Abstract
Forest fires pose a significant threat to forest ecosystems, with severe impacts on both the environment and human society. Understanding the post-fire recovery processes of forests is crucial for developing strategies for species diversity conservation and ecological restoration and preventing further damage. The present study proposes applying the EAswin-Mask2former model based on semantic segmentation in deep learning using visible light band data to better monitor the evolution of burn areas in forests after fires. This model is an improvement of the classical semantic segmentation model Mask2former and can better adapt to the complex environment of burned forest areas. This model employs Swin-Transformer as the backbone for feature extraction, which is particularly advantageous for processing high-resolution images. It also includes the Contextual Transformer (CoT) Block to better capture contextual information capture and incorporates the Efficient Multi-Scale Attention (EMA) Block into the Efficiently Adaptive (EA) Block to enhance the model’s ability to learn key features and long-range dependencies. The experimental results demonstrate that the EAswin-Mask2former model can achieve a mean Intersection-over-Union (mIoU) of 76.35% in segmenting complex forest burn areas across different seasons, representing improvements of 3.26 and 0.58 percentage points, respectively, over the Mask2former models using ResNet and Swin-Transformer backbones, respectively. Moreover, this method surpasses the performance of the DeepLabV3+ and Segformer models by 4.04 and 1.75 percentage points, respectively. Ultimately, the proposed model offers excellent segmentation performance for both forest and burn areas and can effectively track the evolution of burned forests when combined with unmanned aerial vehicle (UAV) remote sensing images.
Published Version
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