Burnt-Net: Wildfire burned area mapping with single post-fire Sentinel-2 data and deep learning morphological neural network. (July 2022)

• Record Type: Journal Article
• Authors: Seydi, Seyd Teymoor; Hasanlou, Mahdi; Chanussot, Jocelyn
• Is Part Of: Ecological indicators. Volume 140(2022)
• Publication Date: 2022-07
• Subjects: Deep learning -- Semantic segmentation -- Burned area -- Sentinel-2 -- Morphological operator; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.108999
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: Proposing an End-to-End deep learning framework to map burned areas using Sentinel-2 imagery. Proposing a multi-patching scenario for enhancing processing time and deep feature extraction. Combing the morphological learnable scale-space operators and multi-scale-residual block. Introducing hybrid Distribution/Region-based loss functions for network error calculating. Evaluating and comparing Burnt-Net efficiency for burned area mapping in different study areas. 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.