# Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification

arXiv: https://arxiv.org/abs/2506.23462 · HTML: https://mlsanity.com/paper/2506.23462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2311.08732/) (2023)
- [Neural networks for geospatial data](https://mlsanity.com/paper/2304.09157/) (2023)
- [Social Media for Emergency Rescue: An Analysis of Rescue Requests on Twitter during Hurricane Harvey](https://mlsanity.com/paper/2111.07187/) (2021)
- [A Novel Disaster Image Dataset and Characteristics Analysis using Attention Model](https://mlsanity.com/paper/2107.01284/) (2021)

## Abstract (arXiv)

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.