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arXiv · 2511.00004

Multimodal Learning with Augmentation Techniques for Natural Disaster Assessment

Adrian-Dinu Urse·Dumitru-Clementin Cercel·Florin Pop
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Abstract

Natural disaster assessment relies on accurate and rapid access to information, with social media emerging as a valuable real-time source. However, existing datasets suffer from class imbalance and limited samples, making effective model development a challenging task. This paper explores augmentation techniques to address these issues on the CrisisMMD multimodal dataset. For visual data, we apply diffusion-based methods, namely Real Guidance and DiffuseMix. For text data, we explore back-translation, paraphrasing with transformers, and image caption-based augmentation. We evaluated these across unimodal, multimodal, and multi-view learning setups. Results show that selected augmentations improve classification performance, particularly for underrepresented classes, while multi-view learning introduces potential but requires further refinement. This study highlights effective augmentation strategies for building more robust disaster assessment systems.

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Adrian-Dinu Urse, Dumitru-Clementin Cercel, Florin Pop. 2025-10-04. Multimodal Learning with Augmentation Techniques for Natural Disaster Assessment. https://arxiv.org/abs/2511.00004

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