FZSNet: Burning Area Segmentation Network for Real-time Detection in Remote Sensing Images

The dataset used in this study is designed for burning area segmentation in remote sensing images, aiming to support accurate and real-time detection of fire regions. The dataset is collected from multi-source remote sensing imagery, including high-resolution RGB images and partial multispectral data, covering diverse scenarios such as forests, grasslands, and urban fringes, which ensures strong scene diversity and complexity.The image resolution ranges from 512×512512 \times 512512×512 to 1024×10241024 \times 10241024×1024. All images are uniformly preprocessed and cropped to meet the input requirements of the proposed network. Pixel-level annotations are provided for burning regions, which are manually labeled by experts based on flame color characteristics, smoke distribution, and thermal radiation cues, ensuring high annotation accuracy and consistency. The annotation categories are defined as foreground (burning area) and background.To enhance the generalization capability of the model, the dataset incorporates various challenging factors, including illumination variations, smoke occlusion, background interference (e.g., red roofs and bare soil), and scale variations. Additionally, considering the small proportion of burning regions in many cases, a significant number of small-scale fire targets are retained to improve the model’s sensitivity to fine-grained objects.The dataset is divided into training, validation, and testing subsets with a typical ratio (e.g., 7:2:1), ensuring a balanced distribution of different scenarios. Furthermore, data augmentation techniques such as random rotation, flipping, scaling, and color jittering are applied during training to improve model robustness.