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Multimodal Learning with Augmentation Techniques for Natural Disaster Assessment

arXiv 2511.00004 ↗First posted 4 Oct 2025cs.CY cs.AI cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 25 references that are in the map. First posted on arXiv 4 Oct 2025.

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20 of 25 references.

References · 20

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  10. 2024DiffuseMix: Label-Preserving Data Augmentation with Diffusion Modelscited by 78
  11. 2014Processing Social Media Messages in Mass Emergency: A Surveycited by 51
  12. 2020Multimodal Categorization of Crisis Events in Social Mediacited by 34
8 more
  1. 2022Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidentscited by 26
  2. 2020CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embeddingcited by 25
  3. 2020Analysis of Social Media Data using Multimodal Deep Learning for Disaster Responsecited by 23
  4. 2020Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Responsecited by 19
  5. 2020COBRA: Contrastive Bi-Modal Representation Algorithmcited by 12
  6. 2023CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media textscited by 7
  7. 2024CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classificationcited by 6
  8. 2024CrisisViT: A Robust Vision Transformer for Crisis Image Classificationcited by 5

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