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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.23462 |
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| _version_ | 1866912457255026688 |
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| author | Kulahara, Manaswi Kashyap, Gautam Siddharth Joshi, Nipun Soni, Arpita |
| author_facet | Kulahara, Manaswi Kashyap, Gautam Siddharth Joshi, Nipun Soni, Arpita |
| contents | Effective disaster management requires timely and accurate insights, yet traditional methods struggle to integrate multimodal data such as images, weather records, and textual reports. To address this, we propose DisasterNet-LLM, a specialized Large Language Model (LLM) designed for comprehensive disaster analysis. By leveraging advanced pretraining, cross-modal attention mechanisms, and adaptive transformers, DisasterNet-LLM excels in disaster classification. Experimental results demonstrate its superiority over state-of-the-art models, achieving higher accuracy of 89.5%, an F1 score of 88.0%, AUC of 0.92%, and BERTScore of 0.88% in multimodal disaster classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23462 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Kulahara, Manaswi Kashyap, Gautam Siddharth Joshi, Nipun Soni, Arpita Machine Learning Artificial Intelligence Effective disaster management requires timely and accurate insights, yet traditional methods struggle to integrate multimodal data such as images, weather records, and textual reports. To address this, we propose DisasterNet-LLM, a specialized Large Language Model (LLM) designed for comprehensive disaster analysis. By leveraging advanced pretraining, cross-modal attention mechanisms, and adaptive transformers, DisasterNet-LLM excels in disaster classification. Experimental results demonstrate its superiority over state-of-the-art models, achieving higher accuracy of 89.5%, an F1 score of 88.0%, AUC of 0.92%, and BERTScore of 0.88% in multimodal disaster classification tasks. |
| title | Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.23462 |