Semantic Segmentation of Burned Areas in Sentinel-2 Satellite Images Using Deep Learning Models

Earth Observation (EO) data have become abundantly available and at low prices or for free, opening many possibilities to tackle problems such as the mapping of burned areas after wildfires have been extinguished. This task can be completed using deep learning semantic segmentation techniques. In this paper, we first present our methodology for adapting burn area polygons into semantic segmentation training data for deep neural models. Then, we evaluate four deep semantic segmentation models, U-Net, U-Net++, DeepLabV3, and DeepLabV3+ on Sentinel-2 satellite imagery data obtained from the Copernicus program over Canada in 2019. Our results show that DeepLab models outperformed U-Net models with the DeepLabV3 model achieving the best recall of 83.78%, while the DeepLabV3+ achieved the best precision of 84.35%. EO data allows us a faster and more accurate assessment of burned areas, which can accelerate the restoration planning and processing of insurance claims.