Wildfire-S1S2-Canada: A Large-Scale Sentinel-1/2 Wildfire Burned Area Mapping Dataset Based on the 2017-2019 Wildfires in Canada

Puzhao Zhang, Xikun Hu, Yifang Ban

Abstract: Wildfires vary across space and time, precisely and timely mapping on the wildfire affected areas is critical for wildfire management, population and property protection, and environmental impact assessment. In this study, we established a large-scale annotated wildfire burned area dataset based on freely available Sentinel-1 SAR and Sentinel-2 multispectral instrument (MSI) data and Canada Wildfire Burned Area Database. This dataset includes bi-temporal Sentinel-1 and Sentinel-2 images, which allows users to exploit remotely sensed data acquired in both optical and microwave domains. On the proposed dataset, we achieved the highest IoU score of 0.86 on the Sentinel-2 data with Siamese U-Net, and the highest IoU score of 0.80 on the Sentinel-1 data using U-Net with early fusion. The combined use of Sentinel-1 and Sentinel-2 failed to bring significant improvement compared to Sentinel-2 based results, but this dataset may have the potential to boost Sentinel-1 based results with Sentinel-2 data for near real-time wildfire progression mapping.