

• Achieved high Intersection over Union (IoU) scores (0.89 for Bursa, 0.87 for Çanakkale) and F1 scores (0.94 for Bursa, 0.92 for Çanakkale).
• Demonstrated that SAM performance is comparable to supervised deep learning models, despite requiring no labeled training data.
• Found that multi-scale configurations and composite index inputs significantly improved geometric integrity, boundary accuracy, and discrimination between burned and unburned areas.

Conclusions:

• The proposed zero-shot SAM approach offers a fast, scalable, and cost-effective alternative for burned area detection, reducing reliance on manual labeling.
• SAM provides a flexible and generalizable framework for environmental monitoring, adaptable to various ecosystems and sensor conditions.
• This study pioneers the systematic application of SAM for burned forest area detection, highlighting its potential for data-scarce or time-critical scenarios.