# Multimodal Natural Disaster Scene Recognition with Integrated Large Model and Mamba
## 5 Conclusions  
In summary, this study utilizes large models and the Mamba module to assess natural disaster scenarios. We propose a framework, Mamba-MDRNet, in which traditional visual models are enhanced with learned representations. By integrating text and probabilistic modality information from large models with the original image modality, we construct a multimodal model. The selective mechanism of the Mamba module dynamically adjusts the weights of each modality, selectively focusing on the most important features based on the quality, relevance, or confidence of the input modalities. Extensive experiments were conducted on our designed natural disaster dataset, and we validated the generalizability and effectiveness of the proposed algorithm across various heterogeneous visual perception architectures. In our comparisons, Mamba-MDRNet with EfficientNet achieved the best performance in classifying natural disaster scenarios. Our approach leverages large pre-trained models to enhance traditional visual models without requiring substantial computational resources to train and deploy LLMs. This method facilitates more efficient natural disaster scenario classification, even in resource-constrained environments, helping to inform appropriate rescue plans. As large \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Learning Rate** & **Batch Size** & **8** & **16** & **32** & **64** \\ \hline \(2\times 10^{-3}\) & 94.1 & 94.6 & 95.2 & 96.3 \\ \(2\times 10^{-4}\) & 97.1 & 97.2 & 97.5 & 97.3 \\ \(2\times 10^{-5}\) & 97.8 & 97.0 & 97.3 & 97.5 \\ \(2\times 10^{-6}\) & 95.7 & 93.6 & 93.6 & 94.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sensitivity of Mamba-MDRNet to learning rate and batch size. pre-trained models continue to evolve, they will capture more modality information in ambiguous scenarios, leading to more precise disaster scene classification. Conceptualization, Y.S. and L.X. ; methodology, Y.S. ; software, Y.S. ; validation, Y.S. and L.X. ; formal analysis, Y.S. ; investigation, Y.S. ; resources, L.X. ; data curation, Y.S. ; writing--original draft preparation, Y.S. ; writing--review and editing, Y.S. ; visualization, Y.S. ; supervision, Y.S. and L.X. ; project administration, Y.S. and L.X. ; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript. This work was sponsored by the Yuxiu Innovation Project of NCUT (Project No. 2024 NCU-TYXCX104). Not applicable. Data Availability Statement: Data are available at [[https://ieee-dataport.org/documents/natural-disaster-scene](https://ieee-dataport.org/documents/natural-disaster-scene)]([https://ieee-dataport.org/documents/natural-disaster-scene](https://ieee-dataport.org/documents/natural-disaster-scene)) (accessed on 3 December 2024). The authors would like to express their sincere gratitude and respect to their supervisor, [PERSON]. They are grateful for his continuous support and assistance, and his guidance has enabled them to constantly grow and progress on their academic path. They will always cherish the precious learning experienced under his guidance. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: