## 6 Conclusions and Future Work  
This research presents an innovative deep-learning-based approach for the detection of fire points, aiming to improve both the accuracy and robustness of detection through a thorough examination of the extensive features associated with fire points. Significantly, this method is the first to implement the Swin Transformer architecture in fire point detection tasks, utilizing its self-attention mechanism to effectively model long-range global dependencies. This feature allows for the precise capturing of global information pertinent to fire points and their surrounding environments. By integrating the Swin Transformer with a Bidirectional Long Short-Term Memory (BiLSTM) network, the proposed approach facilitates comprehensive feature extraction across dual dimensions, ultimately amalgamating these features to enhance the performance of fire point detection. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **A** & **P** & **R** & **F1** & **ED** & **MD** \\ \hline Our Proposed Method & 0.999 & 0.652 & 0.747 & 0.696 & 0.348 & 0.253 \\ BiLSTM & 0.999 & 0.612 & 0.715 & 0.659 & 0.388 & 0.285 \\ Swim Transformer & 0.999 & 0.561 & 0.638 & 0.597 & 0.439 & 0.362 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiment. Comparative analyses with conventional models such as DenseNet, SimpleCNN, and Multi-Layer Perceptron (MLP) demonstrate that the proposed method outperforms these models across various metrics, including accuracy, recall, and F1 score. Notably, the proposed method significantly surpasses CNN models, which predominantly concentrate on local features; and MLP models, which primarily rely on the core band information of fire points, in terms of both the accuracy and the comprehensiveness of fire point detection. Ablation studies further validate the effectiveness of each component, indicating that the integration of multiple features markedly enhances detection performance. Although BiLSTM shows a slight reduction in accuracy, it excels in recall, highlighting its essential role in fire point detection. Moreover, the experimental results suggest that the core band information of fire points is more critical than the global information in the context of fire point detection tasks. Nonetheless, background information still contributes to the model's overall performance, particularly in complex background scenarios. While the proposed method has demonstrated strong performance in experiments, there is still the potential to enhance its accuracy, particularly in extreme weather and challenging terrain. Additionally, the current research is confined to certain geographic areas, leading to notable variations in the model's performance across different locations, which may affect its transferability. To tackle this challenge, future studies should aim to improve the model's ability to generalize, ensuring it can achieve high accuracy across a broader range of geographical and climatic conditions. Enhancing transferability could involve increasing the model's adaptability through diverse training datasets and cross-regional validation, particularly by conducting extensive evaluations under various climatic, terrain, and other conditions. This approach will contribute to the development of more robust models that consistently perform well in different environments, thereby enhancing their reliability and effectiveness in real-world applications. In future research, alongside improving the model's generalization and adaptability, data-level optimization should also be addressed. Although the Himawari-8 satellite data utilized in this study has a high temporal resolution, its spatial resolution is relatively low, which may limit the accuracy of fire point detection. Therefore, future investigations should explore the integration of Himawari-8 data with other high-spatial-resolution remote sensing data to create a dataset that combines both high temporal and spatial resolution, further advancing fire detection technology. Conceptualization, H.P. and D.L.