Burnt-Net: Wildfire burned area mapping with single post-fire Sentinel-2 data and deep learning morphological neural network
Seyd Teymoor Seydi, Mahdi Hasanlou, Jocelyn Chanussot
Abstract
Accurate and timely mapping of wildfire burned areas is crucial for post-fire management, planning, and next subsequent actions. The monitoring and mapping of the burned area by traditional and common methods are time-consuming and challenging while is vital to propose an advanced burned area detection framework for achieving reliable results. To this end, this study proposed a novel End-to-End framework based on deep learning and post-fire Sentinel-2 imagery. The proposed framework known as Burnt-Net combines quadratic morphological operators and standard convolution layers. The multi-patch multi-level residual morphological (MP-MRM) blocks are the main part of the decoder part of the Burnt-Net while the encoder part uses the multi-level residual morphological and transpose convolution layers. To evaluate the efficiency of Burnt-Net the post-fire Sentinel-2 for the latest wildfires over different countries was collected and then, the model was trained and evaluated based on them. Furthermore, the most common deep learning-based model implemented for comparing the result of burned areas by the proposed Burnt-Net. The results of burned areas mapping show the Burnt-Net is robust in the detection of burned areas and provides a mean accuracy of more than 97% by overall accuracy (OA). Furthermore, the Burnt-Net is fast and can provide the burned area map in the near real-time.