Segmentation of Any Fire Event (SAFE): A Rapid and High-Precision Approach for Burned Area Extraction Using Sentinel-2 Imagery - Shuaijun Liu; Yong Xue; Hui Chen; Yang Chen; Tianyu Zhan (Remote Sensing)
The timely and accurate monitoring of wildfires and other sudden natural disasters is crucial for safeguarding the safety of residents and their property. Satellite imagery for wildfire monitoring offers a unique opportunity to obtain near-real-time disaster information through rapid, large-scale remote sensing mapping. However, existing wildfire monitoring methods are constrained by the temporal and spatial limitations of remote sensing imagery, preventing comprehensive fulfillment of the need for high temporal and spatial resolution in wildfire monitoring and early warning. To address this gap, we propose a rapid, high-precision wildfire extraction method without the need for training—SAFE. SAFE combines the generalization capabilities of the Segmentation Anything Model (SAM) and the high temporal effectiveness of hotspot product data such as MODIS and VIIRS. SAFE employs a two-step localization strategy to incrementally identify burned areas and pixels in post-wildfire imagery, thereby reducing computational load and providing high-resolution wildfire impact areas. The high-resolution burned area data generated by SAFE can subsequently be used to train lightweight regional wildfire extraction models, establishing high-precision detection and extraction models applicable to various regions, ultimately reducing undetected areas. We validated this method in four test regions representing two typical wildfire scenarios—grassland and forest. The results showed that SAFE’s F1-score was, on average, 9.37% higher than alternative methods. Additionally, the application of SAFE in large-scale disaster scenarios demonstrated its potential capability to detect the fine spatial distribution of wildfire impacts on a global scale.