Using ResWnet for semantic segmentation of active wildfires from Landsat-8 imagery

Wildfires are a key aspect of many ecosystems, but climate change has created conditions more conducive for devastating wildfires. Thus, it is imperative that relevant agencies know where small fires occur expeditiously. Remote sensing is a key tool for active fire detection (AFD), and satellite imagery in particular is useful due to covering wide areas. Semantic segmentation architectures like U-Net have been used for AFD and have proven very effective. In this paper, we apply a unique variant of U-Net called ResWnet towards AFD, using a large global dataset. ResWnet achieved a precision of 95% and an F-Score of 94.2%, which is better than a U-Net trained on the same dataset.